Mobile Ad-hoc Network (MANET) is a distributed, decentralized network of wireless portable nodes connecting directly without any fixed communication base station or centralized administration. Nodes in MANET move continuously in random directions and follow an arbitrary manner, which presents numerous challenges to these networks and make them more susceptible to different security threats. Due to this decentralized nature of their overall architecture, combined with the limitation of hardware resources, those infrastructure-less networks are more susceptible to different security attacks such as black hole attack, network partition, node selfishness, and Denial of Service (DoS) attacks. This work aims to present, investigate, and design an intrusion detection predictive technique for Mobile Ad hoc networks using deep learning artificial neural networks (ANNs). A simulation-based evaluation and a deep ANNs modelling for detecting and isolating a Denial of Service (DoS) attack are presented to improve the overall security level of Mobile ad hoc networks.
DYNAMIC NEURAL NETWORKS IN THE DETECTION OF DISTRIBUTED ATTACKS IN MOBILE AD-...IJNSA Journal
This paper describes the latest results of a research program that is designed to enhance the security of wireless mobile ad hoc networks (MANET) by developing a distributed intrusion detection capability. The current approach uses learning vector quantization neural networks that have the ability to identify patterns of network attacks in a distributed manner. This capability enables this approach to demonstrate a distributed analysis functionality that facilitates the detection of complex attacks against MANETs. The results of the evaluation of the approach and a discussion of additional areas of research is presented.
IMPACT ANALYSIS OF BLACK HOLE ATTACKS ON MOBILE AD HOC NETWORKS PERFORMANCEijgca
A Mobile Ad hoc Network (MANET) is a collection of mobile stations with wireless interfaces which form a temporary network without using any central administration. MANETs are more vulnerable to attacks because
they have some specific characteristics as complexity of wireless communication and lack of infrastructure. Hence security is an important requirement in mobile ad hoc networks. One of the attacks against network integrity
in MANETs is the Black Hole Attack. In this type of attack all data packets are absorbed by malicious node, hence data loss occurs. In this paper we investigated the impacts of Black Hole attacks on the network
performance. We have simulated black hole attacks using Network Simulator 2 (NS-2) and have measured the packet loss in the network without and with a black hole attacks. Also, we measured the packet loss when the
number of black hole attacks increases.
IMPACT ANALYSIS OF BLACK HOLE ATTACKS ON MOBILE AD HOC NETWORKS PERFORMANCEijgca
A Mobile Ad hoc Network (MANET) is a collection of mobile stations with wireless interfaces which form a temporary network without using any central administration. MANETs are more vulnerable to attacks because they have some specific characteristics as complexity of wireless communication and lack of infrastructure. Hence security is an important requirement in mobile ad hoc networks. One of the attacks against network integrity in MANETs is the Black Hole Attack. In this type of attack all data packets are absorbed by malicious node, hence data loss occurs. In this paper we investigated the impacts of Black Hole attacks on the network performance. We have simulated black hole attacks using Network Simulator 2 (NS-2) and have measured the packet loss in the network without and with a black hole attacks. Also, we measured the packet loss when the number of black hole attacks increases.
In our research work we are improving the performance of mobile ad hoc networks under jamming attack by using an integrated approach. The proposed work includes a network with high mobility, using IEEE Along g standard jamming attacks and countermeasures in wireless sensor networks
HANDLING CROSS-LAYER ATTACKS USING NEIGHBORS MONITORING SCHEME AND SWARM INTE...Editor IJCATR
The standard MAC protocol widely used for Mobile Adhoc Networks (MANETs) is IEEE 802.11.
When attacks in MAC layer are left as such without paying attention, it could possibly disturb channel access and
consequently may cause wastage of resources in terms of bandwidth and power. In this paper, a swarm based detection
and defense technique is proposed for routing and MAC layer attacks in MANET. Using forward and backward ants,
the technique obtains mean value of nodes between the first received RREQ and RREP packets. Based on this
estimation, the source node decides the node as valid or malicious. Moreover the MAC layer parameters namely
number of neighbors identified by the MAC layer, number of neighbors identified by the routing layer, the number of
recent MAC receptions and the number of recent routing protocol receptions are used to determine the node state. The
source node uses these two node state estimation techniques to construct the reliable path to the destination. This
proposed technique improves the network performance and at the same time prevents attackers intelligently.
MANETs have unique characteristics like dynamic topology, wireless radio medium, limited resources and lack of centralized administration; as a result, they are vulnerable to different types of attacks in different layers of protocol stack. wormhole attack detection in wireless sensor networks
DYNAMIC NEURAL NETWORKS IN THE DETECTION OF DISTRIBUTED ATTACKS IN MOBILE AD-...IJNSA Journal
This paper describes the latest results of a research program that is designed to enhance the security of wireless mobile ad hoc networks (MANET) by developing a distributed intrusion detection capability. The current approach uses learning vector quantization neural networks that have the ability to identify patterns of network attacks in a distributed manner. This capability enables this approach to demonstrate a distributed analysis functionality that facilitates the detection of complex attacks against MANETs. The results of the evaluation of the approach and a discussion of additional areas of research is presented.
IMPACT ANALYSIS OF BLACK HOLE ATTACKS ON MOBILE AD HOC NETWORKS PERFORMANCEijgca
A Mobile Ad hoc Network (MANET) is a collection of mobile stations with wireless interfaces which form a temporary network without using any central administration. MANETs are more vulnerable to attacks because
they have some specific characteristics as complexity of wireless communication and lack of infrastructure. Hence security is an important requirement in mobile ad hoc networks. One of the attacks against network integrity
in MANETs is the Black Hole Attack. In this type of attack all data packets are absorbed by malicious node, hence data loss occurs. In this paper we investigated the impacts of Black Hole attacks on the network
performance. We have simulated black hole attacks using Network Simulator 2 (NS-2) and have measured the packet loss in the network without and with a black hole attacks. Also, we measured the packet loss when the
number of black hole attacks increases.
IMPACT ANALYSIS OF BLACK HOLE ATTACKS ON MOBILE AD HOC NETWORKS PERFORMANCEijgca
A Mobile Ad hoc Network (MANET) is a collection of mobile stations with wireless interfaces which form a temporary network without using any central administration. MANETs are more vulnerable to attacks because they have some specific characteristics as complexity of wireless communication and lack of infrastructure. Hence security is an important requirement in mobile ad hoc networks. One of the attacks against network integrity in MANETs is the Black Hole Attack. In this type of attack all data packets are absorbed by malicious node, hence data loss occurs. In this paper we investigated the impacts of Black Hole attacks on the network performance. We have simulated black hole attacks using Network Simulator 2 (NS-2) and have measured the packet loss in the network without and with a black hole attacks. Also, we measured the packet loss when the number of black hole attacks increases.
In our research work we are improving the performance of mobile ad hoc networks under jamming attack by using an integrated approach. The proposed work includes a network with high mobility, using IEEE Along g standard jamming attacks and countermeasures in wireless sensor networks
HANDLING CROSS-LAYER ATTACKS USING NEIGHBORS MONITORING SCHEME AND SWARM INTE...Editor IJCATR
The standard MAC protocol widely used for Mobile Adhoc Networks (MANETs) is IEEE 802.11.
When attacks in MAC layer are left as such without paying attention, it could possibly disturb channel access and
consequently may cause wastage of resources in terms of bandwidth and power. In this paper, a swarm based detection
and defense technique is proposed for routing and MAC layer attacks in MANET. Using forward and backward ants,
the technique obtains mean value of nodes between the first received RREQ and RREP packets. Based on this
estimation, the source node decides the node as valid or malicious. Moreover the MAC layer parameters namely
number of neighbors identified by the MAC layer, number of neighbors identified by the routing layer, the number of
recent MAC receptions and the number of recent routing protocol receptions are used to determine the node state. The
source node uses these two node state estimation techniques to construct the reliable path to the destination. This
proposed technique improves the network performance and at the same time prevents attackers intelligently.
MANETs have unique characteristics like dynamic topology, wireless radio medium, limited resources and lack of centralized administration; as a result, they are vulnerable to different types of attacks in different layers of protocol stack. wormhole attack detection in wireless sensor networks
MANETs (Mobile Ad hoc Network) is a self-governing system in which different mobile nodes are connected by wireless links. MANETs comprise of mobile nodes that are independent for moving in and out over the network. Nodes are the devices or systems that is laptops, mobile phone etc. those are participating in the network. These nodes can operate as router/host or both simultaneously. These nodes can form uninformed topologies as per their connectivity among nodes over the network. Security in MANETs is the prime anxiety for the fundamental working of network. MANETs frequently will be ill with security threats because of it having features like altering its topology dynamically, open medium, lack of central management & monitoring, cooperative algorithms and no apparent security mechanism. These factors draw an attention for the MANETs against the security intimidation. In this paper we have studied about security attack in MANET and its consequences, proposed technique for black hole detection is hybrid in nature which combines the benefit of proactive and reactive protocol and proposed technique is compared with AODV.
Mobile Adhoc Network (MANET) is a self-configuring and infrastructure-less network which consists of mobile devices such as mobiles, laptops, PDA's etc. Because of its lack of infrastructure, wireless mobile communication, dynamic topology, MANET is vulnerable to various security attacks. This survey paper presents an overview of developments of voting and non-voting based certificate revocation mechanisms in past few years. Certificate revocation is an important method used to secure the MANET. Certificate revocation isolates the attacker nodes from participating in network activities by revoking its certificate. Over last few years different schemes are explored for certificate revocation. In concluding section we present the limitations of the current cluster based certificate revocation scheme.
An Enhanced Approach to Avoid Black hole Attack in Mobile Ad hoc Networks usi...ijsrd.com
A mobile ad-hoc network (MANET) is very receptive to security attacks due to its open medium, dynamically changing network topology, lack of centralized monitoring. These vulnerabilities are nature of MANET structure that cannot be removed. As a consequence, attacks with malicious intent have been and will be devised to exploit these vulnerabilities and to cripple MANET operations. One of the well known attack on the MANET is the Black Hole attack which is most common in the ondemand routing protocols such as AODV. A black hole attack refers to an attack by a malicious node, which forcibly gains the route from a source to a destination by the falsification of sequence number and hop count of the routing message. This paper represents an enhanced AOMDV routing protocol for avoiding black hole attack in MANET. This routing protocol uses Ad hoc On-demand Multipath Distance Vector (AOMDV) to form link disjoint multi-path during path discovery to provide better path selection in order to avoid malicious nodes in the path using legitimacy table maintained by each node in the network. Nonmalicious nodes steadily isolate the black hole nodes based on the values collected in their legitimacy table and avoid them while making path between source and destination. The effectiveness of our approach is illustrated by simulations conducted using network simulator ns-2.34.
A Protocol/Scheme to mitigate DDos attacks using AODV Protocolijsrd.com
MANET(Mobile Adhoc Network) is an emerging technology and have great strength to be applied in battlefields and commercial applications such as traffic surveillance, MANET is infrastructure less without any centralized controller. Each node contains routing capability. Each device in a MANET is independent and can move in any direction. One of the major challenges wireless mobile ad-hoc networks face today is security, because no central controller exists. MANETs are a kind of wireless ad hoc networks that usually has a routable networking environment on top of a link layer ad hoc network. There are many security attacks in MANET and DDoS (Distributed denial of service) is one of them. Our main objective is seeing the effect of DDoS in routing, Packet Drop Rate, End to End Delay, no. of Collisions due to attack on network. And with these parameters and many more also we build secure IDS to detect this kind of attack and block it. In this thesis main objective is to study and implement the security against the DDOS attack. DDoS (Distributed Denial of Service) attacks in the networks are required to be prevented, as early as possible before reaching the victim node. DDos attack causes depletion of the network resources such as network bandwidth, disk space, CPU time, data structures, and network connections. Dealing with DDoS attacks is difficult due to their properties such as dynamic attack rates, big scale of botnets. DDos attack become more difficult to handle if it occurs in wireless network because of the properties of ad hoc network such as dynamic topologies, low battery life, Unicast routing Multicast routing , Frequency of updates or network overhead , scalability , mobile agent based routing ,power aware routing etc. Thus it is better to prevent the distributed denial of service attack rather than allowing it to occur and then taking the necessary steps to handle it. The following quantitative metrics Packet Delivery Ratio (PDR), Number of Collisions are to be used to evaluate the performance of DDoS attacks and their prevention techniques under different combinations in the fixed mobile ad hoc network. In our simulation, the effect of DDoS attacks under different number of attackers is studied.
The International Journal of Engineering and Science (The IJES)theijes
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
ANALYZING THE IMPACT OF EAVES ON ENERGY CONSUMPTION OF AODV ROUTING PROTOCOL ...ijwmn
In this dynamic world, communication is a sine qua non for development. Communication represents
sharing of information which can be local or remote. Though local communications may occur face to face
between individuals remote communications take place among people over long distances. Mobile ad hoc
networks (MANETs) are becoming an interesting part of research due to the increasing growth of wireless
devices (laptops, tablets, mobiles etc.) and as well as wireless internet facilities like 4G/Wi-Fi. A MANET
is any infrastructure-less network formed by independent and self-configuring nodes. Each node acts as
router. In order to send data, the source node initiates a routing process by using a routing protocol. The
nature of the wireless medium is always insecure. So, during routing many attacks can take place. The
main objective of an eavesdropper is to grab the confidential information in the network. This secret
information is used by a malicious node to perform further attacks. Here, the entire problem lies in
identifying the eavesdropper because the eavesdropper acts a normal node in the network. In this paper,
we analyzed the impact of eavesdropper while executing an Ad hoc On Demand routing (AODV) protocol
in MANETs. All the simulations are done using QualNet 5.1 network simulator. From the results, it is found
that the network performance degrades in presence of an eavesdropper.
Analyzing the Impact of Eaves on Energy Consumption of AODV Routing Protocol ...ijwmn
In this dynamic world, communication is a sine qua non for development. Communication represents sharing of information which can be local or remote. Though local communications may occur face to face between individuals remote communications take place among people over long distances. Mobile ad hoc networks (MANETs) are becoming an interesting part of research due to the increasing growth of wireless devices (laptops, tablets, mobiles etc.) and as well as wireless internet facilities like 4G/Wi-Fi. A MANET is any infrastructure-less network formed by independent and self-configuring nodes. Each node acts as router. In order to send data, the source node initiates a routing process by using a routing protocol. The nature of the wireless medium is always insecure. So, during routing many attacks can take place. The main objective of an eavesdropper is to grab the confidential information in the network. This secret information is used by a malicious node to perform further attacks. Here, the entire problem lies in identifying the eavesdropper because the eavesdropper acts a normal node in the network. In this paper, we analyzed the impact of eavesdropper while executing an Ad hoc On Demand routing (AODV) protocol in MANETs. All the simulations are done using QualNet 5.1 network simulator. From the results, it is found that the network performance degrades in presence of an eavesdropper.
Requisite Trust Based Routing Protocol for WSNAM Publications
A mobile ad-hoc network (MANET) is an infrastructure less network of mobile devices connected by wireless
links. To secure a MANET in colluding nodes environment, the proposed work aims to detect and defend colluding nodes that
causes internal attacks. In order to achieve this, the work focuses on the novel algorithm of trust computation and route
detection that detects colluding nodes, without message and route redundancy during route discovery by using Requisite Trust
based Secure Routing Protocol (RTSR). The trust will be calculated in local forwarding nodes, which are used to discover the
route. The trust values from one hop neighbors are used to calculate the single trust value for each node using the constant
normalization concept. Route discovery and trust information will be stored in fixed cluster head (CH).
Mitigation of Colluding Selective Forwarding Attack in WMNs using FADEIJTET Journal
ABSTRACT - Wireless Mesh Networks (WMNs) have emerged as a promising technology because of their wide range of
applications. Wireless mesh networks wireless mesh networks (WMNs) are dynamically self – organizing, self –
configuring, self – healing with nodes in the network automatically establishing an adHoc network and maintaining mesh
connectivity. Because of their fast connectivity wireless mesh networks (WMNs) is widely used in military applications.
Security is the major constrain in wireless mesh networks (WMNs). This paper considers a special type of DoS attack
called selective forwarding attack or greyhole attack. With such an attack, a misbehaving mesh router just forwards few
packets it receives but drops sensitive data packets. To mitigate the effect of such attack an approach called FADE :
Forward Assessment based Detection is adopted. FADE scheme detects the presence of attack inside the network by
means of two-hop acknowledgment based monitoring and forward assessment based detection. FADE operates in three
phases and analyzed by determining optimal threshold values. This approach is found to provide effective defense against
the collaborative internal attackers in WMNs.
Black hole Attack Avoidance Protocol for wireless Ad-Hoc networksijsrd.com
A Mobile Ad-Hoc Network is a collection of mobile nodes or a temporary network set up by wireless mobile nodes moving arbitrary in the places that have no network infrastructure in such a manner that the interconnections between nodes are capable of changing on continual basis. Thus the nodes find a path to the destination node using routing protocols. However, due to security vulnerabilities of the routing protocols, wireless ad-hoc networks are unprotected to attacks of the malicious nodes. Various attacks and one of those attacks is the Black Hole Attack against network integrity absorbing all data packets in the network. Since the data packets do not reach the destination node on account of this attack, data loss will occur. Therefore, it is a severe attack that can be easily employed against routing in mobile ad hoc networks. There are lots of detection and defense mechanisms to eliminate the intruder that carry out the black hole attack. . Virtual Infrastructure achieves reliable transmission in Mobile Ad Hoc Network. Black Hole Attack is the major problem to affect the Virtual Infrastructure. In this paper, approach on analyzing and improving the security of AODV, which is one of the popular routing protocols for MANET. Our aim is to ensuring the avoidance against Black hole attack.
Secured Intrusion Protection System through EAACK in MANETSijtsrd
Achieving reliable routing has always been a major issue in the design of communication networks, due to the absence of fixed infrastructure among which mobile ad hoc networks MANETs that can take control of the most adversarial networking environment, and the dynamic network topology the nature of open transmission media. In the MANETs these characteristics also more challenging to make the design of routing protocols. The network topology varies so to determining feasible routing paths for distributing messages in a decentralized is a difficult job. Factors such as the extensive distribution of nodes and open medium, variable wireless link quality topological changes, and propagation path loss become pertinent issues and make MANET unprotected to instructions. Thus, it becomes central to develop a systematic intrusion detection scheme to secure Mobile Ad Hoc networks from intruders. In this project, we put forward and applied an efficient IDS mechanism based on Enhanced Adaptive Acknowledgment EAACK especially made for MANETs which performs better than the earlier techniques such as AACK, TWOACK and Watchdog. Mr. Ravishankar Kandasamy | M. Ajith Kumar | M. Ajith Kumar | G. Arun Kumar "Secured Intrusion Protection System through EAACK in MANETS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30457.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30457/secured-intrusion-protection-system-through-eaack-in-manets/mr-ravishankar-kandasamy
Analyzing the Impact of Blackhole Attacks on AODV and DSR Routing Protocols’ ...IJCSEA Journal
Mobile Ad-Hoc Networks (MANETs) are wireless networks characterized by their lack of a fixed infrastructure, allowing nodes to move freely and serve as both routers and hosts. These nodes establish virtual links and utilize routing protocols such as AODV, DSR, and DSDV to establish connections. However, security is a significant concern, with the Blackhole attack posing a notable threat, wherein a malicious node drops packets instead of forwarding them. To investigate the impact of Blackhole nodes and assess the performance of AODV and DSR protocols, the researchers employed the NS-2.35 ns-allinone2.35 version for simulation purposes. The study focused on several metrics, including average throughput, acket delivery ratio, and residual energy. The findings revealed that AODV demonstrated better energy efficiency and packet delivery compared to DSR, but DSR outperformed AODV in terms of throughput. Additionally, environmental factors and data sizes were taken into account during the analysis.
ANALYZING THE IMPACT OF BLACKHOLE ATTACKS ON AODV AND DSR ROUTING PROTOCOLS’ ...IJCSEA Journal
Mobile Ad-Hoc Networks (MANETs) are wireless networks characterized by their lack of a fixed
infrastructure, allowing nodes to move freely and serve as both routers and hosts. These nodes establish
virtual links and utilize routing protocols such as AODV, DSR, and DSDV to establish connections.
However, security is a significant concern, with the Blackhole attack posing a notable threat, wherein a
malicious node drops packets instead of forwarding them. To investigate the impact of Blackhole nodes and
assess the performance of AODV and DSR protocols, the researchers employed the NS-2.35 ns-allinone2.35 version for simulation purposes. The study focused on several metrics, including average throughput,
packet delivery ratio, and residual energy. The findings revealed that AODV demonstrated better energy
efficiency and packet delivery compared to DSR, but DSR outperformed AODV in terms of throughput.
Additionally, environmental factors and data sizes were taken into account during the analysis.
ANALYZING THE IMPACT OF BLACKHOLE ATTACKS ON AODV AND DSR ROUTING PROTOCOLS’ ...IJCSEA Journal
Mobile Ad-Hoc Networks (MANETs) are wireless networks characterized by their lack of a fixed
infrastructure, allowing nodes to move freely and serve as both routers and hosts. These nodes establish
virtual links and utilize routing protocols such as AODV, DSR, and DSDV to establish connections.
However, security is a significant concern, with the Blackhole attack posing a notable threat, wherein a
malicious node drops packets instead of forwarding them. To investigate the impact of Blackhole nodes and
assess the performance of AODV and DSR protocols, the researchers employed the NS-2.35 ns-allinone2.35 version for simulation purposes. The study focused on several metrics, including average throughput,
packet delivery ratio, and residual energy. The findings revealed that AODV demonstrated better energy
efficiency and packet delivery compared to DSR, but DSR outperformed AODV in terms of throughput.
Additionally, environmental factors and data sizes were taken into account during the analysis.
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
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MANETs (Mobile Ad hoc Network) is a self-governing system in which different mobile nodes are connected by wireless links. MANETs comprise of mobile nodes that are independent for moving in and out over the network. Nodes are the devices or systems that is laptops, mobile phone etc. those are participating in the network. These nodes can operate as router/host or both simultaneously. These nodes can form uninformed topologies as per their connectivity among nodes over the network. Security in MANETs is the prime anxiety for the fundamental working of network. MANETs frequently will be ill with security threats because of it having features like altering its topology dynamically, open medium, lack of central management & monitoring, cooperative algorithms and no apparent security mechanism. These factors draw an attention for the MANETs against the security intimidation. In this paper we have studied about security attack in MANET and its consequences, proposed technique for black hole detection is hybrid in nature which combines the benefit of proactive and reactive protocol and proposed technique is compared with AODV.
Mobile Adhoc Network (MANET) is a self-configuring and infrastructure-less network which consists of mobile devices such as mobiles, laptops, PDA's etc. Because of its lack of infrastructure, wireless mobile communication, dynamic topology, MANET is vulnerable to various security attacks. This survey paper presents an overview of developments of voting and non-voting based certificate revocation mechanisms in past few years. Certificate revocation is an important method used to secure the MANET. Certificate revocation isolates the attacker nodes from participating in network activities by revoking its certificate. Over last few years different schemes are explored for certificate revocation. In concluding section we present the limitations of the current cluster based certificate revocation scheme.
An Enhanced Approach to Avoid Black hole Attack in Mobile Ad hoc Networks usi...ijsrd.com
A mobile ad-hoc network (MANET) is very receptive to security attacks due to its open medium, dynamically changing network topology, lack of centralized monitoring. These vulnerabilities are nature of MANET structure that cannot be removed. As a consequence, attacks with malicious intent have been and will be devised to exploit these vulnerabilities and to cripple MANET operations. One of the well known attack on the MANET is the Black Hole attack which is most common in the ondemand routing protocols such as AODV. A black hole attack refers to an attack by a malicious node, which forcibly gains the route from a source to a destination by the falsification of sequence number and hop count of the routing message. This paper represents an enhanced AOMDV routing protocol for avoiding black hole attack in MANET. This routing protocol uses Ad hoc On-demand Multipath Distance Vector (AOMDV) to form link disjoint multi-path during path discovery to provide better path selection in order to avoid malicious nodes in the path using legitimacy table maintained by each node in the network. Nonmalicious nodes steadily isolate the black hole nodes based on the values collected in their legitimacy table and avoid them while making path between source and destination. The effectiveness of our approach is illustrated by simulations conducted using network simulator ns-2.34.
A Protocol/Scheme to mitigate DDos attacks using AODV Protocolijsrd.com
MANET(Mobile Adhoc Network) is an emerging technology and have great strength to be applied in battlefields and commercial applications such as traffic surveillance, MANET is infrastructure less without any centralized controller. Each node contains routing capability. Each device in a MANET is independent and can move in any direction. One of the major challenges wireless mobile ad-hoc networks face today is security, because no central controller exists. MANETs are a kind of wireless ad hoc networks that usually has a routable networking environment on top of a link layer ad hoc network. There are many security attacks in MANET and DDoS (Distributed denial of service) is one of them. Our main objective is seeing the effect of DDoS in routing, Packet Drop Rate, End to End Delay, no. of Collisions due to attack on network. And with these parameters and many more also we build secure IDS to detect this kind of attack and block it. In this thesis main objective is to study and implement the security against the DDOS attack. DDoS (Distributed Denial of Service) attacks in the networks are required to be prevented, as early as possible before reaching the victim node. DDos attack causes depletion of the network resources such as network bandwidth, disk space, CPU time, data structures, and network connections. Dealing with DDoS attacks is difficult due to their properties such as dynamic attack rates, big scale of botnets. DDos attack become more difficult to handle if it occurs in wireless network because of the properties of ad hoc network such as dynamic topologies, low battery life, Unicast routing Multicast routing , Frequency of updates or network overhead , scalability , mobile agent based routing ,power aware routing etc. Thus it is better to prevent the distributed denial of service attack rather than allowing it to occur and then taking the necessary steps to handle it. The following quantitative metrics Packet Delivery Ratio (PDR), Number of Collisions are to be used to evaluate the performance of DDoS attacks and their prevention techniques under different combinations in the fixed mobile ad hoc network. In our simulation, the effect of DDoS attacks under different number of attackers is studied.
The International Journal of Engineering and Science (The IJES)theijes
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
ANALYZING THE IMPACT OF EAVES ON ENERGY CONSUMPTION OF AODV ROUTING PROTOCOL ...ijwmn
In this dynamic world, communication is a sine qua non for development. Communication represents
sharing of information which can be local or remote. Though local communications may occur face to face
between individuals remote communications take place among people over long distances. Mobile ad hoc
networks (MANETs) are becoming an interesting part of research due to the increasing growth of wireless
devices (laptops, tablets, mobiles etc.) and as well as wireless internet facilities like 4G/Wi-Fi. A MANET
is any infrastructure-less network formed by independent and self-configuring nodes. Each node acts as
router. In order to send data, the source node initiates a routing process by using a routing protocol. The
nature of the wireless medium is always insecure. So, during routing many attacks can take place. The
main objective of an eavesdropper is to grab the confidential information in the network. This secret
information is used by a malicious node to perform further attacks. Here, the entire problem lies in
identifying the eavesdropper because the eavesdropper acts a normal node in the network. In this paper,
we analyzed the impact of eavesdropper while executing an Ad hoc On Demand routing (AODV) protocol
in MANETs. All the simulations are done using QualNet 5.1 network simulator. From the results, it is found
that the network performance degrades in presence of an eavesdropper.
Analyzing the Impact of Eaves on Energy Consumption of AODV Routing Protocol ...ijwmn
In this dynamic world, communication is a sine qua non for development. Communication represents sharing of information which can be local or remote. Though local communications may occur face to face between individuals remote communications take place among people over long distances. Mobile ad hoc networks (MANETs) are becoming an interesting part of research due to the increasing growth of wireless devices (laptops, tablets, mobiles etc.) and as well as wireless internet facilities like 4G/Wi-Fi. A MANET is any infrastructure-less network formed by independent and self-configuring nodes. Each node acts as router. In order to send data, the source node initiates a routing process by using a routing protocol. The nature of the wireless medium is always insecure. So, during routing many attacks can take place. The main objective of an eavesdropper is to grab the confidential information in the network. This secret information is used by a malicious node to perform further attacks. Here, the entire problem lies in identifying the eavesdropper because the eavesdropper acts a normal node in the network. In this paper, we analyzed the impact of eavesdropper while executing an Ad hoc On Demand routing (AODV) protocol in MANETs. All the simulations are done using QualNet 5.1 network simulator. From the results, it is found that the network performance degrades in presence of an eavesdropper.
Requisite Trust Based Routing Protocol for WSNAM Publications
A mobile ad-hoc network (MANET) is an infrastructure less network of mobile devices connected by wireless
links. To secure a MANET in colluding nodes environment, the proposed work aims to detect and defend colluding nodes that
causes internal attacks. In order to achieve this, the work focuses on the novel algorithm of trust computation and route
detection that detects colluding nodes, without message and route redundancy during route discovery by using Requisite Trust
based Secure Routing Protocol (RTSR). The trust will be calculated in local forwarding nodes, which are used to discover the
route. The trust values from one hop neighbors are used to calculate the single trust value for each node using the constant
normalization concept. Route discovery and trust information will be stored in fixed cluster head (CH).
Mitigation of Colluding Selective Forwarding Attack in WMNs using FADEIJTET Journal
ABSTRACT - Wireless Mesh Networks (WMNs) have emerged as a promising technology because of their wide range of
applications. Wireless mesh networks wireless mesh networks (WMNs) are dynamically self – organizing, self –
configuring, self – healing with nodes in the network automatically establishing an adHoc network and maintaining mesh
connectivity. Because of their fast connectivity wireless mesh networks (WMNs) is widely used in military applications.
Security is the major constrain in wireless mesh networks (WMNs). This paper considers a special type of DoS attack
called selective forwarding attack or greyhole attack. With such an attack, a misbehaving mesh router just forwards few
packets it receives but drops sensitive data packets. To mitigate the effect of such attack an approach called FADE :
Forward Assessment based Detection is adopted. FADE scheme detects the presence of attack inside the network by
means of two-hop acknowledgment based monitoring and forward assessment based detection. FADE operates in three
phases and analyzed by determining optimal threshold values. This approach is found to provide effective defense against
the collaborative internal attackers in WMNs.
Black hole Attack Avoidance Protocol for wireless Ad-Hoc networksijsrd.com
A Mobile Ad-Hoc Network is a collection of mobile nodes or a temporary network set up by wireless mobile nodes moving arbitrary in the places that have no network infrastructure in such a manner that the interconnections between nodes are capable of changing on continual basis. Thus the nodes find a path to the destination node using routing protocols. However, due to security vulnerabilities of the routing protocols, wireless ad-hoc networks are unprotected to attacks of the malicious nodes. Various attacks and one of those attacks is the Black Hole Attack against network integrity absorbing all data packets in the network. Since the data packets do not reach the destination node on account of this attack, data loss will occur. Therefore, it is a severe attack that can be easily employed against routing in mobile ad hoc networks. There are lots of detection and defense mechanisms to eliminate the intruder that carry out the black hole attack. . Virtual Infrastructure achieves reliable transmission in Mobile Ad Hoc Network. Black Hole Attack is the major problem to affect the Virtual Infrastructure. In this paper, approach on analyzing and improving the security of AODV, which is one of the popular routing protocols for MANET. Our aim is to ensuring the avoidance against Black hole attack.
Secured Intrusion Protection System through EAACK in MANETSijtsrd
Achieving reliable routing has always been a major issue in the design of communication networks, due to the absence of fixed infrastructure among which mobile ad hoc networks MANETs that can take control of the most adversarial networking environment, and the dynamic network topology the nature of open transmission media. In the MANETs these characteristics also more challenging to make the design of routing protocols. The network topology varies so to determining feasible routing paths for distributing messages in a decentralized is a difficult job. Factors such as the extensive distribution of nodes and open medium, variable wireless link quality topological changes, and propagation path loss become pertinent issues and make MANET unprotected to instructions. Thus, it becomes central to develop a systematic intrusion detection scheme to secure Mobile Ad Hoc networks from intruders. In this project, we put forward and applied an efficient IDS mechanism based on Enhanced Adaptive Acknowledgment EAACK especially made for MANETs which performs better than the earlier techniques such as AACK, TWOACK and Watchdog. Mr. Ravishankar Kandasamy | M. Ajith Kumar | M. Ajith Kumar | G. Arun Kumar "Secured Intrusion Protection System through EAACK in MANETS" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30457.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30457/secured-intrusion-protection-system-through-eaack-in-manets/mr-ravishankar-kandasamy
Analyzing the Impact of Blackhole Attacks on AODV and DSR Routing Protocols’ ...IJCSEA Journal
Mobile Ad-Hoc Networks (MANETs) are wireless networks characterized by their lack of a fixed infrastructure, allowing nodes to move freely and serve as both routers and hosts. These nodes establish virtual links and utilize routing protocols such as AODV, DSR, and DSDV to establish connections. However, security is a significant concern, with the Blackhole attack posing a notable threat, wherein a malicious node drops packets instead of forwarding them. To investigate the impact of Blackhole nodes and assess the performance of AODV and DSR protocols, the researchers employed the NS-2.35 ns-allinone2.35 version for simulation purposes. The study focused on several metrics, including average throughput, acket delivery ratio, and residual energy. The findings revealed that AODV demonstrated better energy efficiency and packet delivery compared to DSR, but DSR outperformed AODV in terms of throughput. Additionally, environmental factors and data sizes were taken into account during the analysis.
ANALYZING THE IMPACT OF BLACKHOLE ATTACKS ON AODV AND DSR ROUTING PROTOCOLS’ ...IJCSEA Journal
Mobile Ad-Hoc Networks (MANETs) are wireless networks characterized by their lack of a fixed
infrastructure, allowing nodes to move freely and serve as both routers and hosts. These nodes establish
virtual links and utilize routing protocols such as AODV, DSR, and DSDV to establish connections.
However, security is a significant concern, with the Blackhole attack posing a notable threat, wherein a
malicious node drops packets instead of forwarding them. To investigate the impact of Blackhole nodes and
assess the performance of AODV and DSR protocols, the researchers employed the NS-2.35 ns-allinone2.35 version for simulation purposes. The study focused on several metrics, including average throughput,
packet delivery ratio, and residual energy. The findings revealed that AODV demonstrated better energy
efficiency and packet delivery compared to DSR, but DSR outperformed AODV in terms of throughput.
Additionally, environmental factors and data sizes were taken into account during the analysis.
ANALYZING THE IMPACT OF BLACKHOLE ATTACKS ON AODV AND DSR ROUTING PROTOCOLS’ ...IJCSEA Journal
Mobile Ad-Hoc Networks (MANETs) are wireless networks characterized by their lack of a fixed
infrastructure, allowing nodes to move freely and serve as both routers and hosts. These nodes establish
virtual links and utilize routing protocols such as AODV, DSR, and DSDV to establish connections.
However, security is a significant concern, with the Blackhole attack posing a notable threat, wherein a
malicious node drops packets instead of forwarding them. To investigate the impact of Blackhole nodes and
assess the performance of AODV and DSR protocols, the researchers employed the NS-2.35 ns-allinone2.35 version for simulation purposes. The study focused on several metrics, including average throughput,
packet delivery ratio, and residual energy. The findings revealed that AODV demonstrated better energy
efficiency and packet delivery compared to DSR, but DSR outperformed AODV in terms of throughput.
Additionally, environmental factors and data sizes were taken into account during the analysis.
Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WS-OLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
May_2024 Top 10 Read Articles in Computer Networks & Communications.pdfIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
A Topology Control Algorithm Taking into Account Energy and Quality of Transm...IJCNCJournal
The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.
Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy C...IJCNCJournal
The integration of artificial intelligence technology with a scalable Internet of Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. The multi-server environment within IoT introduces a flexible security service model, enabling users to interact with any server through a single registration. To ensure secure and privacy preservation services for resources, an authentication scheme is essential. Zhao et al. recently introduced a user authentication scheme for the multi-server environment, utilizing passwords and smart cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on Zhao et al.'s scheme, focusing on denial of service and privacy attacks, revealing a lack of user-friendliness. Subsequently, we propose a new multi-server user authentication scheme for privacy preservation with fuzzy commitment over the IoT environment, addressing the shortcomings of Zhao et al.'s scheme. Formal security verification of the proposed scheme is conducted using the ProVerif simulation tool. Through both formal and informal security analyses, we demonstrate that the proposed scheme is resilient against various known attacks and those identified in Zhao et al.'s scheme.
Advanced Privacy Scheme to Improve Road Safety in Smart Transportation SystemsIJCNCJournal
In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.
April 2024 - Top 10 Read Articles in Computer Networks & CommunicationsIJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
High Performance NMF Based Intrusion Detection System for Big Data IOT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
A Novel Medium Access Control Strategy for Heterogeneous Traffic in Wireless ...IJCNCJournal
So far, Wireless Body Area Networks (WBANs) have played a pivotal role in driving the development of intelligent healthcare systems with broad applicability across various domains. Each WBAN consists of one or more types of sensors that can be embedded in clothing, attached directly to the body, or even implanted beneath an individual's skin. These sensors typically serve asingle application. However, the traffic generated by each sensor may have distinct requirements. This diversity necessitates a dual approach: tailored treatment based on the specific needs of each traffic typeand the fulfillment of application requirements, such asreliability and timeliness. Never the less, the presence of energy constraints and the unreliable nature of wireless communications make QoS provisioning under such networks a non-trivial task. In this context, the current paper introduces a novel Medium AccessControl (MAC) strategy for the regular traffic applications of WBANs, designed to significantly enhance efficiency when compared to the established MAC protocols IEEE 802.15.4 and IEEE 802.15.6, with a particular focus on improving reliability, timeliness, and energy efficiency.
A Topology Control Algorithm Taking into Account Energy and Quality of Transm...IJCNCJournal
The efficient use of energy in wireless sensor networks is critical for extending node lifetime. The network topology is one of the factors that have a significant impact on the energy usage at the nodes and the quality of transmission (QoT) in the network. We propose a topology control algorithm for software-defined wireless sensor networks (SDWSNs) in this paper. Our method is to formulate topology control algorithm as a nonlinear programming (NP) problem with the objective to optimizing two metrics, maximum communication range, and desired degree. This NP problem is solved at the SDWSN controller by employing the genetic algorithm (GA) to determine the best topology. The simulation results show that the proposed algorithm outperforms the MaxPower algorithm in terms of average node degree and energy expansion ratio.
Multi-Server user Authentication Scheme for Privacy Preservation with Fuzzy C...IJCNCJournal
The integration of artificial intelligence technology with a scalable Internet of Things (IoT) platform facilitates diverse smart communication services, allowing remote users to access services from anywhere at any time. The multi-server environment within IoT introduces a flexible security service model, enabling users to interact with any server through a single registration. To ensure secure and privacy preservation services for resources, an authentication scheme is essential. Zhao et al. recently introduced a user authentication scheme for the multi-server environment, utilizing passwords and smart cards, claiming resilience against well-known attacks. This paper conducts cryptanalysis on Zhao et al.'s scheme, focusing on denial of service and privacy attacks, revealing a lack of user-friendliness. Subsequently, we propose a new multi-server user authentication scheme for privacy preservation with fuzzy commitment over the IoT environment, addressing the shortcomings of Zhao et al.'s scheme. Formal security verification of the proposed scheme is conducted using the ProVerif simulation tool. Through both formal and informal security analyses, we demonstrate that the proposed scheme is resilient against various known attacks and those identified in Zhao et al.'s scheme.
Advanced Privacy Scheme to Improve Road Safety in Smart Transportation SystemsIJCNCJournal
In -Vehicle Ad-Hoc Network (VANET), vehicles continuously transmit and receive spatiotemporal data with neighboring vehicles, thereby establishing a comprehensive 360-degree traffic awareness system. Vehicular Network safety applications facilitate the transmission of messages between vehicles that are near each other, at regular intervals, enhancing drivers' contextual understanding of the driving environment and significantly improving traffic safety. Privacy schemes in VANETs are vital to safeguard vehicles’ identities and their associated owners or drivers. Privacy schemes prevent unauthorized parties from linking the vehicle's communications to a specific real-world identity by employing techniques such as pseudonyms, randomization, or cryptographic protocols. Nevertheless, these communications frequently contain important vehicle information that malevolent groups could use to Monitor the vehicle over a long period. The acquisition of this shared data has the potential to facilitate the reconstruction of vehicle trajectories, thereby posing a potential risk to the privacy of the driver. Addressing the critical challenge of developing effective and scalable privacy-preserving protocols for communication in vehicle networks is of the highest priority. These protocols aim to reduce the transmission of confidential data while ensuring the required level of communication. This paper aims to propose an Advanced Privacy Vehicle Scheme (APV) that periodically changes pseudonyms to protect vehicle identities and improve privacy. The APV scheme utilizes a concept called the silent period, which involves changing the pseudonym of a vehicle periodically based on the tracking of neighboring vehicles. The pseudonym is a temporary identifier that vehicles use to communicate with each other in a VANET. By changing the pseudonym regularly, the APV scheme makes it difficult for unauthorized entities to link a vehicle's communications to its real-world identity. The proposed APV is compared to the SLOW, RSP, CAPS, and CPN techniques. The data indicates that the efficiency of APV is a better improvement in privacy metrics. It is evident that the AVP offers enhanced safety for vehicles during transportation in the smart city.
DEF: Deep Ensemble Neural Network Classifier for Android Malware DetectionIJCNCJournal
Malware is one of the threats to security of computer networks and information systems. Since malware instances are available sufficiently, there is increased interest among researchers on usage of Artificial Intelligence (AI). Of late AI-enabled methods such as machine learning (ML) and deep learning paved way for solving many real-world problems. As it is a learning-based approach, accumulated training samples help in improving thequality of training and thus leveraging malware detection accuracy. Existing deep learning methods are focusing on learning-based malware detection systems. However, there is need for improving the state of the art through ensemble approach. Towards this end, in this paper we proposed a framework known as Deep Ensemble Framework (DEF) for automatic malware detection. The framework obtains features from training samples. From given malware instance a grayscale image is generated. There is another process to extract the opcode sequences. Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) techniques are used to obtain grayscale image and opcode sequence respectively. Afterwards, a stacking ensemble is employed in order to achieve efficient malware detection and classification. Malware samples collected fromthe Internet sources and Microsoft are used for theempirical study. An algorithm known as Ensemble Learning for Automatic Malware Detection (EL-AML) is proposed to realize our framework. Another algorithm named Pre-Process is proposed to assist the EL-AML algorithm for obtaining intermediate features required by CNN and LSTM.Empirical study reveals that our framework outperforms many existing methods in terms of speed-up and accuracy.
High Performance NMF based Intrusion Detection System for Big Data IoT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
Enhancing Traffic Routing Inside a Network through IoT Technology & Network C...IJCNCJournal
IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
IoT Guardian: A Novel Feature Discovery and Cooperative Game Theory Empowered...IJCNCJournal
Cyber intrusion attacks increasingly target the Internet of Things (IoT) ecosystem, exploiting vulnerable devices and networks. Malicious activities must be identified early to minimize damage and mitigate threats. Using actual benign and attack traffic from the CICIoT2023 dataset, this WORK aims to evaluate and benchmark machine-learning techniques for IoT intrusion detection. There are four main phases to the system. First, the CICIoT2023 dataset is refined to remove irrelevant features and clean up missing and duplicate data. The second phase employs statistical models and artificial intelligence to discover novel features. The most significant features are then selected in the third phase based on cooperative game theory. Using the original CICIoT2023 dataset and a dataset containing only novel features, we train and evaluate a variety of machine learning classifiers. On the original dataset, Random Forest achieved the highest accuracy of 99%. Still, with novel features, Random Forest's performance dropped only slightly (96%) while other models achieved significantly lower accuracy. As a whole, the work contributes substantial contributions to tailored feature engineering, feature selection, and rigorous benchmarking of IoT intrusion detection techniques. IoT networks and devices face continuously evolving threats, making it necessary to develop robust intrusion detection systems.
** Connect, Collaborate, And Innovate: IJCNC - Where Networking Futures Take ...IJCNCJournal
The International Journal of Computer Networks & Communications (IJCNC) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Computer Networks & Communications. The journal focuses on all technical and practical aspects of Computer Networks & data Communications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced networking concepts and establishing new collaborations in these areas.
Enhancing Traffic Routing Inside a Network through IoT Technology & Network C...IJCNCJournal
IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
A Strategic Approach: GenAI in EducationPeter Windle
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An intrusion detection mechanism for manets based on deep learning artificial neural networks (anns)
1. International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.1, January 2023
DOI: 10.5121/ijcnc.2023.15101 1
AN INTRUSION DETECTION MECHANISM FOR
MANETS BASED ON DEEP LEARNING ARTIFICIAL
NEURAL NETWORKS (ANNS)
Mohamad T Sultan1,2
, Hesham El Sayed1,2
and Manzoor Ahmed Khan3
1
College of Information Technology United Arab Emirates University, Abu Dhabi, UAE
2
Emirates Center for Mobility Research (ECMR), United Arab Emirates University,
United Arab Emirates
3
College of Information Technology United Arab Emirates University, Abu Dhabi, UAE
ABSTRACT
Mobile Ad-hoc Network (MANET) is a distributed, decentralized network of wireless portable nodes
connecting directly without any fixed communication base station or centralized administration. Nodes in
MANET move continuously in random directions and follow an arbitrary manner, which presents
numerous challenges to these networks and make them more susceptible to different security threats. Due
to this decentralized nature of their overall architecture, combined with the limitation of hardware
resources, those infrastructure-less networks are more susceptible to different security attacks such as
black hole attack, network partition, node selfishness, and Denial of Service (DoS) attacks. This work aims
to present, investigate, and design an intrusion detection predictive technique for Mobile Ad hoc networks
using deep learning artificial neural networks (ANNs). A simulation-based evaluation and a deep ANNs
modelling for detecting and isolating a Denial of Service (DoS) attack are presented to improve the overall
security level of Mobile ad hoc networks.
KEYWORDS
Network Protocols, deep learning, ANN, intrusion detection
1. INTRODUCTION
Recently, the significant advances in wireless networking systems have recently made
them among the most innovative topics in computer technologies. Users can access a
wide range of information and services through mobile wireless networks. Latest
technology developments in wireless data communication devices have led to cheaper prices and
larger data rates. Compared to the conventional wired networking, the wireless networking
provides a great deal of flexibility, efficiency and cost effectiveness that make them a good
alternative in providing an efficient network connectivity. The development of Mobile Ad hoc
networks (MANETs) [1][2] presented a reliable, cost-effective and efficient techniques exploit
the availability and presence of mobile hosts during the lack of a fixed communication
infrastructure. In MANET, the mobile nodes are independent and can effortlessly initiate a direct
communication channel with each other as they are freely moving around the infrastructure-less
network in different directions and at different velocity speeds. The Ad hoc network functions in
a very specific way and nodes cooperation is its main element for forwarding the communication
related information from main data sources to the planned destination mobile nodes. Nodes in
MANET relies entirely in its operation on batteries as means of energy to move arbitrarily with
no restrictions. The mobile nodes could leave or join the dynamic network at any specific time
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and can take independent decisions without relying on any centralized authority. Due to its core
aspect of abandoning the availability of any fixed infrastructure as a necessary factor for the
communication to be present. This has dictated that the transmission and communication range of
the entire network will be determined by the transmission range of the individual mobile nodes,
and it is usually smaller in size compared to the range of the cellular networks. Nevertheless, in
cellular networks to avoid interference and provide guaranteed bandwidth, each communication
cell depends on various communication frequencies available from the on-hope adjacent
neighbouring cells. This expands the communication range of the cellular network especially
when different communication cells are joined together to offer a radio and communication
coverage for wide-ranging geographical area. However, in MANET each mobile node has a
wireless interface and interconnects with other nodes over a wireless channel. Mobile nodes in
MANET could range from portable laptops to smartphones or any other digital devices with a
communication wireless antenna. Among the numerous advantages the infrastructure-less ad hoc
network offers are robustness, efficiency, and inherent support for dynamic random mobility. Fig.
1 illustrates the architecture of MANET.
The special characteristics of MANETs have made its deployment a preferable choice for many
fields such as in military battlefield operations, natural disasters and in remote areas. However,
due to their openness and decentralized structure. MANET has become vulnerable to different
kinds of malicious threats and attacks. Their flexibility brings new threats to its security. The
categorization of threats that affect mobile ad hoc networks can be perceived in many ways based
on behaviour, level and position of the specific attack, flows of the used security algorithms and
weaknesses in the structure of the developed routing protocols. Attacks such as blackhole attack,
network partition, node selfishness, malicious node, and denial of service (DoS) are among the
many popular threats that MANETs is facing [3]. The shared goal for those threats is to degrade
the overall network performance. Researchers have become more focus on how to enhance and
provide a secure and reliable mobile ad hoc network. Several techniques have been developed
such as signature-based, statistical anomaly-based, and protocol analysis. This research will focus
on deep learning intrusion detection techniques in MANET based on artificial neural networks
The paper concentrates on very specific attack which is the denial-of-service DOS attach that can
easily disrupt the MANETs operations.
Figure 1. MANET architecture
2. RELATED WORK
Identifying malicious and misbehaving mobile nodes is necessary to protect the MANET
network. Researchers have conducted research on studying the security threats of mobile ad hoc
3. International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.1, January 2023
3
network to make MANET more secure and reliable. In describing the security threats, many
researchers make their own categorization of the security threats. MANET threats are classified
into two levels. The first level is attacks on the basic mechanism resulted from nodes captured,
compromised or the misbehaviour of nodes that do not listen to the rules of cooperative
algorithms. The second level is attacks on the security mechanism which exploit the
vulnerabilities of the security mechanism employed in MANET. In [4] and [5] researchers have
classified security attacks in correspondence with the communication layers, which mean that
each layer has its own threats and vulnerabilities. Table 1 shows security threats at the
communication layers.
Table 1. Communication Layers Security Threats
Layers Attacks
Application layer
Selfish nodes attacks, Malicious attacks like viruses, worms and spyware.
Transport layer
Session hijacking, Session control, flooding attack and ACK-storm attack
on TCP
Network layer
Cache poisoning attacks, Routing protocols attacks (e.g., AODV, DSR,
TORA), Packet dropping attacks, blackhole attack node impersonation,
denial-of-service DoS attack.
Data link layer
Man in the middle attack, MAC interruption (802.11), WEP vulnerability.
Physical layer Eavesdropping, jamming, traffic interceptions.
In [6] the authors have studied the effect of misbehaviour nodes on the MANET network. In this
research a new method was used to efficiently detect and separate malicious mobile nodes from
the network. Thus, the network performance remains balanced and stable regardless of the
presence of the colluding nodes. The malicious behaviour is represented by suspicious behaviour
of unauthorized mobile nodes that can inflict damage on other nodes in the network intentionally
or unintentionally. An example of this could include the scenario where the aim of the mobile
node is not the attack itself but to gain unauthorized benefits over other nodes.
The researchers in [7] proposed a blackhole attack identification mechanism in MANET using
fuzzy-based intrusion detection techniques. Their main target was to detect the blackhole attack
in the mobile network, which is considered as very popular type of malicious attack that disrupts
the operations of MANETs. An adaptive neuro fuzzy inference system was developed by the
researchers. The development of this system was based on the popular optimization technique;
particle swarm optimization (PSO). Similarly, using fuzzy logic techniques the authors in [8]
used a new technique for intrusion detection called node blocking mechanism, to differentiate
two popular attacks that targets the network which are the grey hole and the black hole attacks,
The authors in [9] proposed a system that uses malicious behaviour-detection ratios to enhance
security in mobile networks using modified zone-based intrusion detection techniques. In [10]
another intrusion detection system was proposed using smart approach for intrusion identification
and isolation. This system detects an attack on the ad hoc network by exhibiting a deep learning
neural network with bootstrapped optimistic algorithm. In this system each mobile node submits
finger vein biometric, user id, and latitude and longitude then the intrusion detection is executed
to verify these entities and detect any suspicious behaviour in the network.
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3. MANET ROUTING PROTOCOLS
The random arbitrary nature of mobile nodes in MANET due to the absence of any fixed
communication infrastructure keep the network’s topology in constant change. This rapid and
dynamic change in topology make routing in MANET a challenging task. Thus, an effective
routing strategy is required to smoothly accomplish the forwarding of packets form the source to
the destination. The routing information is changing frequently to reflect the dynamic changes in
network topology. There are numerous potential paths from source to destination. The routing
protocol algorithms discovers a route and transports the data packets to the appropriate
destination. Numerous routing algorithms for MANET have been developed [11]. The
performance of the ad hoc network is highly associated with used routing protocols efficiency.
The proposed routing algorithms for MANET can be divided into three different categories based
on their behaviour and functionality. These categories are proactive, reactive and hybrid routing
algorithms [11][12]. The basic concept of these routing algorithms is to discover the shortest
route for the source-destination routes selection.
3.1. AODV and Targeted Attacks
In MANETs, one of the widely used routing protocols that follows the reactive routing
mechanism is the Ad hoc on demand distance vector (AODV) [13]. The AODV protocol sets up
routes using a query cycle consisting of Route request (RREQ) and Route Reply (RREP). If a
node has the most recent sequence number for a certain destination and needs to deliver data
packets to that location, it will broadcast an RREQ message to its neighbours. Until the requested
data is available in some form, this message will be transmitted. After receiving the RREQ
message, every node builds a path back to its original sender. After receiving an RREQ message,
the destination will respond with an RREP message that includes the destination's current
sequence number and the number of hops taken to get there [14][17]. Keep in mind that if a given
intermediate node has a newly discovered route to the final destination, it will not relay the
RREQ to its neighbours but will instead send an RREP back in the direction of the source. Each
node that gets the RREP message sets up a new forward route to the final destination. Thus,
rather of storing the whole path, each node simply stores the information necessary for the next
step. When a node detects that it has received a duplicate RREQ, it discards the packet. As an
added measure, AODV verifies the freshness of the routes by using sequence numbers. The
destination routes are only altered if a new path to a given destination has a higher sequence
number than the previous path or has the same sequence number but with fewer hops. Moreover,
when there is a link failure or a routing problem happens in the network, another technique is
executed in AODV which is the route error (RERR) [13][14]. This technique sends warning error
packets to the source and destination nodes in the ad hoc network. As an example, used in this
research, this section discusses examples of attacks on AODV routing protocol.
• Packet dropping attack: In this type of attack malicious mobile users may drop all the
legitimate incoming data packets that are mainly employed in route discovery and route
maintenance stages. This is usually happening with aim of disrupting the network services
such as (RREQ, RREP and RERR).
• Denial-of-Service (DoS): One of the most popular known attacks. This type of network attack,
render a resource or a service inaccessible in the network [15]. The main aim of this malicious
attack is not to get an unauthorized access by the perpetrator but is rather an act of vandalism
to shut down a machine, resource or network. This attack will usually result in a legitimate
users being unable to access the available resources. In AODV routing algorithm the attacking
malicious node that wants to disrupt MANET resources begins to frequently broadcast the
route request (RREQ) messages while the route discovery process is taking a place. Using an
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5
expired destination IP address that is still available in the address range of the network but is
not present in the mobile network anymore the neighboring nodes receive these malicious
messages. This malicious activity results in deceiving the mobile nodes to re-forward the route
request messages as there is no neighboring node having a fresh route to this fake and expired
destination address. The main goal of DoS attack is to consume the battery power of nodes
and disrupt or deny access of legitimate nodes to a specific network services.
4. METHODOLOGY
The mobile ad hoc networks are highly vulnerable to different kinds of security threats due to its
dynamic nature of randomness, decentralizations, and lake of central authority. The aim of this
work is to propose an effective intrusion detection mechanism for MANETs using deep learning
techniques such as artificial neural networks (ANNs). The denial-of-service attack that is being
considered in this research is implemented in way where a malicious intruder mobile node injects
its malicious data packets in large volumes into the mobile ad hoc network which leads to a
disruption and denial of services at the destination node. The main routing protocol used in this
study to perform the simulation experiments is the AODV. This routing protocol is used due to its
popularity in MANET. In this study and in our experimental setup all the factors and issues that
has an impact on link stability on the network is considered and analysed. The main attack which
is considered in this research is the Denial of Service (DoS) attack with the aim of rendering the
MANET resources and services inaccessible by overloading it with junk packets in a two way
network communication setting. This type of attack has the possibility to happen over both wired
and wireless networks. However, the wireless networks are more susceptible due to its radio
nature and more loosely specified restrictions such as the case of MANETs. Fig. 2 shows an
example of DoS attack where intruder D floods the host node C with extra malicious packets.
Figure 2. Example of DoS attack, the host node C flooded by Intruder D
The deep learning ANNs are used to detect intrusions based on abnormal network activity and the
attributes, labels and features are selected from the packets generated during the network
simulation. Given to the learning and generalizable attributes of artificial neural networks
(ANNs), and due to their ability to obtain knowledge from data and infer new information, are
more suitable to manage such tasks. The performance of the proposed intrusion detection system
is illustrated by means of simulation using AODV routing protocol in MANETs. ANN modelling
for attack detection using a simulated MANET environment will be used in this research.
6. International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.1, January 2023
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4.1. Implementation
The implementation of the proposed research is illustrated by means of simulations using NS-2
network simulator on Linux ubuntu 10.04 platform to evaluate the performance of the MANET
network with 15 mobile nodes forming a network. Attack detection, using a simulated MANET
environment and ANNs modelling is used. Once a simulation process is completed, NS2 follows
to display the simulation details is by generating a big sized trace file holding all the events
sequentially line by line. For all those reasons, the event-driven technique is used in NS2 as it can
keep all the occurred events as records and all those records can be traced and analysed for
evaluation purposes. In NS2 there are typically two kinds of output data records that can help in
further investigation for a specific simulation scenario. The first one is a trace file which records
the events traces that assist in studying the performance of the network by processing and
analysing it using numerous of methods. The second one is a network animator (NAM) file
which assists in detecting the interactions and movements between the mobile nodes visually.
Fig. 3 illustrates the complete procedure of how a specific simulation is conducted using NS2.
Figure 3. NS2 simulation process
4.1.1. Mobility Model
The mobility model plays a very important role in MANET simulations. The considered model
should attempt to simulate the movement, behaviour, and actions of real nodes in MANET.
However, the mobile nodes in MANET move in a very dynamic arbitrary and decentralized
manner. It’s a dynamic network of autonomous decentralized mobile nodes. A node in the
network could join or leave at any specific time which leads to high rates of link and topology
changes. Moreover, the mobile nodes make decision independently and behave as routers where
they can send, receive, or route the information simultaneously. Thus, to model this kind of
unpredictability and randomness that exist in mobile ad hoc networks, the researchers have
proposed different probability distribution models of MANET nodes. The most popular one that
is highly represent the distribution of MANET nodes is called the Random Waypoint Mobility
Model [16]. For this model the spatial distribution of mobile nodes movements is in general a non
uniform. The mobility model that represents the movements of the mobile nodes is an important
aspect for any simulation process because the way that these mobile nodes move and behave
affects in different ways the performance of the routing protocol that these nodes utilize. The
random waypoint mobility model is simple, reliable and is highly used to assess the behaviour of
the MANET [16]. This mobility model can highly represent the actions and movements of real
mobile nodes in real conditions. There is basically a specific pause time in this model that
operates when there are any sudden changes or differences in direction or velocity of mobile
nodes. When a specific wireless node starts to travel across the network, it remains in one
location for a particular period of time which is a pause time before it moves to another location.
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The node chooses the subsequent destination randomly in the simulation region once that
specified pause time has expired. These mobile nodes also select a speed that is generally
specified between the minimum and maximum speed (0, Maxspeed) during simulation process.
Then it travels to the newly chosen point at that selected speed. When the mobile node reaches at
targeted place, it starts waiting again for a certain period of time, seconds before selecting another
new way point and another speed. Then it initiates the same procedure all over again. Numerous
researchers have adopted and implemented this mobility model in their studies. The Movement of
individuals in a cafeteria or shopping mall, and movement of nodes in a conference are some of
its practical examples. Fig. 4 presents an illustration of the movement pattern for a mobile node
which begins at a randomly selected location (133, 180) and chosen a speed between (0 to 10
m/s) using random waypoint mobility model.
Figure 4. Random waypoint mobility for node movement pattern
4.2. Simulation Setup and Parameter Selection
A scenario file that defines the exact motion of every node in the network along with the exact
number of packets generated by each node in the network is being taken as an input for every
simulation run. This is together accompanied by the exact time at which each change in motion or
packet origination is to occur. The simulation is done using NS2 simulator as shown in table 2
below:
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Table 2. Simulation Parameters
Simulation parameters
Parameter Selected Value
Routing Protocol Ad hoc on demand distance vector (AODV)
Platform Linux distribution ubuntu version 10.04
Number of Nodes 15
Simulation Software NS-2
MAC Layer Protocol IEEE 802.11b
Simulation Area 500m X 500m
Traffic Generation Model CBR (Constant Bit Rate)
Size of packet 512 bytes
Mobility Model Random Waypoint
Maximum Speed 0-20 m/s
No. of Connections 2 to 10
Duration of experiment 200 sec
Type of Antenna Antenna/OmniAntenna
In this simulation execution, 15 nodes are deployed for MANET within the terrain of 500m X
500m using random waypoint mobility for the purpose of realization of a real-time simulation
and the simulation runs for the maximum experiment duration of 200s, with maximum speed of
20m/s. It’s indicated in the simulation parameters table the “Maximum Speed” of mobile nodes
which is in fact implies that the node’s speed is already changing form “0 m/s” which is a
stationary paused node “no movement” to maximum speed of “20 m/s”. Since we have used the
popular mobility model “Random Waypoint Mobility Model”, which is designed to specify users
or mobile nodes movement, their location, velocity, and acceleration change over time. The
mobile nodes speed in our simulation environment could change at any random time form (0 – 20
m/s). The MAC layer used is IEEE 802.11b. Once the simulation is finished, the generated output
files like trace files should be analysed to extract beneficial data and statistics. As stated earlier, a
pair of files will be produced once the simulation process ends. The first one is an event trace file
which records all simulation events while the second one is a network visualization file which
records the data that can be used in network animation. These event trace files are in its raw
format and an analysis and assessment should be performed in order to extract the required
necessary information. Both files are CPU intensive tasks while in simulation process and they
make use and occupy an amount of the memory. The example excerpt in Fig. 5 below shows how
a generated trace file will look like after a simulation run.
Figure 5. Excerpt of trace file
The excerpt above indicates that the data packet was sent (s) at time (t) 2.556838879 sec, from
the main source node (Hs) 1 to target mobile node (Hd) 2. The source node id (Ni) is 1, The
source node X axis coordinates (Nx) is 342.47, while the provided Y axis coordinate (Ny) is 4.35.
9. International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.1, January 2023
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Moreover, its Z coordinate (Nz) is 0.00. The available level of energy (Ne) is 1.000000, while the
type of trace format for this mobile node for routing (Nl) is RTR and the event of the node (Nw)
is blank. Moreover, (Ma) 0, is the specification of MAC level information while the address of
the destination Ethernet (Md) 0, the address of the source Ethernet (Ms) is 0 and Ethernet kind
(Mt) is 0. The features extracted from the logged details can then be used in ANN for attack
detection. The analysis process of these trace files can be done using different tools such as using
the AWK language command and Perl scripts. Different parameter selection for data extraction
can be considered for analysis which merely depends on the nature of the network and the
specific attack. The following parameters will be considered: Packet Loss PL, Packet sent (PS),
Packet received (PR), Energy consumption (EC). Using analysis log files of simulation run, the
parameters were extracted. The data is split for training and testing where 65% of data including
15 mobile nodes in 200 seconds were selected randomly for training and 35% for the purpose of
testing and validation process.
4.3. Designing Artificial Neural Network
An intrusion detection system using neural network (NN) is proposed to secure the MANET.
Neural Network model is trained by applying the simulation data as inputs to the ANN. Feed
Forward Back Propagation (FFBP) in the Neural network toolbox is used and the artificial neural
network is implemented with four inputs, one output layer including two middle hidden layers.
The network training in this setup is conducted using back propagation (BP) learning process,
The (TRAINLM) training function of Levenberg-Marquardt backpropagation is used in addition
to LEARNGDM as an adaptive learning function. Different transfer functions are available like
Purelin, Log-Sigmoid, and Tan-Sigmoid. The main aim of the transfer function is to be used for
estimating the output of a specific network layer from its initial net input. LogSigmoid, and Tan-
Sigmoid are used in this study. Fig. 6 below shows an example of different transfer functions.
Figure 6. Example of different transfer functions
A screenshot of how the artificial neural network setup and design is presented in Fig 7 and Fig. 8
respectively. All the setup parameters must be specified before running the artificial neural
network.
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Figure 7. Neural network setup
Figure 8. Neural network design
4.4. Modelling Artificial Neural Networks for DOS Attack Detection
Given to the learning and generalizable attributes of feedforward neural networks with back
propagation training algorithm, those deep learning networks are used for the purpose of DoS
intrusion detection and to identify and predict any unusual activity and the features are selected
from the packets generated in the simulation process. The number of input nodes will be
determined from the input data set. The number of nodes in the hidden layers in the neural
network are varied frequently during the experiments to achieve a highly accurate and stable
neural network model and to avoid any overfitting. The structural design of the proposed deep
learning neural network consists of two types of different network setups. The first one has 4
inputs and 15 neurons in the first hidden layer and 10 neurons in the second hidden layer and one
output. While the second network has 4 inputs and 20 neurons in the first hidden layer and 10
neurons in the second hidden layer and one output. Training using feed forward back propagation
(FFBP) in ANN is presented in Fig. 9 and the process is indicated as follows:
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• The model selects training epoch from the training set and initialize weights and biases.
• The model the calculates the output of the network.
• Then, the error between the network output and the desired output is calculated.
• The model modifies the weights of the network in a way that minimizes the error.
• The model repeats the steps for each input in the training set until the error for the entire
set is acceptable low.
Figure 9. Training process
5. PERFORMANCE RESULTS
The deep learning technique which is used to design the ANN uses the backpropagation training
algorithm to predicts a specific output. Then, this output is compared with actual known class
label to measure the difference in error between the predicted and actual outputs. The obtained
error is sent back to the neurons for adjustments. FFBP measures the variance of the residuals in
a repeated process. The root mean squared error is just one way to calculate this error. The
method of squaring the sum of the error is used to prevent the cancel out the positives and
negatives values during the sum of the error of all the nodes. We used the root mean squared
error instead of the mean absolute error (MAE) to measure the standard deviation of errors as the
gradient descent requires the derivative of that loss function to be calculated to minimize the loss
function and generate better outputs. The results are presented in table 3 below. The performance
results of the designed deep learning model are shown in the table based on the training data. The
selection process of the best performing model is based on results obtained.
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Table 3. Artificial neural network for training data
Network
Training/ Learning
functions Layers
Transfer
function RMSE Epoch
Feed Forward
Back Propagation
(FFBP)
Training:
TrainLM
Learning:
LearnGDM
4–15–10-1
LogSigmoid
0.1924 10
0.1901 12
TanSigmoid
0.0452 14
0.0618 16
4-20-10-1
LogSigmoid
0.1927 10
0.1801 12
TanSigmoid
0.0492 14
0.0835 16
We executed the neural network to detect unusual malicious activities in MANET. As it can be
noticed in the performance results that two different transfer functions are used in this research
Log-Sigmoid and Tan-Sigmoid. A well-trained ANN should have a very low RMSE at the end of
the training phase The best result in ANNs for FFBP network with Tan-Sigmoid function is
related to 4-15-10-1 network that produce RMSE=0.0452, for 14 epochs. The indication of MSE
being quite small or almost close to zero is that the neural network model output and the desired
output have become very close to each other for the training dataset. The rest of results are given
in table 4 below. The table shows the performance results of the neural network model based on
testing data. It can be noticed that the best result for neural network model using FFBP with Tan-
Sigmoid function is related to 4-15-10-1 design that produce RMSE=0.0512.
Table 4. Artificial neural networks based on testing data
Network
Training
Function Layers Transfer function RMSE
Feed Forward
Back Propagation
(FFBP)
Training:
TrainLM
Learning:
LearnGDM
4–15–10-1
LogSigmoid
0.1998
0.1982
TanSigmoid
0.0512
0.0781
4-20-10-1
LogSigmoid
0.2337
0.1891
TanSigmoid
0.0821
0.0935
Both of Fig. 10 and Fig. 11 show a summary of how the designed artificial neural networks
(ANN 4-15-10-1) and (ANN 4-20-10-1) performed for training and testing phases. In this
research, after we selected the best model with best RMSE value, we used this model to evaluate
the performance of proposed system. The goal is to distinguish a normal connection form a
malicious attack connection in MANET. Thus, we used a performance measure which is the
Detection Rate (DR). This measure is calculated as the number of attack connections which
classified correctly as an attack over the total number of connections in the network. Using this
measure, we were able to detect the attack in the network with high accuracy as shown in the
table below. It can be noticed that as the number of connections increases the detection rate
decreases due to higher false positive rates.
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Table 5. Detection Rate
No. of connections Detection Rate
2 95.3%
5 94.2%
10 91.73%
Figure 10. RMSE for Training and Testing (ANN 4-15-10-1)
Figure 11. RMSE for Training and Testing (ANN 4-20-10-1)
6. CONCLUSIONS
This research paper is mainly focused on modelling and investigating the use of artificial neural
networks ANNs as a mean for intrusion detection in mobile ad hoc networks (MANETs). The
main objective of this work was to analyse, simulate and evaluate the use of feedforward neural
networks with back propagation (FFBP) in MANETs. An extracted dataset generated using the
means of simulations for mobile ad hoc networks is used to calculate the input parameters of this
approach and the RMSR is employed as metric to evaluate the performance of the proposed deep
learning artificial neural network modelling. The proposed modelling can be utilized for detecting
14. International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.1, January 2023
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DoS attack in MANET. The best results in ANNs for FFBP network with Tan- Sigmoid function
is related to 4-15-10-1 network that produce RMSE=0.0452, for 14 epochs for training and
RMSE=0.0512 for testing data. We also used the Detection Rate (DR) as a performance measure
to evaluate the selected neural network model. For the future works, different types of network
attacks will be considered for the purpose of intrusion detection. Another measure could be used
in the analysis is the coefficient of determination or R squared. R square is the percentage of
variation in Y explained by the model. The higher the percentage of the R square is the better.
However, the value of R square will be always less than one irrespective of the values in dataset
being small or large.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGEMENTS
This research was funded by the Emirates Center for Mobility Research (ECMR) of the United
Arab Emirates University (grant number 31R271).
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