An adversary can easily compromise sensor nodes in wireless sensor networks, and generate multiple
attacks through compromised nodes, such as false vote injection attacks and false report injection attacks.
The false vote injection attack tries to drop legitimate reports in an intermediate node, and the false report
injection attack tries to drain the energy consumption of each node. To prevent these attacks, a
probabilistic voting-based filtering scheme (PVFS) has been proposed to select verification nodes, and to
detect fabricated votes in the reports as they occur simultaneously. In this paper, we propose a method that
improves the energy efficiency of each node and the security level of the false report injection attack, while
maintaining the detection power of the false vote injection attack. Our proposed method effectively selects
verification node with considering the conditions of each node, based on a fuzzy rule-based system. The
verification node is decided through the energy remaining level, distance level, and number of detected
false votes in the fuzzy system. We evaluated the effectiveness of the proposed method, as compared to
PVFS, when two attacks occur simultaneously in the sensor network. The experimental results show that
our method saves energy by up to 8%, by improving and maintaining the defence against these multiple
attacks
A security method for multiple attacks in sensor networks against the false r...ijitjournal
In a large scale wireless sensor network, various attacks rapidly spread damages in the network from
inside and outside attacks such as the false report injection attack and the sinkhole attack, respectively.
These attacks drain finite energy resources and devastate constructed routing paths via compromised
nodes. The security methods like SEF (statistical en-route filtering scheme) and LEAP (localized encryption
and authentication protocol) try to cope with these attacks. When these attacks occur at the same time, SEF
and LEAP should be operated simultaneously in the sensor network thus, it introduces some inefficiency. In
this paper, we propose a security method which improves the energy efficiency while maintaining the
security level compared to the simultaneous application of SEF and LEAP. The proposed method is
designed by identifying and eliminating the redundancies within the simultaneous application of the two
methods and providing more efficient functionalities. In the proposed method, two types of new keys are
designed and provided for simultaneous detection of the attacks. Four types of keys are used in each sensor
node – a P1 for encrypting information, a PK (pairwise key) for keeping secure paths, a P2 for verifying a
specific cluster, and a GK (group key) for encrypting message. Among these keys, P1 and P2 are newly
provided keys. We have evaluated the effectiveness of the proposed method compared to the simultaneous
application of SEF and LEAP when the multiple attacks occur. The experiment results show that our
proposed method saves energy up to 10% while maintaining the detection power
AN ENERGY EFFICIENT COUNTERMEASURE AGAINST MULTIPLE ATTACKS OF THE FALSE DATA...ijcsity
Nodes are easily exposed from generated attacks on various layers because they compose simple functions in sensor networks. The false data injection attack drains finite energy resource in a compromised node,and the false HELLO flood attack threatens constructed routing paths in an adversary node. A localized encryption and authentication protocol (LEAP) was developed to prevent the aforementioned attacks through the use of four keys. However, when these attacks occur simultaneously, LEAP may not prevent damage from spreading rapidly throughout the network. In this paper, we propose a method that addresses these attacks through the use of four types of keys, including two new keys. We also improve energy consumption while maintaining a suitable security level. The effectiveness of the proposed method was evaluated relative to that of LEAP when multiple attacks occur. The experimental results reveal that the proposed method enhances energy saving by up to 12% while maintaining sufficient detection power
AN IMPROVED WATCHDOG TECHNIQUE BASED ON POWER-AWARE HIERARCHICAL DESIGN FOR I...IJNSA Journal
Preserving security and confidentiality in wireless sensor networks (WSN) are crucial. Wireless sensor networks in comparison with wired networks are more substantially vulnerable to attacks and intrusions. In WSN, a third person can eavesdrop to the information or link to the network. So, preventing these intrusions by detecting them has become one of the most demanding challenges. This paper, proposes an
improved watchdog technique as an effective technique for detecting malicious nodes based on a power aware hierarchical model. This technique overcomes the common problems in the original Watchdog mechanism. The main purpose to present this model is reducing the power consumption as a key factor
for increasing the network's lifetime. For this reason, we simulated our model with Tiny-OS simulator and then, compared our results with non hierarchical model to ensure the improvement. The results indicate that, our proposed model is better in performance than the original models and it has increased the lifetime of the wireless sensor nodes by around 2611.492 seconds for a network with 100 sensors.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A review paper on watchdog mechanism in wireless sensor network to eliminate ...eSAT Journals
Abstract Wireless Sensor network (WSN) are broadly used today in various fields such as environmental control, surveillance task, object tracking, military applications etc. As WSN is an ad-hoc network which is deployed in such an environment which is physically insecure, intrusion detection has been one of the major area of research in WSN. Inorder to achieve an appropriate level of security in WSNs we cannot depend on cryptographic techniques as these techniques fall prey to insider attacks. This paper discusses on watchdog mechanism, one of the intrusion detection techniques in Wireless Sensor Network. Watchdog is a monitoring technique which detects the misbehaving nodes in the network. The main area of focus in this paper is being made to the problems in existing watchdog technique for malicious node detection. Index Terms: Wireless Sensor Network, Security Intrusion Detection, Watchdog.
The impact of noise on detecting the arrival angle using the root-WSF algorithmTELKOMNIKA JOURNAL
This article discusses three standards of Wi-Fi: traditional, current and next-generation Wi-Fi. These standards have been tested for their ability to detect the arrival angle of a noisy system. In this study, we chose to work with an intelligent system whose noise becomes more and more important to detect the desired angle of arrival. However, the use of the weighted subspace fitting (WSF) algorithm was able to detect all angles even for the 5th generation Wi-Fi without any problem, and therefore proved its robustness against noise.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
A security method for multiple attacks in sensor networks against the false r...ijitjournal
In a large scale wireless sensor network, various attacks rapidly spread damages in the network from
inside and outside attacks such as the false report injection attack and the sinkhole attack, respectively.
These attacks drain finite energy resources and devastate constructed routing paths via compromised
nodes. The security methods like SEF (statistical en-route filtering scheme) and LEAP (localized encryption
and authentication protocol) try to cope with these attacks. When these attacks occur at the same time, SEF
and LEAP should be operated simultaneously in the sensor network thus, it introduces some inefficiency. In
this paper, we propose a security method which improves the energy efficiency while maintaining the
security level compared to the simultaneous application of SEF and LEAP. The proposed method is
designed by identifying and eliminating the redundancies within the simultaneous application of the two
methods and providing more efficient functionalities. In the proposed method, two types of new keys are
designed and provided for simultaneous detection of the attacks. Four types of keys are used in each sensor
node – a P1 for encrypting information, a PK (pairwise key) for keeping secure paths, a P2 for verifying a
specific cluster, and a GK (group key) for encrypting message. Among these keys, P1 and P2 are newly
provided keys. We have evaluated the effectiveness of the proposed method compared to the simultaneous
application of SEF and LEAP when the multiple attacks occur. The experiment results show that our
proposed method saves energy up to 10% while maintaining the detection power
AN ENERGY EFFICIENT COUNTERMEASURE AGAINST MULTIPLE ATTACKS OF THE FALSE DATA...ijcsity
Nodes are easily exposed from generated attacks on various layers because they compose simple functions in sensor networks. The false data injection attack drains finite energy resource in a compromised node,and the false HELLO flood attack threatens constructed routing paths in an adversary node. A localized encryption and authentication protocol (LEAP) was developed to prevent the aforementioned attacks through the use of four keys. However, when these attacks occur simultaneously, LEAP may not prevent damage from spreading rapidly throughout the network. In this paper, we propose a method that addresses these attacks through the use of four types of keys, including two new keys. We also improve energy consumption while maintaining a suitable security level. The effectiveness of the proposed method was evaluated relative to that of LEAP when multiple attacks occur. The experimental results reveal that the proposed method enhances energy saving by up to 12% while maintaining sufficient detection power
AN IMPROVED WATCHDOG TECHNIQUE BASED ON POWER-AWARE HIERARCHICAL DESIGN FOR I...IJNSA Journal
Preserving security and confidentiality in wireless sensor networks (WSN) are crucial. Wireless sensor networks in comparison with wired networks are more substantially vulnerable to attacks and intrusions. In WSN, a third person can eavesdrop to the information or link to the network. So, preventing these intrusions by detecting them has become one of the most demanding challenges. This paper, proposes an
improved watchdog technique as an effective technique for detecting malicious nodes based on a power aware hierarchical model. This technique overcomes the common problems in the original Watchdog mechanism. The main purpose to present this model is reducing the power consumption as a key factor
for increasing the network's lifetime. For this reason, we simulated our model with Tiny-OS simulator and then, compared our results with non hierarchical model to ensure the improvement. The results indicate that, our proposed model is better in performance than the original models and it has increased the lifetime of the wireless sensor nodes by around 2611.492 seconds for a network with 100 sensors.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A review paper on watchdog mechanism in wireless sensor network to eliminate ...eSAT Journals
Abstract Wireless Sensor network (WSN) are broadly used today in various fields such as environmental control, surveillance task, object tracking, military applications etc. As WSN is an ad-hoc network which is deployed in such an environment which is physically insecure, intrusion detection has been one of the major area of research in WSN. Inorder to achieve an appropriate level of security in WSNs we cannot depend on cryptographic techniques as these techniques fall prey to insider attacks. This paper discusses on watchdog mechanism, one of the intrusion detection techniques in Wireless Sensor Network. Watchdog is a monitoring technique which detects the misbehaving nodes in the network. The main area of focus in this paper is being made to the problems in existing watchdog technique for malicious node detection. Index Terms: Wireless Sensor Network, Security Intrusion Detection, Watchdog.
The impact of noise on detecting the arrival angle using the root-WSF algorithmTELKOMNIKA JOURNAL
This article discusses three standards of Wi-Fi: traditional, current and next-generation Wi-Fi. These standards have been tested for their ability to detect the arrival angle of a noisy system. In this study, we chose to work with an intelligent system whose noise becomes more and more important to detect the desired angle of arrival. However, the use of the weighted subspace fitting (WSF) algorithm was able to detect all angles even for the 5th generation Wi-Fi without any problem, and therefore proved its robustness against noise.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Signal detection in cognitive radio network (CRN) is influenced by several factors. One of them is
malicious user that emulate primary user (PU) signal. Emulation of PU signal causes detection error. This
paper investigates the impact of malicious user attack to PU signal detection. A number of malicious users
are randomly deployed around secondary user (SU) at a certain distance. They attempt to attack primary
signal detection that is transmitted from 100 km to SU receiver. Then, the received signal power at
secondary receiver and the performance of probability of false alarm and probability of miss detection
under two hypothesis of Neyman Pearson criterion are studied. The derived results show that a number of
malicious users has a significant impact to the performance of received power at SU and detection error
rate.
SPECTRUM SENSING STRATEGY TO ENHANCE THE QOS IN WHITE-FI NETWORKSIJCNC Journal
The rapidly growing number of wireless devices running applications that require high bandwidths, has
resulted in increasing demands for the unlicensed frequency spectrum. Given the scarcity of allocated
unlicensed frequencies, meeting such demands can become a serious concern. Cognitive Radio (CR)
technology opens the door for the opportunistic use of the licensed spectrum to partially address the issues
relevant to the limited availability of unlicensed frequencies. Combining CR and Wi-Fi to form the socalled
White-Fi networks, has been proposed for achieving higher spectrum utilization. This article
discusses the spectrum sensing in White-Fi networks and the impacts that it has on the QoS of typical
applications. It also reports the analysis of such impacts through various simulation studies. Our results
demonstrate the advantages of an adaptive sensing strategy that is capable of changing the related
parameters based on QoS requirements. We also propose such a sensing strategy that can adapt to the
IEEE 802.11e requirements. The goal of the proposed strategy is the enhancement of the overall QoS of the
applications while maintaining efficient sensing of the spectrum. Simulation results of the scenarios that
implement the proposed mechanisms demonstrate noticeable QoS improvements compared to cases where
common sensing methods are utilized in IEEE802.11 networks.
Threats have become a big problem since the past few years since computer viruses are widely recognized as a significant computer threat. However, the role of Information Technology security must be revisit again since it is too often, IT security managers find themselves in the hopeless situation of trying to uphold a maximum of security as requested from management. While at the same time they are considered an obstacle in the way of developing and introducing new applications into business and government network environments. This paper will focus on Transmission Control Protocol Synchronize Flooding attack detections using the Internet Protocol header as a platform to detect threats, especially in the IP protocol and TCP protocol, and check packets using anomaly detection system which has many advantages, and applied it under the open source Linux. The problem is to detect TCP SYN Flood attack through internet security. This paper also focusing on detecting threats in the local network by monitoring all the packets that goes through the networks. The results show that the proposed detection method can detect TCP SYN Flooding in both normal and attacked network and alert the user about the attack after sending the report to the administrator. As conclusion, TCP SYN Flood and other attacks can be detected through this traffic monitoring tools if the abnormal behaviors of the packets are recognized such as incomplete TCP three-way handshake application and IP header length.
misrouting attack in wireless sensor networks under replication attack. agent based security schemes in Security schemes for wireless sensor networks. International journal paper on wireless sensor networks.
A security method for multiple attacks in sensor networks against false repor...ieijjournal
In a large-scale wireless sensor network, damage spreads rapidly in the network when under false report injection, false votes injection, or wormhole attacks. These attacks cause finite energy resources to be drained, legitimate reports to be dropped, and data to be intercepted by adversary nodes. A probabilistic voting-based filtering scheme (PVFS) and localized encryption and authentication protocol (LEAP) can be used to cope with these attacks. When multiple attacks occur simultaneously, PVFS and LEAP should be operated together. But the concurrent application of PVFS and LEAP provides inefficient duplications of operations in the sensor network. In this paper, we propose a security method which improves the energy efficiency while maintaining the security level of applying PVFS and LEAP simultaneously. The proposed method was designed by identifying and eliminating the redundancies of employing both methods together and providing more efficient functionalities. Four types of new keys were also designed for simultaneous detection of multiple attacks. We evaluated the effectiveness of the proposed method compared to simply applying PVFS and LEAP simultaneously when under multiple attacks. The experimental results demonstrate that our proposed method saves energy by up to 11% while maintaining detection power.
SELECTING VOTES FOR ENERGY EFFICIENCY IN PROBABILISTIC VOTING-BASED FILTERING...ijasa
Wireless sensor networks are easily compromised by an adversary, such as fabricated with false votes attacks and false votes on real reports attacks. These attacks generate false data to drain the energy resource of sensors and interrupt the inflow of a real report. PVFS was proposed to detect them by verifying votes in the real report. When a real event occurs, a cluster head collects all of the votes from its neighboring nodes and selects the votes up to a defined number of votes. In this paper, our proposed method decides the number of votes based on a fuzzy rule-based system to improve energy savings as compared to PVFS. We evaluated the effectiveness of the proposal as two attacks occur simultaneously in the sensor network. The experimental results show that our method saves energy resources and maintains the security level against these multiple attacks
Fuzzy-Based Multiple Path Selection Method for Improving Energy Efficiency in...aciijournal
In wireless sensor networks, adversaries can easily compromise sensors because the sensor resources are
limited. The compromised nodes can inject false data into the network injecting false data attacks. The
injecting false data attack has the goal of consuming unnecessary energy in en-route nodes and causing
false alarms in a sink. A bandwidth-efficient cooperative authentication scheme detects this attack based on
the random graph characteristics of sensor node deployment and a cooperative bit-compressed
authentication technique. Although this scheme maintains a high filtering probability and high reliability in
the sensor network, it wastes energy in en-route nodes due to a multireport solution. In this paper, our
proposed method effectively selects a number of multireports based on the fuzzy rule-based system. We
evaluated the performance in terms of the security level and energy savings in the presence of the injecting
false data attacks. The experimental results indicate that the proposed method improves the energy
efficiency up to 10% while maintaining the same security level as compared to the existing scheme.
Wireless sensor networks (WSNs) are regularly deployed in harsh and unattended environments, and
sensor nodes are easily exposed to attacks due to the random arrangement of the sensor field. An attacker
can inject fabricated reports from a compromised node with false votes and false vote-based reports. The
false report attacks can waste the energy of the intermediate nodes, shortening the network lifetime.
Furthermore, false votes cause the filtering out of legitimate reports. A probabilistic voting-based filtering
scheme (PVFS) was proposed as a countermeasure against this type of attacks by Li and Wu. PVFS uses a
vote threshold, a security threshold, and a verification node. The scheme does not make additional use
energy or communications resources because the verification node and threshold values are fixed. There
needs to be a verification node selection method that considers the energy resources of the node. In this
paper, we propose a verification path election scheme based on a fuzzy logic system. In the proposed
scheme, one node transmits reports in the node with a strong state through a fuzzy logic system after which
a neighbor is selected out of two from the surroundings. Experimental results show that the proposed
scheme improves energy savings up to maximum 13% relative to the PVFS.
A secure routing process to simultaneously defend against false report and wo...ieijjournal
Most research related to secure routing in sensor networks has focused on how to detect and defend against a single attack. However, it is not feasible to predict which attack will occur in sensor networks. It is possible for multiple attacks to occur simultaneously, degrading the performance of the existing security schemes. For example, an attacker may try simultaneous false report and wormhole attacks to effectively damage a sensor network. Hence, a multiple simultaneous attack environment is much more complex than a single attack environment. Thus, a new security scheme that can detect multiple simultaneous attacks with a high probability and low energy consumption is needed. In this paper, we propose a secure routing scheme to defend against wormhole and false report attacks in sensor networks. The proposed method achieves a higher attack detection ratio and consumes less energy in a multi-attack scenario compared to existing schemes. It can also be extended to other types of attacks and security schemes to detect and defend against possible combinations of multiple attacks.
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilistically selects a validation node, determines the validity of the report, and filters the report based on the thresholds that have been set. In this paper, the proposed scheme improves the lifetime, detection rate, and report delivery rate of the entire network by increasing the lifetime of the cluster head (CH) by selecting the numbers of message authentication codes (MACs) and verification nodes of the report. Using this system, the event detection rate and the network lifetime are improved by up to 18% and 6%, respectively, relative to the existing PVFS.
WSN LIFETIME EXTENSION USING GA OPTIMISED FUZZY LOGICijcsit
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilistically selects a validation node, determines the validity of the report, and filters the report based on the thresholds that have been set. In this paper, the proposed scheme improves the lifetime, detection rate, and report delivery rate of the entire network by increasing the lifetime of the cluster head (CH) by selecting the numbers of message authentication codes (MACs) and verification nodes of the report. Using this system, the event detection rate and the network lifetime are improved by up to 18% and 6%, respectively, relative to the existing PVFS.
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilistically selects a validation node, determines the validity of the report, and filters the report based on the thresholds that have been set. In this paper, the proposed scheme improves the lifetime, detection rate, and report delivery rate of the entire network by increasing the lifetime of the cluster head (CH) by selecting the numbers of message authentication codes (MACs) and verification nodes of the report. Using this system, the event detection rate and the network lifetime are improved by up to 18% and 6%, respectively, relative to the existing PVFS.
Ensp energy efficient next hop selection in a probabilistic voting based filt...ieijjournal
In wireless sensor networks, sensor nodes are easily compromised due to their restricted hardware resources. These compromised nodes inject fabricated votes into legitimate reports, and generate false report and false vote injection attacks. These attacks deplete energy resources and block report transmission. A probabilistic voting-based filtering scheme was proposed to detect the bogus votes in reports en-route to protect against attacks. Although this method detects false votes in intermediate nodes, the sensor network needs to be effectively operated in consideration of a node's conditions. In this paper, the proposed method selects effective verification nodes by considering the condition of nodes based on a fuzzy logic system. In the proposed method, the intermediate node selects between two next hop nodes in its range through a fuzzy logic system before forwarding the report. Experimental results suggest that, compared to the original method, the proposed method improves energy savings up to 11% while maintaining a high security level.
ENSP: ENERGY EFFICIENT NEXT HOP SELECTION IN A PROBABILISTIC VOTING-BASED FIL...ieijjournal
In wireless sensor networks, sensor nodes are easily compromised due to their restricted hardware resources. These compromised nodes inject fabricated votes into legitimate reports, and generate false report and false vote injection attacks. These attacks deplete energy resources and block report transmission. A probabilistic voting-based filtering scheme was proposed to detect the bogus votes in reports en-route to protect against attacks. Although this method detects false votes in intermediate nodes, the sensor network needs to be effectively operated in consideration of a node's conditions. In this paper, the proposed method selects effective verification nodes by considering the condition of nodes based on a fuzzy logic system. In the proposed method, the intermediate node selects between two next hop nodes in its range through a fuzzy logic system before forwarding the report. Experimental results
suggest that, compared to the original method, the proposed method improves energy savings up to 11% while maintaining a high security level.
NUMBER OF NEIGHBOUR NODES BASED NEXT FORWARDING NODES DETERMINATION SCHEME FO...ijcsity
Wireless Sensor Networks (Wsn) Are Used In Various Areas. These Networks Are Deployed In An Open Environment. So, They Are Very Weak Against An Attack, And Easily Damaged.The Wsn Has Limited Resources In Terms Of Battery Life, Computing Power, Communication Bandwidth And So On. Many Attacks Aim At That Point.The False Report Injection Attack Is One Of Them. Yu Et Al. Proposed A Dynamic En-Route Filtering Scheme (Def),To Prevent A False Report Injection Attack.In This Paper, We Propose An Energy Enhancement Scheme For Def Using A Fuzzy System. The Def Is Divided Into Three Phases (Key Pre-Distribution Phase, Key Dissemination Phase, Report Forwarding Phase). We Applied Our Scheme At The Next Forwarding Node Determination. So We Used Three Input Factors Of A Fuzzy System To Make A Determination. These Are The Availability Of Energy, Distance To The Base Station,
And Usage Count.Through The Experiments, Our Proposed Method Shows Up To 8.2% Energy Efficiency,Compared With The Def. If The Networks Consume More Energy, Our Proposed Method Shows More Efficiency For The Energy.
BLACKLIST MANAGEMENT USING A VERIFICATION REPORT TO IMPROVE THE ENERGY EFFICI...ijwmn
Recently, the applications scope of Wireless Sensor Networks (WSNs) has been broadened. WSN communication security is important because sensor nodes are vulnerable to various security attacks when deployed in an open environment. An adversary could exploit this vulnerability to inject false reports into the network. En-route filtering techniques have been researched to block false reports. The CFFS scheme
filters the false report by collaboratively validating the report by clustering the nodes. However, CFFS is not considered effective against repetitive attacks. Repeated attacks have a significant impact on network lifetime. In this paper, we propose a method to detect repetitive attacks with cluster-based false data
filtering and to identify the compromised nodes and quickly block them. The proposed scheme uses fuzzy logic to determine the distribution of additional keys according to the network conditions, thereby improving energy efficiency.
A KEY LEVEL SELECTION WITHIN HASH CHAINS FOR THE EFFICIENT ENERGY CONSUMPTION...ijasa
A wireless sensor network is composed of a base station (BS) and numerous sensor nodes. The sensor
nodes lack security because they operate in an open environment, such as the military. In particular, a false
report injection attack captures and compromises sensor nodes. The attack then causes the compromised
nodes to generate forward false reports. Owing to the false report injection attack, not only does the sensor
network have a false alarm, but its limited energy is also drained. In order to defend the false report
injection attack, over the past few years, several studies have been made looking for a solution to the
attack. Ye et al. studied statistical en-route filtering (SEF). SEF is a method of stochastically verifying event
reports in the en-route filtering phase. SEF can filter many false reports early using verification of
intermediate nodes. However, because the number of keys in a sensor node is fixed by the system, the
sensor network cannot control the event report verification probability depending on the circumstances of
the network. Therefore, it is difficult to efficiently consume energy of the sensor network. In order to solve
the problem, we propose a method which controls the event report verification probability by using a key
sequence level of an event report. In the proposed method, when an intermediate node receives an event
report, the node verifies the event report by comparing a key sequence level of the report and its key
sequence level. Elements determining the key sequence level include the density of neighbour nodes in the
sensing range of a center of stimulus (CoS), the number of hops from the CoS to the BS, and the average of
the key sequence level of intermediate nodes in each path. We simulated the proposed method and the SEF
method to evaluate the performance in terms of energy efficiency and security. In the simulation results, the
proposed method consumed an average of 7.9% less energy of the sensor nodes compared to SEF method.
The number of false reports arriving at the BS of the proposed method was also less, by an average of 6.4,
compared to the SEF method. Through the results, we can see that when the number of false report is large
in the sensor network, the proposed method is more energy-efficient and secure than the SEF method.
A SECURITY PERIOD UPDATE METHOD USING EVALUATION FUNCTION FOR IMPROVING ENERG...csandit
In recent years, Wireless Sensor Networks(WSNs) research has been carried out with the goals
of achieving high security and energy efficiency. In a WSN, sensor nodes are vulnerable to
physical attacks because they are deployed in an open environment. An attacker can inject a
false report into networks using these vulnerabilities. F. Ye et al. proposed statistical en-route
filtering to prevent false report injection attacks. In order to effectively use their scheme,
techniques for determining thresholds using fuzzy logic have been studied. To effectively apply
these techniques to the network, an appropriate update period should be set according to the
network environments. In this paper, we propose a security period update method in order to
improve the lifetime of the network in the statistical en-route filtering approach based on a
wireless sensor network of the cluster environment. The experimental results show that up to an
11.96% improvement of the energy efficiency can be achieved when the security threshold is set
to the optimal period.
LOAD BALANCING MANAGEMENT USING FUZZY LOGIC TO IMPROVE THE REPORT TRANSFER SU...cscpconf
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations (BS) that
collect information over widely deployed sensor nodes. Sensor nodes have limited energy source
and low computing power. Due to those features, there is a disadvantage that user's individual
node management is difficult and they are easily captured by attackers. Therefore, efficient
energy allocation of nodes is important and network security protocol is needed. The
Probabilistic Voting Filtering System (PVFS) is a system that prevents false vote injection
attack and false report attack injected from attackers. The reason for the existence of this
protocol is for energy management of nodes through defence against those attacks and in order
to efficiently manage the network based on PVFS, load balancing of nodes should be performed.
In the proposed scheme, fuzzy logic is applied to each cluster head node (CH) to perform load
balancing by determine whether to perform a role as a verification node and an event
forwarding node. The experiment shows that the event detection rate and the report delivery
success rate are improved in proposed scheme compare with original PVFS.
Signal detection in cognitive radio network (CRN) is influenced by several factors. One of them is
malicious user that emulate primary user (PU) signal. Emulation of PU signal causes detection error. This
paper investigates the impact of malicious user attack to PU signal detection. A number of malicious users
are randomly deployed around secondary user (SU) at a certain distance. They attempt to attack primary
signal detection that is transmitted from 100 km to SU receiver. Then, the received signal power at
secondary receiver and the performance of probability of false alarm and probability of miss detection
under two hypothesis of Neyman Pearson criterion are studied. The derived results show that a number of
malicious users has a significant impact to the performance of received power at SU and detection error
rate.
SPECTRUM SENSING STRATEGY TO ENHANCE THE QOS IN WHITE-FI NETWORKSIJCNC Journal
The rapidly growing number of wireless devices running applications that require high bandwidths, has
resulted in increasing demands for the unlicensed frequency spectrum. Given the scarcity of allocated
unlicensed frequencies, meeting such demands can become a serious concern. Cognitive Radio (CR)
technology opens the door for the opportunistic use of the licensed spectrum to partially address the issues
relevant to the limited availability of unlicensed frequencies. Combining CR and Wi-Fi to form the socalled
White-Fi networks, has been proposed for achieving higher spectrum utilization. This article
discusses the spectrum sensing in White-Fi networks and the impacts that it has on the QoS of typical
applications. It also reports the analysis of such impacts through various simulation studies. Our results
demonstrate the advantages of an adaptive sensing strategy that is capable of changing the related
parameters based on QoS requirements. We also propose such a sensing strategy that can adapt to the
IEEE 802.11e requirements. The goal of the proposed strategy is the enhancement of the overall QoS of the
applications while maintaining efficient sensing of the spectrum. Simulation results of the scenarios that
implement the proposed mechanisms demonstrate noticeable QoS improvements compared to cases where
common sensing methods are utilized in IEEE802.11 networks.
Threats have become a big problem since the past few years since computer viruses are widely recognized as a significant computer threat. However, the role of Information Technology security must be revisit again since it is too often, IT security managers find themselves in the hopeless situation of trying to uphold a maximum of security as requested from management. While at the same time they are considered an obstacle in the way of developing and introducing new applications into business and government network environments. This paper will focus on Transmission Control Protocol Synchronize Flooding attack detections using the Internet Protocol header as a platform to detect threats, especially in the IP protocol and TCP protocol, and check packets using anomaly detection system which has many advantages, and applied it under the open source Linux. The problem is to detect TCP SYN Flood attack through internet security. This paper also focusing on detecting threats in the local network by monitoring all the packets that goes through the networks. The results show that the proposed detection method can detect TCP SYN Flooding in both normal and attacked network and alert the user about the attack after sending the report to the administrator. As conclusion, TCP SYN Flood and other attacks can be detected through this traffic monitoring tools if the abnormal behaviors of the packets are recognized such as incomplete TCP three-way handshake application and IP header length.
misrouting attack in wireless sensor networks under replication attack. agent based security schemes in Security schemes for wireless sensor networks. International journal paper on wireless sensor networks.
A security method for multiple attacks in sensor networks against false repor...ieijjournal
In a large-scale wireless sensor network, damage spreads rapidly in the network when under false report injection, false votes injection, or wormhole attacks. These attacks cause finite energy resources to be drained, legitimate reports to be dropped, and data to be intercepted by adversary nodes. A probabilistic voting-based filtering scheme (PVFS) and localized encryption and authentication protocol (LEAP) can be used to cope with these attacks. When multiple attacks occur simultaneously, PVFS and LEAP should be operated together. But the concurrent application of PVFS and LEAP provides inefficient duplications of operations in the sensor network. In this paper, we propose a security method which improves the energy efficiency while maintaining the security level of applying PVFS and LEAP simultaneously. The proposed method was designed by identifying and eliminating the redundancies of employing both methods together and providing more efficient functionalities. Four types of new keys were also designed for simultaneous detection of multiple attacks. We evaluated the effectiveness of the proposed method compared to simply applying PVFS and LEAP simultaneously when under multiple attacks. The experimental results demonstrate that our proposed method saves energy by up to 11% while maintaining detection power.
SELECTING VOTES FOR ENERGY EFFICIENCY IN PROBABILISTIC VOTING-BASED FILTERING...ijasa
Wireless sensor networks are easily compromised by an adversary, such as fabricated with false votes attacks and false votes on real reports attacks. These attacks generate false data to drain the energy resource of sensors and interrupt the inflow of a real report. PVFS was proposed to detect them by verifying votes in the real report. When a real event occurs, a cluster head collects all of the votes from its neighboring nodes and selects the votes up to a defined number of votes. In this paper, our proposed method decides the number of votes based on a fuzzy rule-based system to improve energy savings as compared to PVFS. We evaluated the effectiveness of the proposal as two attacks occur simultaneously in the sensor network. The experimental results show that our method saves energy resources and maintains the security level against these multiple attacks
Fuzzy-Based Multiple Path Selection Method for Improving Energy Efficiency in...aciijournal
In wireless sensor networks, adversaries can easily compromise sensors because the sensor resources are
limited. The compromised nodes can inject false data into the network injecting false data attacks. The
injecting false data attack has the goal of consuming unnecessary energy in en-route nodes and causing
false alarms in a sink. A bandwidth-efficient cooperative authentication scheme detects this attack based on
the random graph characteristics of sensor node deployment and a cooperative bit-compressed
authentication technique. Although this scheme maintains a high filtering probability and high reliability in
the sensor network, it wastes energy in en-route nodes due to a multireport solution. In this paper, our
proposed method effectively selects a number of multireports based on the fuzzy rule-based system. We
evaluated the performance in terms of the security level and energy savings in the presence of the injecting
false data attacks. The experimental results indicate that the proposed method improves the energy
efficiency up to 10% while maintaining the same security level as compared to the existing scheme.
Wireless sensor networks (WSNs) are regularly deployed in harsh and unattended environments, and
sensor nodes are easily exposed to attacks due to the random arrangement of the sensor field. An attacker
can inject fabricated reports from a compromised node with false votes and false vote-based reports. The
false report attacks can waste the energy of the intermediate nodes, shortening the network lifetime.
Furthermore, false votes cause the filtering out of legitimate reports. A probabilistic voting-based filtering
scheme (PVFS) was proposed as a countermeasure against this type of attacks by Li and Wu. PVFS uses a
vote threshold, a security threshold, and a verification node. The scheme does not make additional use
energy or communications resources because the verification node and threshold values are fixed. There
needs to be a verification node selection method that considers the energy resources of the node. In this
paper, we propose a verification path election scheme based on a fuzzy logic system. In the proposed
scheme, one node transmits reports in the node with a strong state through a fuzzy logic system after which
a neighbor is selected out of two from the surroundings. Experimental results show that the proposed
scheme improves energy savings up to maximum 13% relative to the PVFS.
A secure routing process to simultaneously defend against false report and wo...ieijjournal
Most research related to secure routing in sensor networks has focused on how to detect and defend against a single attack. However, it is not feasible to predict which attack will occur in sensor networks. It is possible for multiple attacks to occur simultaneously, degrading the performance of the existing security schemes. For example, an attacker may try simultaneous false report and wormhole attacks to effectively damage a sensor network. Hence, a multiple simultaneous attack environment is much more complex than a single attack environment. Thus, a new security scheme that can detect multiple simultaneous attacks with a high probability and low energy consumption is needed. In this paper, we propose a secure routing scheme to defend against wormhole and false report attacks in sensor networks. The proposed method achieves a higher attack detection ratio and consumes less energy in a multi-attack scenario compared to existing schemes. It can also be extended to other types of attacks and security schemes to detect and defend against possible combinations of multiple attacks.
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilistically selects a validation node, determines the validity of the report, and filters the report based on the thresholds that have been set. In this paper, the proposed scheme improves the lifetime, detection rate, and report delivery rate of the entire network by increasing the lifetime of the cluster head (CH) by selecting the numbers of message authentication codes (MACs) and verification nodes of the report. Using this system, the event detection rate and the network lifetime are improved by up to 18% and 6%, respectively, relative to the existing PVFS.
WSN LIFETIME EXTENSION USING GA OPTIMISED FUZZY LOGICijcsit
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilistically selects a validation node, determines the validity of the report, and filters the report based on the thresholds that have been set. In this paper, the proposed scheme improves the lifetime, detection rate, and report delivery rate of the entire network by increasing the lifetime of the cluster head (CH) by selecting the numbers of message authentication codes (MACs) and verification nodes of the report. Using this system, the event detection rate and the network lifetime are improved by up to 18% and 6%, respectively, relative to the existing PVFS.
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilistically selects a validation node, determines the validity of the report, and filters the report based on the thresholds that have been set. In this paper, the proposed scheme improves the lifetime, detection rate, and report delivery rate of the entire network by increasing the lifetime of the cluster head (CH) by selecting the numbers of message authentication codes (MACs) and verification nodes of the report. Using this system, the event detection rate and the network lifetime are improved by up to 18% and 6%, respectively, relative to the existing PVFS.
Ensp energy efficient next hop selection in a probabilistic voting based filt...ieijjournal
In wireless sensor networks, sensor nodes are easily compromised due to their restricted hardware resources. These compromised nodes inject fabricated votes into legitimate reports, and generate false report and false vote injection attacks. These attacks deplete energy resources and block report transmission. A probabilistic voting-based filtering scheme was proposed to detect the bogus votes in reports en-route to protect against attacks. Although this method detects false votes in intermediate nodes, the sensor network needs to be effectively operated in consideration of a node's conditions. In this paper, the proposed method selects effective verification nodes by considering the condition of nodes based on a fuzzy logic system. In the proposed method, the intermediate node selects between two next hop nodes in its range through a fuzzy logic system before forwarding the report. Experimental results suggest that, compared to the original method, the proposed method improves energy savings up to 11% while maintaining a high security level.
ENSP: ENERGY EFFICIENT NEXT HOP SELECTION IN A PROBABILISTIC VOTING-BASED FIL...ieijjournal
In wireless sensor networks, sensor nodes are easily compromised due to their restricted hardware resources. These compromised nodes inject fabricated votes into legitimate reports, and generate false report and false vote injection attacks. These attacks deplete energy resources and block report transmission. A probabilistic voting-based filtering scheme was proposed to detect the bogus votes in reports en-route to protect against attacks. Although this method detects false votes in intermediate nodes, the sensor network needs to be effectively operated in consideration of a node's conditions. In this paper, the proposed method selects effective verification nodes by considering the condition of nodes based on a fuzzy logic system. In the proposed method, the intermediate node selects between two next hop nodes in its range through a fuzzy logic system before forwarding the report. Experimental results
suggest that, compared to the original method, the proposed method improves energy savings up to 11% while maintaining a high security level.
NUMBER OF NEIGHBOUR NODES BASED NEXT FORWARDING NODES DETERMINATION SCHEME FO...ijcsity
Wireless Sensor Networks (Wsn) Are Used In Various Areas. These Networks Are Deployed In An Open Environment. So, They Are Very Weak Against An Attack, And Easily Damaged.The Wsn Has Limited Resources In Terms Of Battery Life, Computing Power, Communication Bandwidth And So On. Many Attacks Aim At That Point.The False Report Injection Attack Is One Of Them. Yu Et Al. Proposed A Dynamic En-Route Filtering Scheme (Def),To Prevent A False Report Injection Attack.In This Paper, We Propose An Energy Enhancement Scheme For Def Using A Fuzzy System. The Def Is Divided Into Three Phases (Key Pre-Distribution Phase, Key Dissemination Phase, Report Forwarding Phase). We Applied Our Scheme At The Next Forwarding Node Determination. So We Used Three Input Factors Of A Fuzzy System To Make A Determination. These Are The Availability Of Energy, Distance To The Base Station,
And Usage Count.Through The Experiments, Our Proposed Method Shows Up To 8.2% Energy Efficiency,Compared With The Def. If The Networks Consume More Energy, Our Proposed Method Shows More Efficiency For The Energy.
BLACKLIST MANAGEMENT USING A VERIFICATION REPORT TO IMPROVE THE ENERGY EFFICI...ijwmn
Recently, the applications scope of Wireless Sensor Networks (WSNs) has been broadened. WSN communication security is important because sensor nodes are vulnerable to various security attacks when deployed in an open environment. An adversary could exploit this vulnerability to inject false reports into the network. En-route filtering techniques have been researched to block false reports. The CFFS scheme
filters the false report by collaboratively validating the report by clustering the nodes. However, CFFS is not considered effective against repetitive attacks. Repeated attacks have a significant impact on network lifetime. In this paper, we propose a method to detect repetitive attacks with cluster-based false data
filtering and to identify the compromised nodes and quickly block them. The proposed scheme uses fuzzy logic to determine the distribution of additional keys according to the network conditions, thereby improving energy efficiency.
A KEY LEVEL SELECTION WITHIN HASH CHAINS FOR THE EFFICIENT ENERGY CONSUMPTION...ijasa
A wireless sensor network is composed of a base station (BS) and numerous sensor nodes. The sensor
nodes lack security because they operate in an open environment, such as the military. In particular, a false
report injection attack captures and compromises sensor nodes. The attack then causes the compromised
nodes to generate forward false reports. Owing to the false report injection attack, not only does the sensor
network have a false alarm, but its limited energy is also drained. In order to defend the false report
injection attack, over the past few years, several studies have been made looking for a solution to the
attack. Ye et al. studied statistical en-route filtering (SEF). SEF is a method of stochastically verifying event
reports in the en-route filtering phase. SEF can filter many false reports early using verification of
intermediate nodes. However, because the number of keys in a sensor node is fixed by the system, the
sensor network cannot control the event report verification probability depending on the circumstances of
the network. Therefore, it is difficult to efficiently consume energy of the sensor network. In order to solve
the problem, we propose a method which controls the event report verification probability by using a key
sequence level of an event report. In the proposed method, when an intermediate node receives an event
report, the node verifies the event report by comparing a key sequence level of the report and its key
sequence level. Elements determining the key sequence level include the density of neighbour nodes in the
sensing range of a center of stimulus (CoS), the number of hops from the CoS to the BS, and the average of
the key sequence level of intermediate nodes in each path. We simulated the proposed method and the SEF
method to evaluate the performance in terms of energy efficiency and security. In the simulation results, the
proposed method consumed an average of 7.9% less energy of the sensor nodes compared to SEF method.
The number of false reports arriving at the BS of the proposed method was also less, by an average of 6.4,
compared to the SEF method. Through the results, we can see that when the number of false report is large
in the sensor network, the proposed method is more energy-efficient and secure than the SEF method.
A SECURITY PERIOD UPDATE METHOD USING EVALUATION FUNCTION FOR IMPROVING ENERG...csandit
In recent years, Wireless Sensor Networks(WSNs) research has been carried out with the goals
of achieving high security and energy efficiency. In a WSN, sensor nodes are vulnerable to
physical attacks because they are deployed in an open environment. An attacker can inject a
false report into networks using these vulnerabilities. F. Ye et al. proposed statistical en-route
filtering to prevent false report injection attacks. In order to effectively use their scheme,
techniques for determining thresholds using fuzzy logic have been studied. To effectively apply
these techniques to the network, an appropriate update period should be set according to the
network environments. In this paper, we propose a security period update method in order to
improve the lifetime of the network in the statistical en-route filtering approach based on a
wireless sensor network of the cluster environment. The experimental results show that up to an
11.96% improvement of the energy efficiency can be achieved when the security threshold is set
to the optimal period.
LOAD BALANCING MANAGEMENT USING FUZZY LOGIC TO IMPROVE THE REPORT TRANSFER SU...cscpconf
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations (BS) that
collect information over widely deployed sensor nodes. Sensor nodes have limited energy source
and low computing power. Due to those features, there is a disadvantage that user's individual
node management is difficult and they are easily captured by attackers. Therefore, efficient
energy allocation of nodes is important and network security protocol is needed. The
Probabilistic Voting Filtering System (PVFS) is a system that prevents false vote injection
attack and false report attack injected from attackers. The reason for the existence of this
protocol is for energy management of nodes through defence against those attacks and in order
to efficiently manage the network based on PVFS, load balancing of nodes should be performed.
In the proposed scheme, fuzzy logic is applied to each cluster head node (CH) to perform load
balancing by determine whether to perform a role as a verification node and an event
forwarding node. The experiment shows that the event detection rate and the report delivery
success rate are improved in proposed scheme compare with original PVFS.
An Enhanced Detection and Energy-Efficient En-Route Filtering Scheme in Wirel...ieijjournal
Wireless sensor networks (WSNs), due to their small size, low cost, and untethered communication over a short-range, have great potential for applications and services. Due to hostile environments and an unattended nature, they are prone to many types of attacks by adversaries. False data injection attacks compromise data accuracy at the sink node and cause undesirable energy depletion at the sink and intermediate nodes. In order to detect and counter false data attacks, a number of en-route filtering schemes have been proposed. However, they lack a strong false report detection capacity or cannot support network dynamics well. Commutative cipher-based en-route filtering (CCEF) is based on fixed paths, and a fixed detection probability, and does not consider the residual energy of a node. In an enhanced detectioncapacity and energy-efficient en-route filtering (EDEF) scheme, we use a fuzzy logic system which considers the residual energy, false traffic ratio (FTR), and number of message authentication codes (MACs) in a report to evaluate the fitness of a node to be a verification node. This helps to balance network energy usage and reduce the number of hops a false report may travel. The simulation results demonstrate the validity of our scheme with increased energy-efficiency (4.55 to 13.92%) and detection power (99.95%)
AN ENHANCED DETECTION AND ENERGYEFFICIENT EN-ROUTE FILTERING SCHEME IN WIRELE...ieijjournal
Wireless sensor networks (WSNs), due to their small size, low cost, and untethered communication over a short-range, have great potential for applications and services. Due to hostile environments and an unattended nature, they are prone to many types of attacks by adversaries. False data injection attacks compromise data accuracy at the sink node and cause undesirable energy depletion at the sink and intermediate nodes. In order to detect and counter false data attacks, a number of en-route filtering schemes have been proposed. However, they lack a strong false report detection capacity or cannot support network dynamics well. Commutative cipher-based en-route filtering (CCEF) is based on fixed paths, and a
fixed detection probability, and does not consider the residual energy of a node. In an enhanced detectioncapacity and energy-efficient en-route filtering (EDEF) scheme, we use a fuzzy logic system which considers the residual energy, false traffic ratio (FTR), and number of message authentication codes
(MACs) in a report to evaluate the fitness of a node to be a verification node. This helps to balance network
energy usage and reduce the number of hops a false report may travel. The simulation results demonstrate the validity of our scheme with increased energy-efficiency (4.55 to 13.92%) and detection power (99.95%) against false report attacks in WSNs.
Wireless sensor networks provide ubiquitous computing systems in various open environments. In the
environment, sensor nodes can easily be compromised by adversaries to generate injecting false data
attacks. The injecting false data attack not only consumes unnecessary energy in en-route nodes, but also
causes false alarms at the base station. To detect this type of attack, a bandwidth-efficient cooperative
authentication (BECAN) scheme was proposed to achieve high filtering probability and high reliability
based on random graph characteristics and cooperative bit-compressed authentication techniques. This
scheme may waste energy resources in en-route nodes due to the fixed number of forwarding reports. In
this paper, our proposed method effectively selects a dynamic number of forwarding reports in the source
nodes based on an evaluation function. The experimental results indicate that our proposed method
enhances the energy savings while maintaining security levels as compared to BECAN.
A Method of Improving Energy Efficiency Through Geofencing and False Data Blo...IJCNCJournal
In wireless sensor networks, sensor nodes have the disadvantage of being vulnerable to several attacks due to the use of wireless communication and constrained energy. Adversaries exploit vulnerable characteristics of these nodes to capture them and generate false positive and false negative attacks. These attacks result in false alarms in a base station and information loss in intermediate nodes. A context-aware architecture for a probabilistic voting-based filtering scheme (CAA-PVFS) identifies compromised nodes that cause the damage. Although this method immediately detects the compromised nodes using its CAA, its additional network use consumes unnecessary energy. In this paper, our proposed method configures geofencing for the compromised nodes and blocks the nodes using false data injection. The proposed method reduces the unnecessary energy of the additional network while maintaining security strength. Experimental results indicate that the proposed method offers energy savings of up to 17% while maintaining the security strength against the two attacks as compared to the existing method.
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FUZZY-BASED ENERGY EFFICIENT METHOD FOR MULTIPLE ATTACKS IN SENSOR NETWORKS: AGAINST FALSE VOTE AND REPORT INJECTION ATTACKS
1. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
DOI: 10.5121/ijci.2013.2201 1
FUZZY-BASED ENERGY EFFICIENT METHOD FOR
MULTIPLE ATTACKS IN SENSOR NETWORKS:
AGAINST FALSE VOTE AND REPORT INJECTION
ATTACKS
Su Man Nam1
and Tae Ho Cho2
1
College of Information and Communication Engineering, Sungkyunkwan University,
Suwon 440-746, Republic of Korea
smnam@ece.skku.ac.kr
2
College of Information and Communication Engineering, Sungkyunkwan University,
Suwon 440-746, Republic of Korea
taecho@ece.skku.ac.kr
ABSTRACT
An adversary can easily compromise sensor nodes in wireless sensor networks, and generate multiple
attacks through compromised nodes, such as false vote injection attacks and false report injection attacks.
The false vote injection attack tries to drop legitimate reports in an intermediate node, and the false report
injection attack tries to drain the energy consumption of each node. To prevent these attacks, a
probabilistic voting-based filtering scheme (PVFS) has been proposed to select verification nodes, and to
detect fabricated votes in the reports as they occur simultaneously. In this paper, we propose a method that
improves the energy efficiency of each node and the security level of the false report injection attack, while
maintaining the detection power of the false vote injection attack. Our proposed method effectively selects
verification node with considering the conditions of each node, based on a fuzzy rule-based system. The
verification node is decided through the energy remaining level, distance level, and number of detected
false votes in the fuzzy system. We evaluated the effectiveness of the proposed method, as compared to
PVFS, when two attacks occur simultaneously in the sensor network. The experimental results show that
our method saves energy by up to 8%, by improving and maintaining the defence against these multiple
attacks.
KEYWORDS
Wireless sensor network, Probabilistic voting-based filtering scheme, False report injection attacks, False
vote injection attacks, Fuzzy system
1. INTRODUCTION
Wireless sensor networks (WSNs) are economically feasible technologies for a variety of
applications [1]-[2]. These sensor networks enable the low-cost and low-power development of
multi-functional use in open environments [2]. A WSN is composed of a base station, and a large
number of sensors in a sensor field. The base station collects event data from the sensor nodes,
and provides information to users [1], [3]. However, adversaries can easily compromise the
nodes, because of their limited computation, communication, storage, and energy supply
capacities [4]-[5]. The adversaries can simultaneously generate multiple attacks, such as false
vote injection and false report injection attacks, to destroy the wireless network.
2. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
2
Figure 1. Multiple attacks.
Figure 1 show multiple attacks in the sensor network, such as a false vote injection attack (FRIA)
[6]-[7], and a false report injection attack (FVIA) [7]. Two nodes (Figure 1-a and Figure 1-b) are
compromised by forward false reports and false votes. In FVIV, a compromised node (Figure 1-a)
injects a false vote in a legitimate report (Figure 1-c) to drop the report en route (Figure 1-d). The
legitimate report is filtered out in an intermediate node, before arriving at the base station. In
FRIA, a compromised node (Figure 1-b) injects a false report with false votes (Figure 1-e), to
drain the energy resource of intermediate nodes (Figure 1-f), and cause false alarms in the base
station. These attacks drop real event information, and consume needless energy.
Li et al. [7] proposed a probabilistic voting-based filtering scheme (PVFS) to detect multiple
attacks in intermediate nodes, as FRIA and FVIA simultaneously occur in the sensor network.
This method is suitable for filtering fabricated votes based on a cluster-based model. The scheme
selects verification CHs with a probability to prevent the fabricated votes before forwarding a
report. It is different from having verification CHs in a path, when reports are transmitted.
In this paper, we propose a method to effectively select verification CHs based on a fuzzy rule-
based system [8]. Our proposed method decides the verification CHs through the energy
remaining level, distance level, and number of detected false votes. Therefore, our method
increases the security level and energy savings against FVIA and FRIA, as compared to PVFS.
The remainder of this paper is organized as follows. The background and motivation are
described in Section 2. The proposed method is introduced in Section 3, and the experimental
results are described in Section 4. Finally, conclusions and future work are discussed in Section 5.
2. BACKGROUND
In the sensor network, FRIA and FVIA are frequently generated in the application layer, and
threaten the lifetime of the network. FRIA injects false reports to cause unnecessary energy of
sensor nodes. FVIA injects false votes in a legitimate report to be filtered out. We will discuss an
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
2
Figure 1. Multiple attacks.
Figure 1 show multiple attacks in the sensor network, such as a false vote injection attack (FRIA)
[6]-[7], and a false report injection attack (FVIA) [7]. Two nodes (Figure 1-a and Figure 1-b) are
compromised by forward false reports and false votes. In FVIV, a compromised node (Figure 1-a)
injects a false vote in a legitimate report (Figure 1-c) to drop the report en route (Figure 1-d). The
legitimate report is filtered out in an intermediate node, before arriving at the base station. In
FRIA, a compromised node (Figure 1-b) injects a false report with false votes (Figure 1-e), to
drain the energy resource of intermediate nodes (Figure 1-f), and cause false alarms in the base
station. These attacks drop real event information, and consume needless energy.
Li et al. [7] proposed a probabilistic voting-based filtering scheme (PVFS) to detect multiple
attacks in intermediate nodes, as FRIA and FVIA simultaneously occur in the sensor network.
This method is suitable for filtering fabricated votes based on a cluster-based model. The scheme
selects verification CHs with a probability to prevent the fabricated votes before forwarding a
report. It is different from having verification CHs in a path, when reports are transmitted.
In this paper, we propose a method to effectively select verification CHs based on a fuzzy rule-
based system [8]. Our proposed method decides the verification CHs through the energy
remaining level, distance level, and number of detected false votes. Therefore, our method
increases the security level and energy savings against FVIA and FRIA, as compared to PVFS.
The remainder of this paper is organized as follows. The background and motivation are
described in Section 2. The proposed method is introduced in Section 3, and the experimental
results are described in Section 4. Finally, conclusions and future work are discussed in Section 5.
2. BACKGROUND
In the sensor network, FRIA and FVIA are frequently generated in the application layer, and
threaten the lifetime of the network. FRIA injects false reports to cause unnecessary energy of
sensor nodes. FVIA injects false votes in a legitimate report to be filtered out. We will discuss an
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
2
Figure 1. Multiple attacks.
Figure 1 show multiple attacks in the sensor network, such as a false vote injection attack (FRIA)
[6]-[7], and a false report injection attack (FVIA) [7]. Two nodes (Figure 1-a and Figure 1-b) are
compromised by forward false reports and false votes. In FVIV, a compromised node (Figure 1-a)
injects a false vote in a legitimate report (Figure 1-c) to drop the report en route (Figure 1-d). The
legitimate report is filtered out in an intermediate node, before arriving at the base station. In
FRIA, a compromised node (Figure 1-b) injects a false report with false votes (Figure 1-e), to
drain the energy resource of intermediate nodes (Figure 1-f), and cause false alarms in the base
station. These attacks drop real event information, and consume needless energy.
Li et al. [7] proposed a probabilistic voting-based filtering scheme (PVFS) to detect multiple
attacks in intermediate nodes, as FRIA and FVIA simultaneously occur in the sensor network.
This method is suitable for filtering fabricated votes based on a cluster-based model. The scheme
selects verification CHs with a probability to prevent the fabricated votes before forwarding a
report. It is different from having verification CHs in a path, when reports are transmitted.
In this paper, we propose a method to effectively select verification CHs based on a fuzzy rule-
based system [8]. Our proposed method decides the verification CHs through the energy
remaining level, distance level, and number of detected false votes. Therefore, our method
increases the security level and energy savings against FVIA and FRIA, as compared to PVFS.
The remainder of this paper is organized as follows. The background and motivation are
described in Section 2. The proposed method is introduced in Section 3, and the experimental
results are described in Section 4. Finally, conclusions and future work are discussed in Section 5.
2. BACKGROUND
In the sensor network, FRIA and FVIA are frequently generated in the application layer, and
threaten the lifetime of the network. FRIA injects false reports to cause unnecessary energy of
sensor nodes. FVIA injects false votes in a legitimate report to be filtered out. We will discuss an
3. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
3
existing countermeasure against FRIA and FVIA in Section 2.1 and explains the motivation for
our proposed method.
2.1. PVFS: Probabilistic Voting-based Filtering Scheme
Li et al. [1] proposed a probabilistic voting-based filtering scheme to simultaneously prevent
multiple attacks in a sensor network, and to maintain the detection power at a sufficiently high
level, such as FRIA and FVIA. PVFS uses a voting method with a cluster-based model and a
probability key assignment. This scheme consists of four phases: 1) key assignment, 2) report
generation, 3) verification node selection, and 4) en-route filtering. 1) As sensor nodes are
deployed, a cluster head (CH) is elected in each cluster region, and each sensor selects one key
from the partition of the CH in a global key pool. 2) When a real event occurs, the CH collects all
votes (such as MAC [6]) from its neighboring nodes. The CH randomly selects the votes as a
defined value, and attaches a report. 3) The CH chooses verification CHs with a probability to
verify the attached votes in the report. 4) If the number of detected false votes in a report is lower
than the threshold value, FVIA is considered in a verification CH, and transmits it to next hop. If
the number of detected false votes is greater than the threshold value, FRIA is considered in a
verification CH, and drops it. Therefore, PVFS detects fabricated votes generated from a
compromised node and prevents FVIA and FRIA in the intermediate nodes.
Figure 2. Filtering Processes in PVFS.
Error! Reference source not found. illustrates phases of the report generation in a cluster
(Error! Reference source not found.-a), the verification CH selection (Error! Reference
source not found.-b), and en-route filtering (Error! Reference source not found.-c) in a path.
The cluster is comprised of a CH and five nodes including two compromised node (Node2 and
Node4). We consider that s is the required number of votes a legitimate report should carry, and
fT is the threshold of false votes required to drop a report. In phase of the report generation, when
a real event occurs in the cluster, CH0 broadcasts to its neighbors. The neighboring nodes forward
their vote including event information to the CH. After collecting the votes, Vote1, Vote2, Vote3,
Vote5 is randomly selected to produce a report. In phase of the verification CH selection, the CH1
and CH2 are chosen for verification of the votes by a probability 0/= ddP i (the hop count from a
verification CHi/CH0 to the base station). That is, there are 9/8=1P of CH1, 97=2P of CH2, and
96=3P of CH3, respectively (the hops of CH0 is 9). Both CH1 gets CH2 get verification keys of
CH0. CH1 gets keys }5,4,3,1{=K , and CH2 get keys }3,2,1{=K . In phase of en-route filtering,
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
3
existing countermeasure against FRIA and FVIA in Section 2.1 and explains the motivation for
our proposed method.
2.1. PVFS: Probabilistic Voting-based Filtering Scheme
Li et al. [1] proposed a probabilistic voting-based filtering scheme to simultaneously prevent
multiple attacks in a sensor network, and to maintain the detection power at a sufficiently high
level, such as FRIA and FVIA. PVFS uses a voting method with a cluster-based model and a
probability key assignment. This scheme consists of four phases: 1) key assignment, 2) report
generation, 3) verification node selection, and 4) en-route filtering. 1) As sensor nodes are
deployed, a cluster head (CH) is elected in each cluster region, and each sensor selects one key
from the partition of the CH in a global key pool. 2) When a real event occurs, the CH collects all
votes (such as MAC [6]) from its neighboring nodes. The CH randomly selects the votes as a
defined value, and attaches a report. 3) The CH chooses verification CHs with a probability to
verify the attached votes in the report. 4) If the number of detected false votes in a report is lower
than the threshold value, FVIA is considered in a verification CH, and transmits it to next hop. If
the number of detected false votes is greater than the threshold value, FRIA is considered in a
verification CH, and drops it. Therefore, PVFS detects fabricated votes generated from a
compromised node and prevents FVIA and FRIA in the intermediate nodes.
Figure 2. Filtering Processes in PVFS.
Error! Reference source not found. illustrates phases of the report generation in a cluster
(Error! Reference source not found.-a), the verification CH selection (Error! Reference
source not found.-b), and en-route filtering (Error! Reference source not found.-c) in a path.
The cluster is comprised of a CH and five nodes including two compromised node (Node2 and
Node4). We consider that s is the required number of votes a legitimate report should carry, and
fT is the threshold of false votes required to drop a report. In phase of the report generation, when
a real event occurs in the cluster, CH0 broadcasts to its neighbors. The neighboring nodes forward
their vote including event information to the CH. After collecting the votes, Vote1, Vote2, Vote3,
Vote5 is randomly selected to produce a report. In phase of the verification CH selection, the CH1
and CH2 are chosen for verification of the votes by a probability 0/= ddP i (the hop count from a
verification CHi/CH0 to the base station). That is, there are 9/8=1P of CH1, 97=2P of CH2, and
96=3P of CH3, respectively (the hops of CH0 is 9). Both CH1 gets CH2 get verification keys of
CH0. CH1 gets keys }5,4,3,1{=K , and CH2 get keys }3,2,1{=K . In phase of en-route filtering,
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
3
existing countermeasure against FRIA and FVIA in Section 2.1 and explains the motivation for
our proposed method.
2.1. PVFS: Probabilistic Voting-based Filtering Scheme
Li et al. [1] proposed a probabilistic voting-based filtering scheme to simultaneously prevent
multiple attacks in a sensor network, and to maintain the detection power at a sufficiently high
level, such as FRIA and FVIA. PVFS uses a voting method with a cluster-based model and a
probability key assignment. This scheme consists of four phases: 1) key assignment, 2) report
generation, 3) verification node selection, and 4) en-route filtering. 1) As sensor nodes are
deployed, a cluster head (CH) is elected in each cluster region, and each sensor selects one key
from the partition of the CH in a global key pool. 2) When a real event occurs, the CH collects all
votes (such as MAC [6]) from its neighboring nodes. The CH randomly selects the votes as a
defined value, and attaches a report. 3) The CH chooses verification CHs with a probability to
verify the attached votes in the report. 4) If the number of detected false votes in a report is lower
than the threshold value, FVIA is considered in a verification CH, and transmits it to next hop. If
the number of detected false votes is greater than the threshold value, FRIA is considered in a
verification CH, and drops it. Therefore, PVFS detects fabricated votes generated from a
compromised node and prevents FVIA and FRIA in the intermediate nodes.
Figure 2. Filtering Processes in PVFS.
Error! Reference source not found. illustrates phases of the report generation in a cluster
(Error! Reference source not found.-a), the verification CH selection (Error! Reference
source not found.-b), and en-route filtering (Error! Reference source not found.-c) in a path.
The cluster is comprised of a CH and five nodes including two compromised node (Node2 and
Node4). We consider that s is the required number of votes a legitimate report should carry, and
fT is the threshold of false votes required to drop a report. In phase of the report generation, when
a real event occurs in the cluster, CH0 broadcasts to its neighbors. The neighboring nodes forward
their vote including event information to the CH. After collecting the votes, Vote1, Vote2, Vote3,
Vote5 is randomly selected to produce a report. In phase of the verification CH selection, the CH1
and CH2 are chosen for verification of the votes by a probability 0/= ddP i (the hop count from a
verification CHi/CH0 to the base station). That is, there are 9/8=1P of CH1, 97=2P of CH2, and
96=3P of CH3, respectively (the hops of CH0 is 9). Both CH1 gets CH2 get verification keys of
CH0. CH1 gets keys }5,4,3,1{=K , and CH2 get keys }3,2,1{=K . In phase of en-route filtering,
4. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
4
when the report arrives in CH1, Vote1, Vote3, and Vote5 are true by using the keys of CH1. The
report is forwarded to CH2 with 0=fT . CH2 finds a fabricated Vote2 by key 2 and sets the
corresponding bit to 1. The report is transmitted to next CH3 as 2=fT has not been reached yet.
The legitimate report will be safely forwarded in the base station against FVIA (Error!
Reference source not found.-d). That is, PVFS detects FVIA through the number of the
threshold value as 2=fT has not been reached yet. In contrast, when CH0 selects Vote1, Vote2,
Vote3, and Vote 4, a false report is forwarded with fabricated Vote2 and Vote4. CH1 detects the
false Vote4 by key 4, and sets the corresponding bit to 1. When the false report arrives in CH2, it
drops the false report because the false Vote2 is detected by key 2 as 2=fT has been reached. The
false report is en-route filtered out in intermediate nodes against FRIA. That is PVFS prevents
FRIA through the number of threshold value in the false report as 2≥fT has been reached.
Therefore, PVFS selects verification CHs by using the probability, detects both of FRIA and
FVIA through the threshold value as they simultaneously occurs in the sensor network.
2.2. Motivation
In order to simultaneously detect multiple attacks, such as FRIA and FVIA, PVFS should be
operated in the sensor network. PVFS has the phases of the key assignment, the report generation,
the verification CH selection, and en-route filtering. The phase of en-route filtering affects energy
consumption of each node because of the detection of injected votes. In this paper, we effectively
select verification CHs based a fuzzy rule-based system to early detect both of FRIA and FVIA.
Figure 3. Motivation.
Error! Reference source not found. shows the verification CH selection in the proposed method
based the fuzzy system instead of the probability selection of PVFS. Before forwarding a report, a
CH effectively decide verification through a fuzzy rule-based system. Our method improves the
detection power of FRIA while maintaining the security level of FVIA. Therefore, our proposed
method saves energy resources of each node in the sensor network compared to PVFS. In
addition, we expect that our method will prolong the lifetime as the whole network has a long-
term operation.
3. PROPOSED METHOD
Our proposed method selects verification CHs based the fuzzy rule-based system to detect false
votes before forwarding reports. The fuzzy system decides the verification CH through three
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
4
when the report arrives in CH1, Vote1, Vote3, and Vote5 are true by using the keys of CH1. The
report is forwarded to CH2 with 0=fT . CH2 finds a fabricated Vote2 by key 2 and sets the
corresponding bit to 1. The report is transmitted to next CH3 as 2=fT has not been reached yet.
The legitimate report will be safely forwarded in the base station against FVIA (Error!
Reference source not found.-d). That is, PVFS detects FVIA through the number of the
threshold value as 2=fT has not been reached yet. In contrast, when CH0 selects Vote1, Vote2,
Vote3, and Vote 4, a false report is forwarded with fabricated Vote2 and Vote4. CH1 detects the
false Vote4 by key 4, and sets the corresponding bit to 1. When the false report arrives in CH2, it
drops the false report because the false Vote2 is detected by key 2 as 2=fT has been reached. The
false report is en-route filtered out in intermediate nodes against FRIA. That is PVFS prevents
FRIA through the number of threshold value in the false report as 2≥fT has been reached.
Therefore, PVFS selects verification CHs by using the probability, detects both of FRIA and
FVIA through the threshold value as they simultaneously occurs in the sensor network.
2.2. Motivation
In order to simultaneously detect multiple attacks, such as FRIA and FVIA, PVFS should be
operated in the sensor network. PVFS has the phases of the key assignment, the report generation,
the verification CH selection, and en-route filtering. The phase of en-route filtering affects energy
consumption of each node because of the detection of injected votes. In this paper, we effectively
select verification CHs based a fuzzy rule-based system to early detect both of FRIA and FVIA.
Figure 3. Motivation.
Error! Reference source not found. shows the verification CH selection in the proposed method
based the fuzzy system instead of the probability selection of PVFS. Before forwarding a report, a
CH effectively decide verification through a fuzzy rule-based system. Our method improves the
detection power of FRIA while maintaining the security level of FVIA. Therefore, our proposed
method saves energy resources of each node in the sensor network compared to PVFS. In
addition, we expect that our method will prolong the lifetime as the whole network has a long-
term operation.
3. PROPOSED METHOD
Our proposed method selects verification CHs based the fuzzy rule-based system to detect false
votes before forwarding reports. The fuzzy system decides the verification CH through three
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
4
when the report arrives in CH1, Vote1, Vote3, and Vote5 are true by using the keys of CH1. The
report is forwarded to CH2 with 0=fT . CH2 finds a fabricated Vote2 by key 2 and sets the
corresponding bit to 1. The report is transmitted to next CH3 as 2=fT has not been reached yet.
The legitimate report will be safely forwarded in the base station against FVIA (Error!
Reference source not found.-d). That is, PVFS detects FVIA through the number of the
threshold value as 2=fT has not been reached yet. In contrast, when CH0 selects Vote1, Vote2,
Vote3, and Vote 4, a false report is forwarded with fabricated Vote2 and Vote4. CH1 detects the
false Vote4 by key 4, and sets the corresponding bit to 1. When the false report arrives in CH2, it
drops the false report because the false Vote2 is detected by key 2 as 2=fT has been reached. The
false report is en-route filtered out in intermediate nodes against FRIA. That is PVFS prevents
FRIA through the number of threshold value in the false report as 2≥fT has been reached.
Therefore, PVFS selects verification CHs by using the probability, detects both of FRIA and
FVIA through the threshold value as they simultaneously occurs in the sensor network.
2.2. Motivation
In order to simultaneously detect multiple attacks, such as FRIA and FVIA, PVFS should be
operated in the sensor network. PVFS has the phases of the key assignment, the report generation,
the verification CH selection, and en-route filtering. The phase of en-route filtering affects energy
consumption of each node because of the detection of injected votes. In this paper, we effectively
select verification CHs based a fuzzy rule-based system to early detect both of FRIA and FVIA.
Figure 3. Motivation.
Error! Reference source not found. shows the verification CH selection in the proposed method
based the fuzzy system instead of the probability selection of PVFS. Before forwarding a report, a
CH effectively decide verification through a fuzzy rule-based system. Our method improves the
detection power of FRIA while maintaining the security level of FVIA. Therefore, our proposed
method saves energy resources of each node in the sensor network compared to PVFS. In
addition, we expect that our method will prolong the lifetime as the whole network has a long-
term operation.
3. PROPOSED METHOD
Our proposed method selects verification CHs based the fuzzy rule-based system to detect false
votes before forwarding reports. The fuzzy system decides the verification CH through three
5. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
5
input factors of energy remaining level, distance level, and the number of detected false votes.
Therefore, our proposed method improves a security level and energy effectiveness compared to
PVFS. In this section, the proposed method is described in detail.
3.1. Assumptions
We assume the sensor nodes are fixed after they are deployed. The sensor network composes a
base station and a number of sensor nodes, e.g. the Berkeley MICAz motes [3], the initial
paths is established through directed diffusion [4], and minimum cost forwarding algorithms
[5]. We choose the cluster-based model Error! Reference source not found. to organize the
sensor nodes. In a cluster, one node is elected to be a CH.
It is further assumed that every node forwards reports into the base station along their path. A
compromised node generates false reports in a path. The generated false reports are forwarded
from a compromised node toward the base station before filtering it out.
3.2. Overview
Our proposed method selects verification CHs based the fuzzy rule-based system before
forwarding a report to next intermediate CH. Our method decides the verification CHs by using
three input factors of an intermediate CH: a) energy remaining level, b) distance level, and c) the
number of detected false votes
Figure 4. The proposed method based fuzzy logic.
Error! Reference source not found. shows the phases of the verification CH selection by
applying the three input factors when they are selected in a path. The proposed method consider
that the verification CH is effectively selected through energy remaining level, distance level, and
the number of detected false votes. For example, CH1 is a verification node, and CH2 is a normal
CH due to unsuited conditions. A report, which is forwarded from CH0, is verified in CH1. CH2
then receives and directly transmit it to next hop. Thus, the proposed method effectively detects
the multiple attacks using the fuzzy rule-based system and saves needless energy of each sensor
when FRIA and FVIA simultaneously occur.
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
5
input factors of energy remaining level, distance level, and the number of detected false votes.
Therefore, our proposed method improves a security level and energy effectiveness compared to
PVFS. In this section, the proposed method is described in detail.
3.1. Assumptions
We assume the sensor nodes are fixed after they are deployed. The sensor network composes a
base station and a number of sensor nodes, e.g. the Berkeley MICAz motes [3], the initial
paths is established through directed diffusion [4], and minimum cost forwarding algorithms
[5]. We choose the cluster-based model Error! Reference source not found. to organize the
sensor nodes. In a cluster, one node is elected to be a CH.
It is further assumed that every node forwards reports into the base station along their path. A
compromised node generates false reports in a path. The generated false reports are forwarded
from a compromised node toward the base station before filtering it out.
3.2. Overview
Our proposed method selects verification CHs based the fuzzy rule-based system before
forwarding a report to next intermediate CH. Our method decides the verification CHs by using
three input factors of an intermediate CH: a) energy remaining level, b) distance level, and c) the
number of detected false votes
Figure 4. The proposed method based fuzzy logic.
Error! Reference source not found. shows the phases of the verification CH selection by
applying the three input factors when they are selected in a path. The proposed method consider
that the verification CH is effectively selected through energy remaining level, distance level, and
the number of detected false votes. For example, CH1 is a verification node, and CH2 is a normal
CH due to unsuited conditions. A report, which is forwarded from CH0, is verified in CH1. CH2
then receives and directly transmit it to next hop. Thus, the proposed method effectively detects
the multiple attacks using the fuzzy rule-based system and saves needless energy of each sensor
when FRIA and FVIA simultaneously occur.
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
5
input factors of energy remaining level, distance level, and the number of detected false votes.
Therefore, our proposed method improves a security level and energy effectiveness compared to
PVFS. In this section, the proposed method is described in detail.
3.1. Assumptions
We assume the sensor nodes are fixed after they are deployed. The sensor network composes a
base station and a number of sensor nodes, e.g. the Berkeley MICAz motes [3], the initial
paths is established through directed diffusion [4], and minimum cost forwarding algorithms
[5]. We choose the cluster-based model Error! Reference source not found. to organize the
sensor nodes. In a cluster, one node is elected to be a CH.
It is further assumed that every node forwards reports into the base station along their path. A
compromised node generates false reports in a path. The generated false reports are forwarded
from a compromised node toward the base station before filtering it out.
3.2. Overview
Our proposed method selects verification CHs based the fuzzy rule-based system before
forwarding a report to next intermediate CH. Our method decides the verification CHs by using
three input factors of an intermediate CH: a) energy remaining level, b) distance level, and c) the
number of detected false votes
Figure 4. The proposed method based fuzzy logic.
Error! Reference source not found. shows the phases of the verification CH selection by
applying the three input factors when they are selected in a path. The proposed method consider
that the verification CH is effectively selected through energy remaining level, distance level, and
the number of detected false votes. For example, CH1 is a verification node, and CH2 is a normal
CH due to unsuited conditions. A report, which is forwarded from CH0, is verified in CH1. CH2
then receives and directly transmit it to next hop. Thus, the proposed method effectively detects
the multiple attacks using the fuzzy rule-based system and saves needless energy of each sensor
when FRIA and FVIA simultaneously occur.
6. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
6
3.3. Proposed Method based Fuzzy Logic
3.3.1. Further Subsections
This section discusses the factors that are used for fuzzy inference.
• ERL (Energy Remaining Level) This value indicates how much energy remains in CHs. It is
important to present energy of each node in the sensor network. The value specifies energy
level of each CH from 1 to 100. If this value is close to 1, energy reaming level is low.
• DTL (Distance Level) This value indicates the hop count from a CH to the base station.
When a report is forwarded, it travels via the number of hops toward the base station. The
value specifies the distance level of each CH from 1 to 100. Before forwarding the report,
intermediate CHs calculate the level by 100×0HCHCi (HCi is hops of an intermediate CH,
HC0 is hops of CH0). If this value is high, the intermediate CH is close to CH0.
• NDV (Number of Detected False Votes) This value indicates the condition of the sensor
network security. If verification CHs detect many false votes occurred from compromised
nodes, the verification CHs maintain high security level in a path. The value specifies the
number of detected false votes from 1 to 50. If the value exceeds 50, the value keeps 50.
The three input factors ERL, DTL, and NDV decide a flag (FLG) for verification CHs through
the fuzzy rule-based system.
3.3.2. Fuzzy Membership Functions and Rules
Error! Reference source not found., Error! Reference source not found., Error! Reference
source not found. illustrate the membership functions of the fuzzy logic input parameters for
selecting verification CHs. We tune membership functions as the best rule with lots of
experiment. The input factors of the fuzzy variables are represented as:
• Energy Remaining Level = {Small (SM), Middle (MD), Large (LG)}
• Distance Level = {Very Near (VN), Near (NR), Middle (MD), Far (FR), Very Far (VF)}
• Number of Detected False Votes = {Low (LW), Middle (MD), High (HG)}
Error! Reference source not found. shows the membership function of the fuzzy logic output
parameters for effectively deciding verification CHs. The output factors of the fuzzy variables are
represented as:
• Flag = {Keep, Verification}
7. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
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Figure 5. ERL of Fuzzy Membership Function.
Figure 6. NDV of Fuzzy Membership Function.
Figure 7. NDV of Fuzzy Membership Function.
8. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
8
Figure 8. FLG of Fuzzy Membership Function.
We defined 45 (=3×5×3) fuzzy rules as shown in Error! Reference source not found..
Table 1. Fuzzy if-then rules.
Rule
No.
Input (if)
Output
(then)
ERL DTL NDV FLG
0 SM VN LW KP
14 SM VF HG VF
23 MD MD HG VF
30 LG VN LW PS
44 LG VF HG VF
Error! Reference source not found. shows representative rules as frequently occurred in the
fuzzy membership function. For instance, if ERL is SM, DTL is VN, and NDV is LW, then it
keeps a normal CH to conserve energy resource (Rule 0). If ERL is SM, DTL VF, and NDV is
HG, then it is recommended to have a verification node to maintain high detection power for
detecting false votes in a report (Rule 14). If ERL is MD, DTL is MD, and NDV is HG, then it is
also recommend detecting false votes in a verification CH (Rule 23). If ERL is LG, DTL is VN,
and NDV is LW, then a normal CH transmits a report to next CH without verification (Rule 30).
If ERL is LG, DTL is VF, and NDV is HG, then a normal CH is recommend for detecting false
votes, maintains the high security level (Rule 44). Therefore, it is important to effectively select
verification CHs appropriately before forwarding a report due to energy saving.
4. EXPERIMENTAL RESULTS
An experiment was performed for the proposed method and compared to PVFS. There are 500
total sensor nodes in the sensor network of the simulation environment. The simulation
environment, which is 1,000×1,000 m2, is composed of 50 clusters and 10 nodes in a cluster. We
compromised 20 nodes in 11 hops of the sensor network. The compromised nodes inject false
9. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
9
reports to consume unnecessary energy and false votes to drop legitimate reports in intermediate
nodes. The size of a report is 24 bytes, and the size of a vote is 1 byte. Each node uses 16.25 µJ to
transmit per byte, 12.5 µJ to receive per byte, and 15 µJ to generate a vote per byte [2]. We
randomly generate 300 events in clusters, and the ratio of false reports is 20% of the events.
Figure 9. Energy consumptions per hop.
Error! Reference source not found. shows the energy consumptions of CHs as FRIA and FVIA
simultaneously occur in the sensor network. These attacks are generated in hops 11 of
compromised nodes. In the proposed method, the energy consumption between hops 8 and 10 is
lower than PVFS. That is, our method improves energy savings because it selects verification
CHs based the fuzzy rule-based system more than PVFS for selecting verification nodes by using
the probability. Therefore, our proposal saves the energy resources of each node to effectively
choose the verification CHs more than PVFS.
Figure 10. Number of filtered false votes per hop.
Error! Reference source not found. shows the number of detected false votes her hop for FVIA.
Both of PVFS and the proposed method detects the false votes in verification CHs close to
compromised nodes (hops 11). Our method prevents the most of the false votes in legitimate
reports by up about 72% between hops 7 and 10. PVFS detects the false votes in verification CHs
between hops 2 and 6 by using the probability selection. Thus, the proposed method maintains the
security level of FVIA through the number of detected the false votes as compared to PVFS.
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
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reports to consume unnecessary energy and false votes to drop legitimate reports in intermediate
nodes. The size of a report is 24 bytes, and the size of a vote is 1 byte. Each node uses 16.25 µJ to
transmit per byte, 12.5 µJ to receive per byte, and 15 µJ to generate a vote per byte [2]. We
randomly generate 300 events in clusters, and the ratio of false reports is 20% of the events.
Figure 9. Energy consumptions per hop.
Error! Reference source not found. shows the energy consumptions of CHs as FRIA and FVIA
simultaneously occur in the sensor network. These attacks are generated in hops 11 of
compromised nodes. In the proposed method, the energy consumption between hops 8 and 10 is
lower than PVFS. That is, our method improves energy savings because it selects verification
CHs based the fuzzy rule-based system more than PVFS for selecting verification nodes by using
the probability. Therefore, our proposal saves the energy resources of each node to effectively
choose the verification CHs more than PVFS.
Figure 10. Number of filtered false votes per hop.
Error! Reference source not found. shows the number of detected false votes her hop for FVIA.
Both of PVFS and the proposed method detects the false votes in verification CHs close to
compromised nodes (hops 11). Our method prevents the most of the false votes in legitimate
reports by up about 72% between hops 7 and 10. PVFS detects the false votes in verification CHs
between hops 2 and 6 by using the probability selection. Thus, the proposed method maintains the
security level of FVIA through the number of detected the false votes as compared to PVFS.
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
9
reports to consume unnecessary energy and false votes to drop legitimate reports in intermediate
nodes. The size of a report is 24 bytes, and the size of a vote is 1 byte. Each node uses 16.25 µJ to
transmit per byte, 12.5 µJ to receive per byte, and 15 µJ to generate a vote per byte [2]. We
randomly generate 300 events in clusters, and the ratio of false reports is 20% of the events.
Figure 9. Energy consumptions per hop.
Error! Reference source not found. shows the energy consumptions of CHs as FRIA and FVIA
simultaneously occur in the sensor network. These attacks are generated in hops 11 of
compromised nodes. In the proposed method, the energy consumption between hops 8 and 10 is
lower than PVFS. That is, our method improves energy savings because it selects verification
CHs based the fuzzy rule-based system more than PVFS for selecting verification nodes by using
the probability. Therefore, our proposal saves the energy resources of each node to effectively
choose the verification CHs more than PVFS.
Figure 10. Number of filtered false votes per hop.
Error! Reference source not found. shows the number of detected false votes her hop for FVIA.
Both of PVFS and the proposed method detects the false votes in verification CHs close to
compromised nodes (hops 11). Our method prevents the most of the false votes in legitimate
reports by up about 72% between hops 7 and 10. PVFS detects the false votes in verification CHs
between hops 2 and 6 by using the probability selection. Thus, the proposed method maintains the
security level of FVIA through the number of detected the false votes as compared to PVFS.
10. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
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Figure 11. Number of filtered false reports per hop.
Error! Reference source not found. shows the number of detected false reports for FRIA. The
proposed method improves the detection power of FRIA to effectively select the verification CHs
through the fuzzy rule-based system. Our method improves the detection of the false report by up
about 70% between hops 5 and 9 more than PVFS, influences the energy consumption of
intermediate CHs as shown Error! Reference source not found.. Therefore, the proposed
method improves the security level of FRIA and decreased the energy consumption more than
PVFS.
Figure 12. Average energy consumption per event.
Error! Reference source not found. shows the average energy consumption of intermediate
CHs per events 50. The energy consumption in PVFS and the proposed method is approximated
as 50 events occur in the sensor network. When 150 events are generated, the energy
consumption of the proposed method saves about 292µm more than PVFS. That is, a gap for
energy consumption is produced due to mutual security countermeasure against the multiple
attacks, such as FVIA and FRIA. Therefore, our proposed method improves the energy
consumption by up about 8% as compared to PVFS, we expect to prolong the lifetime of the
whole sensor network.
5. CONCLUSIONS
In WSN, an adversary easily compromises sensor nodes and simultaneously generates the
multiple attacks, such as FVIA and FRIA. These attacks block an inflow of legitimate reports and
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
10
Figure 11. Number of filtered false reports per hop.
Error! Reference source not found. shows the number of detected false reports for FRIA. The
proposed method improves the detection power of FRIA to effectively select the verification CHs
through the fuzzy rule-based system. Our method improves the detection of the false report by up
about 70% between hops 5 and 9 more than PVFS, influences the energy consumption of
intermediate CHs as shown Error! Reference source not found.. Therefore, the proposed
method improves the security level of FRIA and decreased the energy consumption more than
PVFS.
Figure 12. Average energy consumption per event.
Error! Reference source not found. shows the average energy consumption of intermediate
CHs per events 50. The energy consumption in PVFS and the proposed method is approximated
as 50 events occur in the sensor network. When 150 events are generated, the energy
consumption of the proposed method saves about 292µm more than PVFS. That is, a gap for
energy consumption is produced due to mutual security countermeasure against the multiple
attacks, such as FVIA and FRIA. Therefore, our proposed method improves the energy
consumption by up about 8% as compared to PVFS, we expect to prolong the lifetime of the
whole sensor network.
5. CONCLUSIONS
In WSN, an adversary easily compromises sensor nodes and simultaneously generates the
multiple attacks, such as FVIA and FRIA. These attacks block an inflow of legitimate reports and
International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
10
Figure 11. Number of filtered false reports per hop.
Error! Reference source not found. shows the number of detected false reports for FRIA. The
proposed method improves the detection power of FRIA to effectively select the verification CHs
through the fuzzy rule-based system. Our method improves the detection of the false report by up
about 70% between hops 5 and 9 more than PVFS, influences the energy consumption of
intermediate CHs as shown Error! Reference source not found.. Therefore, the proposed
method improves the security level of FRIA and decreased the energy consumption more than
PVFS.
Figure 12. Average energy consumption per event.
Error! Reference source not found. shows the average energy consumption of intermediate
CHs per events 50. The energy consumption in PVFS and the proposed method is approximated
as 50 events occur in the sensor network. When 150 events are generated, the energy
consumption of the proposed method saves about 292µm more than PVFS. That is, a gap for
energy consumption is produced due to mutual security countermeasure against the multiple
attacks, such as FVIA and FRIA. Therefore, our proposed method improves the energy
consumption by up about 8% as compared to PVFS, we expect to prolong the lifetime of the
whole sensor network.
5. CONCLUSIONS
In WSN, an adversary easily compromises sensor nodes and simultaneously generates the
multiple attacks, such as FVIA and FRIA. These attacks block an inflow of legitimate reports and
11. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
11
threaten the lifetime of the sensors. To detect these attacks, PVFS was proposed to select
verification CH with a probability and to prevent false votes. In this paper, the proposed method
decides effective verification CHs according to conditions of the CHs based a fuzzy rule-based
system. Our proposed method improves the detection power of FRIA while maintaining the
security level of FVIA as compared to PVFS, and saves the energy resources of each node by up
about 8%. Therefore, our proposal effectively selects the verification CHs by using the fuzzy
rule-based system as compared to PVFS, and improves energy consumption of each node and the
security level of FRIA while maintaining the detection power of FVIA. In addition, we will apply
various attacks of the sensor network to discuss further optimal solutions.
6. ACKNOWLEDGMENTS
This work was supported by National Research Foundation of Korea Grant funded by the Korean
Government (No. 2012-0002475).
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Authors
Su Man Nam received his B.S. degrees in computer information from Hanseo
university, Korea, in February 2009 and M.S degrees in in Electrical and Computer
Engineering from Sungkyunkwan University in 2013, respectively. He is currently a
doctoral student in the College of Information and Communication Engineering at
Sungkyunkwan University, Korea. His research interests include wireless sensor
network, security in wireless sensor networks, and modelling & simulation.
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Tae Ho Cho received the Ph.D. degree in Electrical and Computer Engineering from
the University of Arizona, USA, in 1993, and the B.S. and M.S. degrees in Electrical
Engineering from Sungkyunkwan University, Republic of Korea, and the University of
Alabama, USA, respectively. He is currently a Professor in the College of Information
and Communication Engineering, Sungkyunkwan University, Korea. His research
interests are in the areas of wireless sensor network, intelligent systems, modeling &
simulation, and enterprise resource planning.