The document proposes a distributed trust-based framework to protect mobile agents and host platforms in mobile ad hoc networks (MANETs) from attacks. The framework uses a distributed reputation model where mobile agents deployed in the network collaborate with visited host nodes to form a consistent trust view. Agents share information about suspected malicious nodes. A node's trustworthiness is quantified based on direct observations from agent experiences and indirect observations from neighboring nodes, using concepts from Dempster-Shafer theory. Simulation results show the approach can robustly detect malicious nodes like blackholes and wormholes, even in large networks, helping to secure mobile agents in the dynamic MANET environment.
Modelling of A Trust and Reputation Model in Wireless Networksijeei-iaes
Security is the major challenge for Wireless Sensor Networks (WSNs). The sensor nodes are deployed in non controlled environment, facing the danger of information leakage, adversary attacks and other threats. Trust and Reputation models are solutions for this problem and to identify malicious, selfish and compromised nodes. This paper aims to evaluate varying collusion effect with respect to static (SW), dynamic (DW), static with collusion (SWC), dynamic with collusion (DWC) and oscillating wireless sensor networks to derive the joint resultant of Eigen Trust Model. An attempt has been made for the same by comparing aforementioned networks that are purely dedicated to protect the WSNs from adversary attacks and maintain the security issues. The comparison has been made with respect to accuracy and path length and founded that, collusion for wireless sensor networks seems intractable with the static and dynamic WSNs when varied with specified number of fraudulent nodes in the scenario. Additionally, it consumes more energy and resources in oscillating and collusive environments.
A DISTRIBUTED TRUST MANAGEMENT FRAMEWORK FOR DETECTING MALICIOUS PACKET DROPP...IJNSA Journal
In a multi-hop mobile ad hoc network (MANET) mobile nodes communicate with each other forming a cooperative radio network. Security remains a major challenge for these networks due to their features of open medium, dynamically changing topologies, reliance on cooperative algorithms, absence of centralized monitoring points, and lack of any clear lines of defense. Most of the currently existingsecurity algorithms designed for these networks are insecure, in efficient, and have low detection accuracy for nodes’ misbehaviour. In this paper, a new approach has been proposed to bring out the complementary relationship between key distribution and misbehaviour detection for developing an integrated security solution for MANETs. The redundancy of routing inform ation in ad hoc networks is utilized to develop a highly reliable protocol that works even in presence of transient network
partitioning and Byzantine failure of nodes. The proposed mechanism is fully co-operative, and thus it is more robust as the vulnerabilities of the election algorithms used for choosing the subset of nodes for cooperation are absent. Simulation results show the effectiveness of the proposed protocol.
This document discusses trust-based routing in mobile ad hoc networks (MANETs). It provides an overview of several trust management approaches that have been proposed to improve routing reliability in MANETs. Specifically, it summarizes three approaches:
1. A framework that calculates trust values using direct observation and indirect recommendations to determine access control between nodes. Trust is mapped to access levels.
2. A hybrid trust management framework (HTMF) that evaluates trustworthiness based on direct observations and second-hand information to improve robustness against attacks.
3. An adaptive multi-level trust (AMLeT) framework that calculates two complementary trust levels - hard and soft trust - based on criteria like time and security
A New Way of Identifying DOS Attack Using Multivariate Correlation Analysisijceronline
This document summarizes a research paper that proposes a new method for identifying denial of service (DoS) attacks using multivariate correlation analysis (MCA). The method involves three main steps: 1) generating basic features from network traffic, 2) using MCA to extract correlations between features and generate triangle area maps, and 3) using an anomaly-based detection mechanism to distinguish attacks from normal traffic based on differences from pre-generated normal profiles. The researchers evaluate their method on the KDD Cup 99 dataset and achieve moderate detection performance. However, they identify issues related to differences in feature scales that reduce detection of some attacks. They propose using statistical normalization to address this.
Privacy preserving distributed profile matching in proximity-based mobile soc...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
A REVIEW: TRUST, ATTACKS AND SECURITY CHALLENGES IN MANETieijjournal
Mobile Ad-hoc Networks or MANETs are mostly found in situations where any fixed facilities are just not available. MANET provides some fundamental responsibilities such as routing, packet forwarding communication and network management etc over self structured network. This specially affects the energy, bandwidth and memory computation requirements. Providing trust in MANET is an additional
critical task because of lack of centralized infrastructure. Since during the deployment of MANET nodes that are fresh continue returning and aged ones go from the cluster/network, there is demand for maintaining the record also to provide appropriate certification for the arriving node(s) that are fresh as well as the present node(s) in the network. But due to various types of intrusion threats and attacks it is hard to fully scrutinize any new node so as to allow only safe nodes to get connected with the existing safe system. In a cluster of large size these trusted node(s) will likely be communicating together, all the while
allowing or disallowing entry/communication of the compromised node(s) or trusted model to continue to
maintain a stable, secured, trustworthy group of movable nodes. All the reported techniques have been systematically categorized and their strong and weak points have been discussed.
A METHOD OF TRUST MANAGEMENT IN WIRELESS SENSOR NETWORKSijsptm
The research problem considered in this paper is how to protect wireless sensor networks (WSN) against cyber-threats by applying trust management and how to strengthen network resilience to attacks targeting the trust management mechanism itself. A new method, called WSN Cooperative Trust Management Method (WCT2M), of distributed trust management in multi-layer wireless sensor networks is proposed and its performance is evaluated. The method is specified by giving its class model in UML and by
explaining the related attributes and methods. Different attacks against the network and against WCT2M deployed in the network are considered. The experimental evaluation of WCT2M involves laboratory experiments and simulations using a dedicated simulator. The evaluation focuses on efficiency of detecting and isolating the malicious nodes that implement different attack scenarios in the network and on the
method’s sensitivity to the changes in effectiveness of the security mechanisms deployed in the network nodes.
THE NASH’S BALANCE IN THE THEORY OF GAMES FOR A SECURE MODEL MECHANISM IN ROU...ijcisjournal
The present work is dedicated to study attacks and countermeasure in MANET. After a short introduction to what the Mobile Ad hoc Networks (MANETs) are and network security we present a survey of various attacks in MANETs pertaining to fail routing protocols. We present the different tools used by these attacks and the mechanisms used by the secured routing protocols to counter them. We also study a mechanism of security, named the reputation, proposed for the MANETs and the protocol which implements it. We also propose a secure mechanism which is based on the reputation. Our work ends with a proposal analytical model to the modules of our mechanism and the equilibrium states of our model.
Modelling of A Trust and Reputation Model in Wireless Networksijeei-iaes
Security is the major challenge for Wireless Sensor Networks (WSNs). The sensor nodes are deployed in non controlled environment, facing the danger of information leakage, adversary attacks and other threats. Trust and Reputation models are solutions for this problem and to identify malicious, selfish and compromised nodes. This paper aims to evaluate varying collusion effect with respect to static (SW), dynamic (DW), static with collusion (SWC), dynamic with collusion (DWC) and oscillating wireless sensor networks to derive the joint resultant of Eigen Trust Model. An attempt has been made for the same by comparing aforementioned networks that are purely dedicated to protect the WSNs from adversary attacks and maintain the security issues. The comparison has been made with respect to accuracy and path length and founded that, collusion for wireless sensor networks seems intractable with the static and dynamic WSNs when varied with specified number of fraudulent nodes in the scenario. Additionally, it consumes more energy and resources in oscillating and collusive environments.
A DISTRIBUTED TRUST MANAGEMENT FRAMEWORK FOR DETECTING MALICIOUS PACKET DROPP...IJNSA Journal
In a multi-hop mobile ad hoc network (MANET) mobile nodes communicate with each other forming a cooperative radio network. Security remains a major challenge for these networks due to their features of open medium, dynamically changing topologies, reliance on cooperative algorithms, absence of centralized monitoring points, and lack of any clear lines of defense. Most of the currently existingsecurity algorithms designed for these networks are insecure, in efficient, and have low detection accuracy for nodes’ misbehaviour. In this paper, a new approach has been proposed to bring out the complementary relationship between key distribution and misbehaviour detection for developing an integrated security solution for MANETs. The redundancy of routing inform ation in ad hoc networks is utilized to develop a highly reliable protocol that works even in presence of transient network
partitioning and Byzantine failure of nodes. The proposed mechanism is fully co-operative, and thus it is more robust as the vulnerabilities of the election algorithms used for choosing the subset of nodes for cooperation are absent. Simulation results show the effectiveness of the proposed protocol.
This document discusses trust-based routing in mobile ad hoc networks (MANETs). It provides an overview of several trust management approaches that have been proposed to improve routing reliability in MANETs. Specifically, it summarizes three approaches:
1. A framework that calculates trust values using direct observation and indirect recommendations to determine access control between nodes. Trust is mapped to access levels.
2. A hybrid trust management framework (HTMF) that evaluates trustworthiness based on direct observations and second-hand information to improve robustness against attacks.
3. An adaptive multi-level trust (AMLeT) framework that calculates two complementary trust levels - hard and soft trust - based on criteria like time and security
A New Way of Identifying DOS Attack Using Multivariate Correlation Analysisijceronline
This document summarizes a research paper that proposes a new method for identifying denial of service (DoS) attacks using multivariate correlation analysis (MCA). The method involves three main steps: 1) generating basic features from network traffic, 2) using MCA to extract correlations between features and generate triangle area maps, and 3) using an anomaly-based detection mechanism to distinguish attacks from normal traffic based on differences from pre-generated normal profiles. The researchers evaluate their method on the KDD Cup 99 dataset and achieve moderate detection performance. However, they identify issues related to differences in feature scales that reduce detection of some attacks. They propose using statistical normalization to address this.
Privacy preserving distributed profile matching in proximity-based mobile soc...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
A REVIEW: TRUST, ATTACKS AND SECURITY CHALLENGES IN MANETieijjournal
Mobile Ad-hoc Networks or MANETs are mostly found in situations where any fixed facilities are just not available. MANET provides some fundamental responsibilities such as routing, packet forwarding communication and network management etc over self structured network. This specially affects the energy, bandwidth and memory computation requirements. Providing trust in MANET is an additional
critical task because of lack of centralized infrastructure. Since during the deployment of MANET nodes that are fresh continue returning and aged ones go from the cluster/network, there is demand for maintaining the record also to provide appropriate certification for the arriving node(s) that are fresh as well as the present node(s) in the network. But due to various types of intrusion threats and attacks it is hard to fully scrutinize any new node so as to allow only safe nodes to get connected with the existing safe system. In a cluster of large size these trusted node(s) will likely be communicating together, all the while
allowing or disallowing entry/communication of the compromised node(s) or trusted model to continue to
maintain a stable, secured, trustworthy group of movable nodes. All the reported techniques have been systematically categorized and their strong and weak points have been discussed.
A METHOD OF TRUST MANAGEMENT IN WIRELESS SENSOR NETWORKSijsptm
The research problem considered in this paper is how to protect wireless sensor networks (WSN) against cyber-threats by applying trust management and how to strengthen network resilience to attacks targeting the trust management mechanism itself. A new method, called WSN Cooperative Trust Management Method (WCT2M), of distributed trust management in multi-layer wireless sensor networks is proposed and its performance is evaluated. The method is specified by giving its class model in UML and by
explaining the related attributes and methods. Different attacks against the network and against WCT2M deployed in the network are considered. The experimental evaluation of WCT2M involves laboratory experiments and simulations using a dedicated simulator. The evaluation focuses on efficiency of detecting and isolating the malicious nodes that implement different attack scenarios in the network and on the
method’s sensitivity to the changes in effectiveness of the security mechanisms deployed in the network nodes.
THE NASH’S BALANCE IN THE THEORY OF GAMES FOR A SECURE MODEL MECHANISM IN ROU...ijcisjournal
The present work is dedicated to study attacks and countermeasure in MANET. After a short introduction to what the Mobile Ad hoc Networks (MANETs) are and network security we present a survey of various attacks in MANETs pertaining to fail routing protocols. We present the different tools used by these attacks and the mechanisms used by the secured routing protocols to counter them. We also study a mechanism of security, named the reputation, proposed for the MANETs and the protocol which implements it. We also propose a secure mechanism which is based on the reputation. Our work ends with a proposal analytical model to the modules of our mechanism and the equilibrium states of our model.
TRUST ORIENTED SECURITY FRAMEWORK FOR AD HOC NETWORKcscpconf
An ad hoc network is a group of wireless mobile hosts that are connected momentarily through
wireless connections in the dearth of any centralized control or some supporting services. The
mobile ad hoc network is at risk by its environment because of the vulnerabilities at channel and
node level. The conventional security mechanisms deals with only protecting resources from unauthorized access, but are not capable to safeguard the network from who offer resources. Adding trust to the on hand security infrastructures would improvise the security of these environments. A trust oriented security framework for adhoc network using ontological engineering approach is proposed by modeling ad hoc network, the OLSR (Optimized Link State Routing) protocol and trust model as OWL (Ontology Web language) ontologies, which are integrated using Jena. In this model, a trustor can calculate its trust about trustee and use the calculated trust values to make decisions depending on the context of the application or interaction about granting or rejecting it. A number of experiments with a potential implementation of suggested framework are performed to validate the characteristics of a trust oriented model suggested by the literature by this framework
Classification and review of security schemesHabitamuAsimare
Mobile computing systems face security challenges due to their vulnerability. This document analyzes security schemes for mobile computing, classifying approaches for mobile ad hoc networks (MANETs) and mobile agents. For MANETs, threats include denial of service attacks and routing attacks. For mobile agents, protecting agents from hostile hosts is difficult. The taxonomy highlights contributions to address different attack types and approaches, identifying limitations and open issues to better secure mobile networks.
Elimination of Malicious Node by using Clustering Technique in Mobile Ad Hoc ...IRJET Journal
This document discusses techniques for eliminating malicious nodes in mobile ad hoc networks (MANETs) through clustering. Specifically, it proposes finding a single trusted cluster head node based on both transmission range and highest energy, rather than just highest energy, in order to avoid selecting malicious nodes as cluster heads. The proposed approach is tested through simulation in NS2, showing improved network performance compared to existing techniques. Distributed denial of service (DDoS) attacks pose a challenge in MANETs due to their open nature, and trust-based routing protocols have been developed to help mitigate such attacks by identifying malicious or untrusted nodes.
This document summarizes a research paper that proposes a new correlation approach based on watermarking to identify the source of attacks that occur through intermediary nodes. The approach embeds watermarks in encrypted network flows by slightly adjusting the timing of selected packets. It is designed to be robust against timing perturbations intentionally introduced by attackers. Experimental results show that the watermark-based approach achieves close to 100% true positives in identifying the source of attacks.
1. The document proposes a threat modeling approach called "randomized seeding attack Model" to prevent attacks that could affect virtual machines in the cloud.
2. The model uses Fibonacci and Lucas number series to represent how attacks could randomly spread from one virtual machine to others in the cloud environment.
3. Key aspects of the model include using random Fibonacci sequences to represent the spreading of attacks, where each new virtual machine affected is represented by the next number in the sequence.
An exaustive survey of trust models in p2 p networkijwscjournal
Most of the peers accessing the services are under the assumption that the service accessed in a P2P
network is utmost secured. By means of prevailing hard security mechanisms, security goals like
authentication, authorization, privacy, non repudiation of services and other hard security issues are
resolved. But these mechanisms fail to provide soft security. An exhaustive survey of existing trust and
reputation models in P2P network regarding service provisioning is presented and challenges are listed.
Trust issues like trust bootstrapping, trust evidence procurement, trust assessment, trust interaction
outcome evaluation and other trust based classification of peer’s behavior into trusted,, inconsistent, un
trusted, malicious, betraying, redemptive are discussed,
Review of Security Issues in Mobile Wireless Sensor NetworksEswar Publications
MWSNs are finding applicability in wide range of applications. Applications spread from day to day utilities to military and surveillance, where they may sense information about vehicular movements around border. Considering the importance of data being sent by these nodes, threat of compromising them has also increased. This paper aims to explore various types of attacks and tries to classify them based on some common parameter. Better understanding of various attacks, their style of functioning and point of penetration can help researchers devise better preventive measures.
Secure intrusion detection and attack measure selectionUvaraj Shan
This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.
PROTECTING PRIVACY IN VANETs USING MIX ZONES WITH VIRTUAL PSEUDONYM CHANGE IJNSA Journal
This document summarizes a research paper that proposes a technique for securely changing pseudonyms in vehicular ad hoc networks (VANETs) to enhance privacy. The technique uses "mix zones", predefined regions where vehicles can change pseudonyms. It introduces "virtual pseudonym changes" using transceivers if real vehicles are insufficient. Transceivers mimic pseudonym changes to increase complexity for adversaries trying to link pseudonyms. The technique calculates mapping weights between zones to determine when virtual changes are needed. It aims to guarantee high privacy even with low traffic by obscuring pseudonym linkages.
Adaptive Circumstance Knowledgeable Trusted System for Security Enhancement i...IJTET Journal
Every MANET application has its own policy and they need some special policies to enhance the security. In
MANET, each node acts as the router. The main challenging of the MANET setting up routing paths through the legitimate
nodes only. To make the MANET as the trusted system some external policies or schemes are needed. However, whether
for malicious or selfish purposes, a node may not cooperate during the network events or even try to interrupt them, both
are consider as misbehaviors. Substantial analysis efforts have been made to finding misbehaviors. Both the faulty
behaviors and malicious behavior are generally equally treated as misbehaviors without any further analysis by most of the
malicious behavior detection mechanisms. In this paper, propose the Adaptive Circumstance Knowledgeable trusted
framework, in which various contextual information, such as battery status weather condition and communication channel
status, are used to identify whether the misbehavior is a result of malicious activity or not.
Effectual Routine for Trilateral Authentication in Ad-hoc Networks using Mult...IOSR Journals
This document proposes a protocol for trilateral authentication in ad-hoc networks using multicast conventions. It introduces a central authority that manages key authentication and certification to increase security and reliability. Nodes are grouped into clusters, each with a cluster head. For similar clusters, authentication uses time asymmetry based on TESLA. For cross-cluster traffic, it uses secret information asymmetry where the source sends packets to cluster heads, which relay to members. Evaluation shows the central authority uses less memory than previous methods and the protocol has higher efficiency.
IRJET- A Confidence Model based Routing Practice for Secure Adhoc NetworksIRJET Journal
This document proposes a Trusted AODV (TAODV) routing protocol to secure ad hoc networks. TAODV extends the AODV routing protocol and uses a trust model to represent trust relationships between nodes. In TAODV, a node's trust in another is represented by an opinion that considers belief, disbelief, and uncertainty. Opinions are updated over time based on successful or failed communications. TAODV introduces a trust recommendation mechanism to exchange trust information between nodes. The protocol performs trusted routing discovery and maintenance based on nodes' trust opinions of one another to improve security and efficiency compared to cryptographic approaches.
Evasion Streamline Intruders Using Graph Based Attacker model Analysis and Co...Editor IJCATR
Network Intrusion detection and Countermeasure Election in virtual network systems (NICE) are used to establish a
defense-in-depth intrusion detection framework. For better attack detection, NICE incorporates attack graph analytical procedures into
the intrusion detection processes. We must note that the design of NICE does not intend to improve any of the existing intrusion
detection algorithms; indeed, NICE employs a reconfigurable virtual networking approach to detect and counter the attempts to
compromise VMs, thus preventing zombie VMs. NICE includes two main phases: deploy a lightweight mirroring-based network
intrusion detection agent (NICE-A) on each cloud server to capture and analyze cloud traffic. A NICE-A periodically scans the virtual
system vulnerabilities within a cloud server to establish Scenario Attack Graph (SAGs), and then based on the severity of identified
vulnerability toward the collaborative attack goals, NICE will decide whether or not to put a VM in network inspection state. Once a
VM enters inspection state, Deep Packet Inspection (DPI) is applied, and/or virtual network reconfigurations can be deployed to the
inspecting VM to make the potential attack behaviors prominent.
EXPOSURE AND AVOIDANCE MECHANISM OF BLACK HOLE AND JAMMING ATTACK IN MOBILE A...ijcseit
Mobile ad hoc network (MANETs) is an infrastructure-less/self-configurable system in which every node
carries on as host or router and every node can participate in the transmission of packets. Because of its
dynamic behaviour such system is more susceptible against various sorts of security threats, for example,
Black hole, Wormhole , Jamming , Sybil, Byzantine attack and so on which may block the transmission of
the system. Black hole attack and Jamming attack is one of them which promote itself has shortest or new
fresh route to the destination while jamming attack which make activity over the system. This paper
introduces the thorough literature study for the Black hole attack and jamming attack of both the attack by
various researchers.
An Optimal Risk- Aware Mechanism for Countering Routing Attacks in MANETsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
For enhancing VANET security, an autonomic trust and reputation monitoring scheme is proposed which uses a trust based data transfer protocol based on reputation and plausibility checks. The proposed framework uses autonomic principles and trust monitoring scheme to provide uniform trust information throughout the network with minimum overhead. It aims to reduce the impact of double-face attacks by isolating selfish and malicious nodes. The scheme generates local and global trust tables to evaluate node trustworthiness and identify malicious nodes for isolation from the network.
This document summarizes a research paper that proposes a new trust management scheme to enhance security in mobile ad hoc networks (MANETs). The scheme uses uncertain reasoning to evaluate trust values based on direct observation from a node and indirect observation from neighboring nodes. Trust is evaluated using Bayesian inference for direct observation and Dempster-Shafer theory for indirect observation. Simulation results show the scheme improves throughput, packet delivery ratio, and security with a slight increase in delay and overhead compared to the standard Ad Hoc On-Demand Distance Vector (AODV) routing protocol.
AUTHENTICATION USING TRUST TO DETECT MISBEHAVING NODES IN MOBILE AD HOC NETWO...IJNSA Journal
Providing security in Mobile Ad Hoc Network is crucial problem due to its open shared wireless medium, multi-hop and dynamic nature, constrained resources, lack of administration and cooperation. Traditionally routing protocols are designed to cope with routing operation but in practice they may be affected by misbehaving nodes so that they try to disturb the normal routing operations by launching different attacks with the intention to minimize or collapse the overall network performance. Therefore detecting a trusted node means ensuring authentication and securing routing can be expected. In this article we have proposed a Trust and Q-learning based Security (TQS) model to detect the misbehaving nodes over Ad Hoc On Demand Distance-Vector (AODV) routing protocol. Here we avoid the misbehaving nodes by calculating an aggregated reward, based on the Q-learning mechanism by using their historical forwarding and responding behaviour by the way misbehaving nodes can be isolated.
AUTHENTICATION USING TRUST TO DETECT MISBEHAVING NODES IN MOBILE AD HOC NETWO...IJNSA Journal
Providing security in Mobile Ad Hoc Network is crucial problem due to its open shared wireless medium,
multi-hop and dynamic nature, constrained resources, lack of administration and cooperation.
Traditionally routing protocols are designed to cope with routing operation but in practice they may be
affected by misbehaving nodes so that they try to disturb the normal routing operations by launching
different attacks with the intention to minimize or collapse the overall network performance. Therefore
detecting a trusted node means ensuring authentication and securing routing can be expected. In this
article we have proposed a Trust and Q-learning based Security (TQS) model to detect the misbehaving
nodes over Ad Hoc On Demand Distance-Vector (AODV) routing protocol. Here we avoid the misbehaving
nodes by calculating an aggregated reward, based on the Q-learning mechanism by using their historical
forwarding and responding behaviour by the way misbehaving nodes can be isolated.
Routing and Security Issues for Trust Based Framework in Mobile Ad Hoc Networksiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document discusses routing and security issues for trust-based frameworks in mobile ad hoc networks (MANETs). It begins by defining MANETs and explaining that routing protocols play an important role in discovering optimal paths between nodes. However, the dynamic and unpredictable nature of MANETs makes routing difficult. The document then examines security issues for MANETs including attacks from malicious nodes. It argues that establishing trust between nodes is important for routing and security. However, existing trust-based routing proposals have issues that are not clearly addressed, such as how to calculate and establish trust between nodes. The document concludes by stating that addressing these open issues could help develop more efficient and robust trust-based routing protocols for MANETs.
TRUST ORIENTED SECURITY FRAMEWORK FOR AD HOC NETWORKcscpconf
An ad hoc network is a group of wireless mobile hosts that are connected momentarily through
wireless connections in the dearth of any centralized control or some supporting services. The
mobile ad hoc network is at risk by its environment because of the vulnerabilities at channel and
node level. The conventional security mechanisms deals with only protecting resources from unauthorized access, but are not capable to safeguard the network from who offer resources. Adding trust to the on hand security infrastructures would improvise the security of these environments. A trust oriented security framework for adhoc network using ontological engineering approach is proposed by modeling ad hoc network, the OLSR (Optimized Link State Routing) protocol and trust model as OWL (Ontology Web language) ontologies, which are integrated using Jena. In this model, a trustor can calculate its trust about trustee and use the calculated trust values to make decisions depending on the context of the application or interaction about granting or rejecting it. A number of experiments with a potential implementation of suggested framework are performed to validate the characteristics of a trust oriented model suggested by the literature by this framework
Classification and review of security schemesHabitamuAsimare
Mobile computing systems face security challenges due to their vulnerability. This document analyzes security schemes for mobile computing, classifying approaches for mobile ad hoc networks (MANETs) and mobile agents. For MANETs, threats include denial of service attacks and routing attacks. For mobile agents, protecting agents from hostile hosts is difficult. The taxonomy highlights contributions to address different attack types and approaches, identifying limitations and open issues to better secure mobile networks.
Elimination of Malicious Node by using Clustering Technique in Mobile Ad Hoc ...IRJET Journal
This document discusses techniques for eliminating malicious nodes in mobile ad hoc networks (MANETs) through clustering. Specifically, it proposes finding a single trusted cluster head node based on both transmission range and highest energy, rather than just highest energy, in order to avoid selecting malicious nodes as cluster heads. The proposed approach is tested through simulation in NS2, showing improved network performance compared to existing techniques. Distributed denial of service (DDoS) attacks pose a challenge in MANETs due to their open nature, and trust-based routing protocols have been developed to help mitigate such attacks by identifying malicious or untrusted nodes.
This document summarizes a research paper that proposes a new correlation approach based on watermarking to identify the source of attacks that occur through intermediary nodes. The approach embeds watermarks in encrypted network flows by slightly adjusting the timing of selected packets. It is designed to be robust against timing perturbations intentionally introduced by attackers. Experimental results show that the watermark-based approach achieves close to 100% true positives in identifying the source of attacks.
1. The document proposes a threat modeling approach called "randomized seeding attack Model" to prevent attacks that could affect virtual machines in the cloud.
2. The model uses Fibonacci and Lucas number series to represent how attacks could randomly spread from one virtual machine to others in the cloud environment.
3. Key aspects of the model include using random Fibonacci sequences to represent the spreading of attacks, where each new virtual machine affected is represented by the next number in the sequence.
An exaustive survey of trust models in p2 p networkijwscjournal
Most of the peers accessing the services are under the assumption that the service accessed in a P2P
network is utmost secured. By means of prevailing hard security mechanisms, security goals like
authentication, authorization, privacy, non repudiation of services and other hard security issues are
resolved. But these mechanisms fail to provide soft security. An exhaustive survey of existing trust and
reputation models in P2P network regarding service provisioning is presented and challenges are listed.
Trust issues like trust bootstrapping, trust evidence procurement, trust assessment, trust interaction
outcome evaluation and other trust based classification of peer’s behavior into trusted,, inconsistent, un
trusted, malicious, betraying, redemptive are discussed,
Review of Security Issues in Mobile Wireless Sensor NetworksEswar Publications
MWSNs are finding applicability in wide range of applications. Applications spread from day to day utilities to military and surveillance, where they may sense information about vehicular movements around border. Considering the importance of data being sent by these nodes, threat of compromising them has also increased. This paper aims to explore various types of attacks and tries to classify them based on some common parameter. Better understanding of various attacks, their style of functioning and point of penetration can help researchers devise better preventive measures.
Secure intrusion detection and attack measure selectionUvaraj Shan
This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.
PROTECTING PRIVACY IN VANETs USING MIX ZONES WITH VIRTUAL PSEUDONYM CHANGE IJNSA Journal
This document summarizes a research paper that proposes a technique for securely changing pseudonyms in vehicular ad hoc networks (VANETs) to enhance privacy. The technique uses "mix zones", predefined regions where vehicles can change pseudonyms. It introduces "virtual pseudonym changes" using transceivers if real vehicles are insufficient. Transceivers mimic pseudonym changes to increase complexity for adversaries trying to link pseudonyms. The technique calculates mapping weights between zones to determine when virtual changes are needed. It aims to guarantee high privacy even with low traffic by obscuring pseudonym linkages.
Adaptive Circumstance Knowledgeable Trusted System for Security Enhancement i...IJTET Journal
Every MANET application has its own policy and they need some special policies to enhance the security. In
MANET, each node acts as the router. The main challenging of the MANET setting up routing paths through the legitimate
nodes only. To make the MANET as the trusted system some external policies or schemes are needed. However, whether
for malicious or selfish purposes, a node may not cooperate during the network events or even try to interrupt them, both
are consider as misbehaviors. Substantial analysis efforts have been made to finding misbehaviors. Both the faulty
behaviors and malicious behavior are generally equally treated as misbehaviors without any further analysis by most of the
malicious behavior detection mechanisms. In this paper, propose the Adaptive Circumstance Knowledgeable trusted
framework, in which various contextual information, such as battery status weather condition and communication channel
status, are used to identify whether the misbehavior is a result of malicious activity or not.
Effectual Routine for Trilateral Authentication in Ad-hoc Networks using Mult...IOSR Journals
This document proposes a protocol for trilateral authentication in ad-hoc networks using multicast conventions. It introduces a central authority that manages key authentication and certification to increase security and reliability. Nodes are grouped into clusters, each with a cluster head. For similar clusters, authentication uses time asymmetry based on TESLA. For cross-cluster traffic, it uses secret information asymmetry where the source sends packets to cluster heads, which relay to members. Evaluation shows the central authority uses less memory than previous methods and the protocol has higher efficiency.
IRJET- A Confidence Model based Routing Practice for Secure Adhoc NetworksIRJET Journal
This document proposes a Trusted AODV (TAODV) routing protocol to secure ad hoc networks. TAODV extends the AODV routing protocol and uses a trust model to represent trust relationships between nodes. In TAODV, a node's trust in another is represented by an opinion that considers belief, disbelief, and uncertainty. Opinions are updated over time based on successful or failed communications. TAODV introduces a trust recommendation mechanism to exchange trust information between nodes. The protocol performs trusted routing discovery and maintenance based on nodes' trust opinions of one another to improve security and efficiency compared to cryptographic approaches.
Evasion Streamline Intruders Using Graph Based Attacker model Analysis and Co...Editor IJCATR
Network Intrusion detection and Countermeasure Election in virtual network systems (NICE) are used to establish a
defense-in-depth intrusion detection framework. For better attack detection, NICE incorporates attack graph analytical procedures into
the intrusion detection processes. We must note that the design of NICE does not intend to improve any of the existing intrusion
detection algorithms; indeed, NICE employs a reconfigurable virtual networking approach to detect and counter the attempts to
compromise VMs, thus preventing zombie VMs. NICE includes two main phases: deploy a lightweight mirroring-based network
intrusion detection agent (NICE-A) on each cloud server to capture and analyze cloud traffic. A NICE-A periodically scans the virtual
system vulnerabilities within a cloud server to establish Scenario Attack Graph (SAGs), and then based on the severity of identified
vulnerability toward the collaborative attack goals, NICE will decide whether or not to put a VM in network inspection state. Once a
VM enters inspection state, Deep Packet Inspection (DPI) is applied, and/or virtual network reconfigurations can be deployed to the
inspecting VM to make the potential attack behaviors prominent.
EXPOSURE AND AVOIDANCE MECHANISM OF BLACK HOLE AND JAMMING ATTACK IN MOBILE A...ijcseit
Mobile ad hoc network (MANETs) is an infrastructure-less/self-configurable system in which every node
carries on as host or router and every node can participate in the transmission of packets. Because of its
dynamic behaviour such system is more susceptible against various sorts of security threats, for example,
Black hole, Wormhole , Jamming , Sybil, Byzantine attack and so on which may block the transmission of
the system. Black hole attack and Jamming attack is one of them which promote itself has shortest or new
fresh route to the destination while jamming attack which make activity over the system. This paper
introduces the thorough literature study for the Black hole attack and jamming attack of both the attack by
various researchers.
An Optimal Risk- Aware Mechanism for Countering Routing Attacks in MANETsIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
For enhancing VANET security, an autonomic trust and reputation monitoring scheme is proposed which uses a trust based data transfer protocol based on reputation and plausibility checks. The proposed framework uses autonomic principles and trust monitoring scheme to provide uniform trust information throughout the network with minimum overhead. It aims to reduce the impact of double-face attacks by isolating selfish and malicious nodes. The scheme generates local and global trust tables to evaluate node trustworthiness and identify malicious nodes for isolation from the network.
This document summarizes a research paper that proposes a new trust management scheme to enhance security in mobile ad hoc networks (MANETs). The scheme uses uncertain reasoning to evaluate trust values based on direct observation from a node and indirect observation from neighboring nodes. Trust is evaluated using Bayesian inference for direct observation and Dempster-Shafer theory for indirect observation. Simulation results show the scheme improves throughput, packet delivery ratio, and security with a slight increase in delay and overhead compared to the standard Ad Hoc On-Demand Distance Vector (AODV) routing protocol.
AUTHENTICATION USING TRUST TO DETECT MISBEHAVING NODES IN MOBILE AD HOC NETWO...IJNSA Journal
Providing security in Mobile Ad Hoc Network is crucial problem due to its open shared wireless medium, multi-hop and dynamic nature, constrained resources, lack of administration and cooperation. Traditionally routing protocols are designed to cope with routing operation but in practice they may be affected by misbehaving nodes so that they try to disturb the normal routing operations by launching different attacks with the intention to minimize or collapse the overall network performance. Therefore detecting a trusted node means ensuring authentication and securing routing can be expected. In this article we have proposed a Trust and Q-learning based Security (TQS) model to detect the misbehaving nodes over Ad Hoc On Demand Distance-Vector (AODV) routing protocol. Here we avoid the misbehaving nodes by calculating an aggregated reward, based on the Q-learning mechanism by using their historical forwarding and responding behaviour by the way misbehaving nodes can be isolated.
AUTHENTICATION USING TRUST TO DETECT MISBEHAVING NODES IN MOBILE AD HOC NETWO...IJNSA Journal
Providing security in Mobile Ad Hoc Network is crucial problem due to its open shared wireless medium,
multi-hop and dynamic nature, constrained resources, lack of administration and cooperation.
Traditionally routing protocols are designed to cope with routing operation but in practice they may be
affected by misbehaving nodes so that they try to disturb the normal routing operations by launching
different attacks with the intention to minimize or collapse the overall network performance. Therefore
detecting a trusted node means ensuring authentication and securing routing can be expected. In this
article we have proposed a Trust and Q-learning based Security (TQS) model to detect the misbehaving
nodes over Ad Hoc On Demand Distance-Vector (AODV) routing protocol. Here we avoid the misbehaving
nodes by calculating an aggregated reward, based on the Q-learning mechanism by using their historical
forwarding and responding behaviour by the way misbehaving nodes can be isolated.
Routing and Security Issues for Trust Based Framework in Mobile Ad Hoc Networksiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document discusses routing and security issues for trust-based frameworks in mobile ad hoc networks (MANETs). It begins by defining MANETs and explaining that routing protocols play an important role in discovering optimal paths between nodes. However, the dynamic and unpredictable nature of MANETs makes routing difficult. The document then examines security issues for MANETs including attacks from malicious nodes. It argues that establishing trust between nodes is important for routing and security. However, existing trust-based routing proposals have issues that are not clearly addressed, such as how to calculate and establish trust between nodes. The document concludes by stating that addressing these open issues could help develop more efficient and robust trust-based routing protocols for MANETs.
Mitigating Various Attacks in Mobile Ad-hoc Networks Using Trust Based ApproachIJLT EMAS
A Mobile ad hoc network (MANET) is self-organizing,
decentralized and infrastructure-less wireless network. The
successful transmission of the data packet depends on the
complete cooperation of each node in the network. These types of
network don’t have permanent base station, so each node in the
network acts as a router. Due to openness, decentralized, selforganizing
nature of MANET, it is vulnerable to various attacks.
So security is the main concern in MANET.
In this project, we have considered 2 attacks; Vampire
attack and DDoS attacks. Vampire attack drains the energy of
the nodes. DDoS attack exhausts the resources available to a
network, such that the node cannot provide any services. Here,
we discuss methods 2 methods as a solution to our problem; one
is to prevent the attack from happening and other to detect and
recover from the attacks.
Detecting Various Black Hole Attacks by Using Preventor Node in Wireless Sens...IRJET Journal
This document discusses detecting black hole attacks in wireless sensor networks. It begins with an abstract that introduces black hole attacks as a security threat in mobile ad hoc networks (MANETs) where malicious nodes drop packets. The document then reviews previous work on defending against black hole attacks, including using trust values, dummy nodes, and sequence number verification. It proposes using a "preventor node" to create a secure environment and detect black hole attacks in MANETs to improve network performance and security.
FLOODING ATTACKS DETECTION OF MOBILE AGENTS IN IP NETWORKScsandit
This document summarizes a research paper that proposes a new framework for detecting flooding attacks in mobile agent networks. The framework integrates divergence measures like Hellinger distance and Chi-square over a sketch data structure. The sketch data structure is used to derive probability distributions from traffic data in fixed memory. Divergence measures compare the current and prior probability distributions to detect deviations indicating attacks. The performance of detecting attacks while minimizing false alarms is evaluated using real network traces with injected flooding attacks. Experimental results show the proposed approach outperforms existing solutions.
This document discusses trust-based routing in mobile ad hoc networks (MANETs). It provides an overview of several trust management approaches that have been proposed for MANET routing:
1. A direct and indirect trust formation approach that calculates trust values based on a node's own observations and recommendations from peer nodes. This allows resources to be shared only among trusted nodes.
2. A hybrid trust management framework (HTMF) that more robustly evaluates trust based on direct observations and second-hand information. This makes it resistant to certain attacks.
3. An adaptive multi-level trust (AMLeT) framework that calculates two complementary trust levels - hard and soft trust - depending on security needs. It introduces
Cluster Based Misbehaviour Detection and Authentication Using Threshold Crypt...CSCJournals
In mobile ad hoc networks, the misbehaving nodes can cause dysfunction in the network resulting in damage of other nodes. In order to establish secure communication with the group members of a network, use of a shared group key for confidentiality and authentication is required. Distributing the shares of secret group key to the group members securely is another challenging task in MANET. In this paper, we propose a Cluster Based Misbehavior Detection and Authentication scheme using threshold cryptography in MANET. For secure data transmission, when any node requests a certificate from a cluster head (CH), it utilizes a threshold cryptographic technique to issue the certificate to the requested node for authentication. The certificate of a node is renewed or rejected by CH, based on its trust counter value. An acknowledgement scheme is also included to detect and isolate the misbehaving nodes. By simulation results, we show that the proposed approach reduces the overhead.
Security Enhancement using Trust Management in MANETsIJTET Journal
Abstract— The distinctive options of mobile ad hoc networks (MANETs), victimization recent advances in unsure reasoning originated from AI community, we tend to projected a unified trust management schemes Mobile Ad-hoc networks area unit self-organizing and self re-configuring multi hop wireless networks wherever, the structure of the network changes dynamically the safety of the OLSR protocol is rib by a selected variety of attack known as ‗Black Hole‘ attack. During this attack a malicious node advertises itself as having the shortest path to the destination node. To combat with region attack, it\'s projected to attend and check the replies from all the neighboring nodes to search out a secure route with protection to our data, however this approach suffers from high delay. Associate in Nursing approach is projected to combat the region attack by victimization Trust management schemes with neighbors WHO claim to possess a route to destination. during this project we tend to area unit victimization NS2.34 software system for our projected model testing. and that we got the simplest result against the safety attack.
EXPOSURE AND AVOIDANCE MECHANISM OF BLACK HOLE AND JAMMING ATTACK IN MOBILE A...ijcseit
This document summarizes previous work on detecting and preventing black hole attacks and jamming attacks in mobile ad hoc networks (MANETs). It first describes the mechanisms of black hole attacks and jamming attacks in MANETs. It then reviews several papers that have proposed methods for detecting black hole attacks using techniques like watchdog mechanisms and anomaly detection. It also summarizes a paper that introduced a novel method for detecting and preventing black hole attacks to securely route packets to destinations. Finally, it provides an overview of different jamming attack strategies such as persistent, deceptive, random, and reactive jamming.
PERFORMANCE ANALYSIS OF THE NEIGHBOR WEIGHT TRUST DETERMINATION ALGORITHM IN ...IJNSA Journal
Mobile ad-hoc networks (MANETs) are susceptible to attacks by malicious nodes that could easily bring down the whole network. Therefore, it is important to have
a reliable mechanism for detecting and isolating malicious nodes before they can do any harm to the network. One of the possible mechanisms is by using trust-based routing protocols. One of the main requirements of such protocols is to have a cost-effective trust determination algorithm. This paper presents the performance analysis of a recently developed trust determination algorithm, namely, the neighbor-weight trust determination (NWTD) algorithm. The performance of the algorithm is evaluated through simulation using the MANET simulator (MANSim). The simulation results demonstrated the reliability and effectiveness of the algorithm in identifying and isolating any maliciously behaving node(s) in a timely manner.
PERFORMANCE ANALYSIS OF THE NEIGHBOR WEIGHT TRUST DETERMINATION ALGORITHM IN ...IJNSA Journal
Mobile ad-hoc networks (MANETs) are susceptible to attacks by malicious nodes that could easily bring
down the whole network. Therefore, it is important to have a reliable mechanism for detecting and isolating
malicious nodes before they can do any harm to the network. One of the possible mechanisms is by using
trust-based routing protocols. One of the main requirements of such protocols is to have a cost-effective
trust determination algorithm. This paper presents the performance analysis of a recently developed trust
determination algorithm, namely, the neighbor-weight trust determination (NWTD) algorithm. The
performance of the algorithm is evaluated through simulation using the MANET simulator (MANSim). The
simulation results demonstrated the reliability and effectiveness of the algorithm in identifying and
isolating any maliciously behaving node(s) in a timely manner.
PERFORMANCE ANALYSIS OF THE NEIGHBOR WEIGHT TRUST DETERMINATION ALGORITHM IN ...IJNSA Journal
Mobile ad-hoc networks (MANETs) are susceptible to attacks by malicious nodes that could easily bring down the whole network. Therefore, it is important to have a reliable mechanism for detecting and isolating malicious nodes before they can do any harm to the network. One of the possible mechanisms is by using trust-based routing protocols. One of the main requirements of such protocols is to have a cost-effective trust determination algorithm. This paper presents the performance analysis of a recently developed trust determination algorithm, namely, the neighbor-weight trust determination (NWTD) algorithm. The performance of the algorithm is evaluated through simulation using the MANET simulator (MANSim). The simulation results demonstrated the reliability and effectiveness of the algorithm in identifying and isolating any maliciously behaving node(s) in a timely manner.
MANET (Mobile Ad-hoc Network) is hot spot for research due to its various advantages and
disadvantages. Providing safe communication between mobile nodes, recognization the position of
nodes, reducing overhead, handling misbehavior and location updates are such a difficult issues in
ad-hoc network, so providing trust schemes is an important in this network. MANET provides some
basic functions like routing, communication, network management and packet forwarding etc over
self organized network. Because MANET has not a fixed topology, in which mobile nodes comes and
leaves the network within a random period of time. It effects energy, bandwidth and memory
computations of network. Providing trust in MANET is such a crucial task because it doesn’t having
centralized infrastructure. In this paper, we survey the different trust model schemes of MANET with
their unique features, merits and demerits & findings.
A SECURE CLUSTER BASED COMMUNICATION IN WIRELESS NETWORK USING CRYPTOGRAPHIC ...IJNSA Journal
Mobile Adhoc Networks are becoming very popular in current Wireless Technology, which is been
associated to business, socially and in some critical applications like Military etc, The network which is
formed by self configuring wireless links which are connected to each other. These applications are
categorized by hostile environment that they serve while communicating between nodes. However in such
Wireless Network will be more exposed to different types of security attacks. The challenge is to meet
secure network communication. In this paper we focus on cluster based secure communication to improve
the reliability between clusters. In this scheme the Cluster Members (CM) submits a report to the Cluster
Head (CH) and temporarily stores Evidences as a security tokens. The reports contain digital signatures.
The CH will verify the consistency of the CM report and updates to Accounting Centre (AC). AC will verify
the uniformity of reports and clears the cryptographic operations. For attacker nodes, the security tokens
are requested to classify and expel the attacker nodes which submit wrong reports.
A SECURE CLUSTER BASED COMMUNICATION IN WIRELESS NETWORK USING CRYPTOGRAPHIC ...IJNSA Journal
Mobile Adhoc Networks are becoming very popular in current Wireless Technology, which is been associated to business, socially and in some critical applications like Military etc, The network which is formed by self configuring wireless links which are connected to each other. These applications are categorized by hostile environment that they serve while communicating between nodes. However in such Wireless Network will be more exposed to different types of security attacks. The challenge is to meet secure network communication. In this paper we focus on cluster based secure communication to improve the reliability between clusters. In this scheme the Cluster Members (CM) submits a report to the Cluster Head (CH) and temporarily stores Evidences as a security tokens. The reports contain digital signatures. The CH will verify the consistency of the CM report and updates to Accounting Centre (AC). AC will verify the uniformity of reports and clears the cryptographic operations. For attacker nodes, the security tokens are requested to classify and expel the attacker nodes which submit wrong reports.
Trustworthy Service Enhancement in Mobile Social Networkijsrd.com
Mobile Social Network is network which allows mobile users to discover and interact with existing and potential friends. A Trustworthy Service Evaluation (TSE) system is a system that enables users to share service reviews in Service oriented mobile social networks (S-MSNs). Each service provider should independently maintain a TSE for itself that collects and stores users’ reviews about its services without requiring any third trusted authority. These service reviews can be made available to interested users to make service selection decisions. In this three unique service review attacks are identified, i.e., link ability, rejection, and modification attacks, and then develop security mechanisms for the TSE to deal with these attacks. In this we extend the bTSE(basic TSE) to a Sybil-resisted TSE (SrTSE) which enable the detection of two typical sybil attacks. In SrTSE if a user generates multiple reviews toward a vendor in a predefined time slot with different pseudonyms, the real identity of that user will be revealed. Hence a Trustworthy Service in Mobile Social Network is introduces so that users can access services securely.
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SECURING MOBILE AGENTS IN MANET AGAINST ATTACKS USING TRUST
1. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
DOI : 10.5121/ijnsa.2011.3620 259
SECURING MOBILE AGENTS IN MANET
AGAINST ATTACKS USING TRUST
ChandreyeeChowdhury *SarmisthaNeogy
Dept. of Computer Scienceand Engineering
Jadavpur University
*sarmisthaneogy@gmail.com
ABSTRACT.
The emerging trend of using mobile agents for mobile adhoc network (MANET) applications intensifies
the need for protecting them. Here we propose a distributed trust based framework to protect both the
agents and the host platforms (running at the nodes) especially against threats of the underlying
environment where agents may get killed or rerouted by visiting hosts. The best way to defend against
this situation is to prevent both the hosts and agents from communicating with the malicious ones. In this
regard this paper develops a distributed reputation model of MANET using concepts from Dempster-
Shafer theory. The agents (deployed for some purposes like service discovery) while roaming in the
networkwork collaboratively with the hosts they visit to form a consistent trust view of MANET. An agent
may exchange information about suspected nodes with a visiting host. To speed up convergence,
information about an unknown node can be solicited from trusted neighborhood. Thus an inactive node,
without deploying agents may also get a partial view of the network. The agents can use combination of
encryption and digital signature to provide privacy and authentication services. Node mobility and the
effect of environmental noise are considered. The results show the robustness of our proposed scheme
even in bigger networks.
KEYWORDS
Mobile Agent, Security, Hashcode, Trust, Dempster–Shafer Belief Theory
1. INTRODUCTION
Nowadays mobile agent seems to be a popular choice for designing applicationslike service
discovery, network discovery, automatic network reconfiguration etc. for resource constrained
environments like mobile adhoc networks (MANET).Many a time task processing is taken up
by mobile agents that roam in the network and consequently get the task done.
But securing agents is a big concern particularly when the underlying network typically
undergoes continuous topology changes thereby disrupting flow of information over the
existing paths.As has been pointed out in [1] security of a mobile agent paradigm emphasizes
on protecting and preventing a mobile agent from malicious hosts’ attacks by applying
cryptographic functions. Unfortunately these countermeasures become insufficientwhen the
environment itself brings with it much vulnerability like blackhole[2], grayhole[2] or
wormhole[2] attack. Commonly used routing protocols[2] cannot prevent such attacks. In such
cases, the agents are either engulfed by a host (blackhole) or is forwarded
elsewhere(wormhole). But in either case the agents will never be able to come back to its owner
in due time.Thus an agent if happens to pass through such a host will effectively be lost.
2. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
260
Howeverpreventing a mobile agent from visiting a malicious node solves most of the risk
factors. Consequently this technique not only protects the agents but also its owner that is the
nodes in MANET. This point onwards, the terms node and host are used interchangeably unless
otherwise stated.
Our threat model is as follows. We assume that the adversary can place malicious
(wormhole/blackhole/grayhole) nodes at arbitraryplaces in the network, and that these nodes are
connectedthrough a communication channel that cannot be observed by othernodes. These
nodes either kill or mislead the agents in such a way that they (agents) never come back to their
owners in time (within a specified time-out limit). The agents (by using a combination of
hashcode and digital signature (as in [3]))can successfully detect any attempt of changing its
code upon reaching at a host site. Thus the attacker does not need to know details of above
mentioned techniques to fool the nodes to believe that their agents are lost due to adverse
MANET conditions.We assume agents are deployed by some distributed applications like
service discovery or clustering etc where graceful degradation in performance (as some nodes
may become malicious) is acceptable. If an agent needs to visit a particular node in MANET (as
in e-commerce) and that node is corrupted then obviously the task cannot be completed in our
model, even.
To enforce, we use the concept of trust that has received considerable attention in information
security literature. In a way, trust and security are two sides of the same coin, because if a
system is secure, it is trusted, and if it istrusted, then it must be secure and vice-versa [4].
This observation leads us to consider security as a property of asystem in a given environment,
and trust as a subjective belief resulting fromassessing a system and its environment. As in [1]
we define trust as a subjective quantified predictor of the expected future behaviour of a trustee
according to a specific agreement elicited from theoutcomes of the previous interactions, both
from direct experiences and indirect experiences. Reputation of an individual host refers to
certain characteristics related to its trustworthiness. Reputation can be obtained from a set of
interaction feedbacks, in a mobile agent system; wheremobile agents describe a visited host’s
performance in fulfilling its obligations. Indirect experiences can also be considered which is
gathered from other trustworthy nodes in the neighbourhood. To speed up convergence, the list
of suspicious nodes may be shared among the nodes in MANET via the agents.
In this paper we describe a trust based framework for mobile agent based system in a dynamic
and hostile MANET environment. We show how cooperative behaviour of the agents and nodes
help to secure MANET and prevent an agent from getting trapped into a suspicious
neighbourhood.Thus our definition of trust may range from complete belief to complete
disbelief to full uncertainty as well as is described in section 3. This section also describes the
basis of different types of observations done by the agents and consequently the nodes.
The next section(section 4) illustrates the way we model mobile agents on MANET in order to
detect a malicious agent as well as a malicious platform (depending on trust level defined later)
in a distributed way (using the reputation system designed in section 3).Section 5 gives the
experimental resultsto show the robustness of our schemefollowed by concluding remarks (in
section 6). In the following section (2), state of the art regarding this area of research is
elaborated.
3. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
261
2. RELATED WORKS
This section summarizes the literature related to trust management schemes in MANETs and
mobile agent based systems.
2.1. Trust Management in MANET
Trust-based data routing has been extensively studied inwireless networks including MANETs
[5][6][7][8]. The basic framework of a Trust Management System (TMS) includes a Reputation
System (RS)and a Watchdog. Generally, the RS consists of reputationupdating through direct
observation of the Watchdog (that is,first-hand information), reputation integration based on
theindirect information from other members (i.e., second-handinformation), and reputation
aging. The watchdogs normally monitor the event of data forwarding and count the arrival of
ACKs corresponding to data sent out/forwarded. To cope with mobility, in [6] multiple
feedbacks are compressed together. But using mobile agents for this purpose (which can
already be deployed for functions like service discovery, clustering MANET etc.) will yield far
better results as mobile agents are designed in such a way that they can easily cope with
frequent disconnections and limited bandwidth characterizing MANET especially delay tolerant
networks [6]. In [5] it is shown that mobility reduces uncertainty in trust calculation as it
increases the chance of directly interacting with a node.
2.2. Trust in Mobile Agent Based Systems
Trust management system for mobile agents is also well studied [1] in literature. In [9] a
distributed reputation management model is proposed that is based on Dempster-Shafer theory
of evidence. This system solves some of the problems in e – bay‘s reputation model taking
deceptive ratings into account. A trust model is described in [10] for multi-agent systems
(MAS) that considers information collected from several sources (interaction trust, witness
reputation, role based trust and certified reputation). It also usesDempster-Shafer theory of
evidence. In [11], a mobile agent based reputation management system has been proposed for e-
business environments. The system uses direct interactions and feedback from customers in a
social network using agents, where each customer models trustworthiness of a vendor. The
modelling of trustworthiness is done using Dempster -Shafer evidential theory, fuzzy logic. But
this model does not fit into a resource constrained dynamic environment like MANET. In [1], a
reputation-based trust model is proposed for mobile agents. Bayesian Network based trust
computing is used and two algorithms are proposed for strategically malicious trustee
prevention.
But most of these works are not focused on MANET and so the effect of dynamic topology
changes, noisy environments, and more importantly mobility, are not considered in these works.
Thus, securing mobile agents and nodes in MANET by using the notion of trust is a
comparatively new research paradigm. More importantly, assumption of a trusted third party or
a trusted server and with 100% availability is practically not feasible in MANET. So the
approaches based on a fully trusted node renders useless in resource constrained dynamic
environments like MANET.
Although some works have been done to detect blackholeattack [12] or even wormhole
attack[13] in MANET but we did not come across any work that studies its effect on agents in
MANET or uses the agents to detect such traps.
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262
3. TRUST MODEL
A reputation system [14] represents a promising method for fostering trust amongcomplete
strangers and for helping each individual to adjust or update its degree oftrust towards its
corresponding interaction partner and thereby reduce uncertainty.But a reputation system also
suffers from threats like strategic rater [15] or strategically malicious host [14]. We attempt to
address these threats by taking opinions from the agents and peers.
The main focus of our work is to study and prevent the nodes and agents (deployed by them) in
a MANETfrom the effect of network layer attacks like blackhole[2] or wormhole[2] in which
the affected agents do not come back to their owner.
Due to the inherent distributed nature of MANET nodes can only have imperfect knowledge
about others. Thus it is impossible to know with certainty whether a host is malicious or not;
but we can only have an opinion about it, which translates into degrees of belief (how much
trustworthy a host is) or disbelief (how much suspected a host is) as well as uncertainty in case
both belief and disbelief are lacking. We express this mathematically [4] as:
b+d+u=1 (1)
Here b, d, u designate belief, disbelief and uncertainty respectively.
The design of our reputation system is shown in figure 1. It focuses on how to exploit the
collected information to quantify the reputation of a node to ensure that an agent never falls into
a blackhole, grayhole[2] or even wormhole trap. In addition, digital signature may be used to
prevent or at least detect any attempt to change static code of the agent[16]. To quantify trust,
parameters (b, d and u) are updated from direct observations (agent’s experience at different
nodes) and indirect observations (feedback from neighbouring nodes and others, collected via
agents). Both observations are combined towards quantifying trust from (b, d and u). Aging is
also considered in the process that accounts for network dynamicity.
3.1. Direct Observation
As mentioned in [16] each agent may carry the following
Figure1.Trust evaluation framework at hosts taking feedbacks
Query Results
from Trusted
Neiighbourhood
Agent Feedback
about nodes they
visited
Reputation
Systemi
(b, d, u)
Direct
Observation
Indirect Observation
Aging
Trust
evaluation for
future agent
deployments
j
k
Feedback about
suspected nodes
collected by agents
Indirect Observation
j
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SIGNATUREowner(code for hashcode computation+ static application code) + dynamic code(if
any) + data
Here application code of agent refers to the purpose for which it is deployed by its owner. The
signature helps to authenticate the code and checks trustworthiness of the host. The dynamic
code part will be meaningful for strong migration [17] which is uncommon in MANET. In the
end, every agent shares its experience with the owner. Thus we assume that an agent eventually
finds its owner whenever it needs. Here we take Beta(α,β) distribution as in [5][16].αijrepresents
the number of good transactions between the agents deployed by owneriand nodej. Thus for
each positive feedback from agents, αijis incremented as follows:
ߙሺ௪ሻ = ߱ ∗ ߙሺௗሻ + ሺ1 − ߱ሻ ∗
(2)
Here pj
k
represents agentk’s observation about nodej. In this case weighted average is taken,
where ω (0<ω<1) represents the absolute trust on each agent’s observation as this observation
may change from time to time taking care of network dynamicity.
Moreover, it may so happen that an agent successfully visits a number of hosts before falling
into a trap. So, every node maintains last L owner ids that sent agents to this node. Thus if
agentk while visiting nodej finds its owner id in L (indicating some agent from the same owner
has recently visited this node) then it further increments pj
k
in equation2 accordingly.
An agent may not come back to its owner in time (time-out) due to network latency or presence
of wormhole or blackhole. But to detect the exact cause, the owner divides the task into n
subtasks (value of n depends on network bandwidth) and deploys n agents. These agents are
expected to come back faster and reveal more information about the network. Now if anagentk
does not come back, the owneri increments βij as follows
ߚሺ௪ሻ = ߱ ∗ ߚሺௗሻ + ሺ1 − ߱ሻ ∗
(3)
Here j represents all nodes that the part agentk needs to visit in order to complete the subtask
given to it. But βij may not reflect the exact scenario as a node can be strategically malicious
[14]. Thus there is an uncertainty associated with the agent’s observation. To deal with such
issue, an approach proposed in [5], leveraging on the Dempster–Shafer Belief Theory [18] is
adopted here to quantify the uncertainty of some random variables.
Thus the uncertainty in predicting the nature of nodej by nodei is [6][3]:
u୧୨ =
12 ∗ α୧୨ ∗ β୧୨
ሺα୧୨ + β୧୨ሻଶ ∗ ሺ1 + α୧୨ + β୧୨ሻ
ሺ4ሻ
An agent while visiting a host site may also share and update its suspected list with the host.
Any appended entry to the list will be considered as indirect observation at the agent owner.
This is done to prevent a node from having any deceptive information.
The values of αij and βij are fed to the reputation system that maps these to a tuple (bij, dij, uij).
Here bij gives nodei’s belief in nodej’s behavior as safe host site for agents deployed by nodei.
Similarly dij indicates nodei’s disbelief and uij reflects nodei’s uncertainty of predicting nodej as
a safe host site for its agents. Here uij is calculated using equation 4. Consequently following
equation 1, the total certainty (= (1-uij)) is divided into bij and dij according to their proportion of
supporting evidence as follows (initial observation is based on[5]):
6. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
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ܾሺtሻ =
∝
∝+ ߚ
൫1 − ݑ൯ initially
∝ೕ
∝ೕାఉೕ
൫1 − ݑ൯ ∗ ωଵ + ܾሺt − ∆tሻ ∗ ωଶ
ωଵ + ωଶ
otherwise
ሺ5ሻ
݀ሺݐሻ =
ߚ
∝+ ߚ
൫1 − ݑ൯ initially
ఉೕ
∝ೕାఉೕ
൫1 − ݑ൯ ∗ ωଵ + ݀ሺݐ − ∆ݐሻ ∗ ωଶ
ωଵ + ωଶ
otherwise
ሺ6ሻ
Averaging (weighted) is needed to reflect n part agents’ behavior in the same tuple (bij, dij,
uij).New observation is given a weight of ω1 and old observation is given a weight of ω2.Thus
old values of bij and dij are given lesser weights(ω2< ω1) than recent values to represent aging.
In this way with the help of Dempster–Shafer Belief Theory [18] uncertainty can
significantly be reduced even though perfect accuracy could not be achieved.
3.2. Indirect Observation
For faster convergence of trust, nodes share information about suspicious node/samong each
other via the agents. A node is suspected if its b<u<d. This information indirectly influences a
node’s view of the network. The influence is indirect as an agent suspects a node based on
another (preferably trusted) node’s observation without ever visiting that node.This second-
hand information helps a node to cope with long delays and frequent partitions (formation of
disconnected clusters) which are characteristics of MANET.
Let bl
i:j
represent belief (b) of nodei on nodel while taking indirect observation from nodej. So
this parameter depends on two factors-(i) nodei’s belief on nodej and (ii) nodej’sbest possible
final observation on nodel as predicted by nodeias follows
bj
l
=TrustLimit(highest value of trust for a suspected node); dj
l
=(1-TrustLimit); uj
l
=1
Following the approaches proposed in [5] (bl
i:j
,dl
i:j
,ul
i:j
) can be formulated as
ܾ
:
= ܾ
× ܾ
ሺ7ሻ
݀
:
= ܾ
× ݀
(8)
ݑ
:
= ܾ
× ݑ
+ ݀
+ ݑ
(9)
It can be noted that nodei’s disbelief in nodej’s observation becomes an uncertainty for
predicting nodel[3]. Also nodei’s uncertainty on nodej amounts to the uncertainty of nodei in
predicting nodel’s future behavior.
If nodei enters a new network and gets trapped by its very neighbours then no agents
deployed will come back. Then nodei will prefer to wait till it moves. Moreover if a significant
number of agents do not come back indicating wormhole or blackhole trap in transit, nodei will
prefer to request and collect information about suspicious nodes from its trusted neighborhood.
The neighbors respond with their final observation (bj
l
, dj
l
, uj
l
) about the nodes (all ls) they
suspect. This is updated in the same way using equations 7-9.
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Thus a node predicts about the future behavior of a node taking indirect feedbacks from all
agents in the last time interval (∆t) and updates its view (b, d, u) as follows [5]
ܾ: =
ܾ
:
|ܵ|
∈ௌ
ሺ10ሻ
݀: =
݀
:
|ܵ|
∈ௌ
ሺ11ሻ
ݑ: =
ܾ
× ݑ
+ ݀
+ ݑ
|ܵ|
∈ௌ
ሺ12ሻ
Here bi:l represents the indirect belief of nodei about nodek. S denotes the set of nodes that
shared its view of the network (that nodei received) with the agents deployed by nodeiin the last
time interval.
3.3. Combining Direct and Indirect Observation
After collecting first-hand and second-hand information from the agents/trusted
neighborhood, a node attempts to integrate them all to come to a unified conclusion about
future behavior of the nodes. Thus the comprehensive belief (bj
i(f)
), disbelief (dj
i(f)
) and
uncertainty (uj
i(f)
) of nodei on nodej are derived from the following equations, as in [5]
ܾ
ሺሻ
= ߮ଵ × ܾ + ߮ଶ × ܾ:ሺ13ሻ
d
ሺሻ
= ߮ଵ × ݀ + ߮ଶ × d: ሺ14ሻ
ݑ
ሺሻ
= 1 − ܾ
ሺሻ
− ݀
ሺሻ
ሺ15ሻ
Where
߮ଵ =
ߛ × ݑ:
ሺ1 − ߛሻ × ݑ + ߛ × ݑ: − 0.5 × ݑ × ݑ:
ሺ16ሻ
߮ଶ =
ሺ1 − ߛሻ × ݑ
ሺ1 − ߛሻ × ݑ + ߛ × ݑ: − 0.5 × ݑ × ݑ:
ሺ17ሻ
Here γ (0<γ<1) indicates a node’s confidence on the agents it deployed. Larger values of γ
(>0.5) means a node tends to trust its agents whereas smaller values (<0.5) indicates that a node
tends to trustothers’ recommendations. Now, trust can be quantified from the comprehensive
belief, disbelief and uncertainty as [4][6]
ܶ = ܾ
ሺሻ
+ ߪ × ݑ
ሺሻ
(18)
Here σ gives relative atomicity based on the principle of indifference [3]. We have taken σ to
be 0.5 indicating that among the total uncertainty associated with an agent’s visit, there is a
50% probability that the agent will be safe. But we can tune this parameter more accurately
meaning, that for higher values of disbelief, there is a possibility that σ<0.5 and vice versa.
Consequently, depending on the trust values calculated from equation (18) and the safety
requirement of the applications (running at the nodes) that deploys agents, an owner decides an
agent’s task route or asks it to avoid suspicious host sites.
8. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
266
4. IMPLEMENTING OUR MODEL ON MANET
In this paper, we define our mobile agent-based system (S) to be consisting of M independent
agents deployed by k owners that may move in the underlying MANET. To describe our model
we will take help of the abstraction of an ad hoc network as in [16]. The nodes move according
to Smooth Random Mobility Model [16] and two ray propagation [17] of radio signals is
assumed while checking for link existence. Here we try to protect mobile agents from visiting
malicious hosts (nodes) and to prevent trusted nodes from sending agents to malicious ones. We
assume the compromised nodes can send malicious agents to mislead a node about its trust
level. Also a compromised node may work as a black hole tovisitoragents.
In this scenario we can think of a mobile agent as a token visiting one node to another in the
network (if the nodes are connected) based on some strategy as needed by the underlying
applications to accomplish its task.
An important use of mobile agents is to collect data from a network like service discovery
[19] or clustering in MANET [20] or ecommerce applications [21], etc. An agent starts its
journey from a given owner and moves from one node to another at its will. The owner
provides a Priority List to the agent which contains a list of node ids that are most beneficial
migration sites (for the application that deployed that particular agent). A Suspicious Node List
is also given that indicates potential blackhole or wormhole points.Reachinga trusted site an
agent shares and updates its knowledge about suspicious nodes. So, an agent will always try to
visit nodes from (Priority List–updated Suspicious Node List) set. But this movement is
successful if the two nodes are connected according to equation (24) and there is no
simultaneous transmission in the neighborhood of the intended destination (taken care of by the
MAC protocol). Thus, an agent residing at node MNA moves to node MNB (connected to MNA)
with probability pt.
We describe the security model as follows.
4.1. Detailed Algorithm
The following data structures are needed
• Priority_list of agent j: PL:-it has two fields- node_id and trust_level (unvisited 0;
suspected -1; trusted +1; recent visit by an agent of same owner +2)
• Suspected node list for agent j: SL:-two fields-node id and optional provider id if not
given by the owner of agent j
• β: a positive integer to be kept at node
• α: a positive integer to be kept at node
• Default trust level : TS (> k(otherwise, a node becomes suspicious))
• Trust level view of the MANET by node i: (Trust level1, Trust level2, Trust
level3,………………..)I where trust level1 represents the
trust value assigned to node id=1 by the current node i
according to equation (18)
• Cagent-id: number of part agents sent for the lost agent designated by ’agent-id’
• X:maximum number of malicious nodes in the network
• Tagent-id: maximum time an agent can be enroute
9. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
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Initially the priority lists of all agents have 0 trust level corresponding to every node id in
their priority list (PL). So, node i’s view of the network will be (TS,TS,……)i.
The workflow can be divided into two parts: (i) Computation/Action in mobile node and (ii)
functions of the agents.
Algorithm I describesthe function of the agents collecting first handinformation about an
agent’s trust and second hand information about the nodes whom the hosts (visited by the
agent) suspect.
Algorithm II , an evolutionary algorithm based on Monte Carlo simulation, is running at the
nodes that takes its input from algorithm I and any message received from trusted neighbors
(second hand information) to update the distributed trust model and hence the node’s trust level
view of the network. This in turn affects the route taken by newer agents.
Steps followed by each agent
Algorithm – I: Agent_code()
1. While task given to the agent is not completed
1.1. Move to an agent site (MN) (unvisited) according to the priority list provided
1.1.1.Check if the next node to be visited falls in the appended suspected nodes list
1.2. If that destination falls in the same cluster as it is now residing, the agent moves
to the new destination with probability p[16]
1.3. Before processing, as in [16][3] hashcode can be used to detect any attempt to
change agent’s code/data by the node
1.3.1.Gather information needed by the application that deployed this agent
1.3.2.Update computed results
1.3.3.Hash code should also be computed to take care of updated data
1.3.4.Share and update its suspected node list (if any) provided by the owner with
this host
1.3.4.1. Appended entry (if any) will be marked by the id of this host.
1.4. Else go to step 2 //inference: most likely agent’s visit was not safe
2. Retract back to the owner
3. Stop
Steps followed by every mobile node (host platform)
Algorithm – II: MN_code()
1. Input network configurations (initial position, speed of the nodes)
2. For t=t0 to T repeat the following
2.1. Some nodes may fail following Weibull distribution and others move according
to SRMM and as a result a new edge list, E’ is formed as in [16][17].
2.2. If an agent comes to this node (MNj)
2.2.1.If the agent is found to be suspected (authentication fails or it comes from a
suspected node) then
2.2.1.1. Kill that agent
2.2.2.Otherwise allow computation at this node
2.2.3.Looking at the suspected node list of this agent, this node updates its
indirect observation using equations 7 through 12 depending on how much
the node trusts this visiting agent’s owner
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2.2.4.Also the node shares it’s (if nonempty) suspected node list with the current
visitor.
2.3. If an agent owned by this node comes back containing at most one suspected
node in its PL then
2.3.1.Call Update_Trust().
2.4. If an agent does not come back and time out occurs,
2.4.1.Divide the job of that agent into n parts and spawn n agents which carry n
priority sub lists.
2.4.2.Start a timer (Tagentid) for these n new agents to indicate that it is arepeat
attempt.
2.4.3.The agent ids are also given in such a way to indicate each one as (1/n)th
part of 1 task-that of the lost agent.
2.4.4.Set Cagent-idto n.
2.5. If a part agent comes back, decrease Cagent-id by one.
2.5.1.Call Update_Trust() method.
2.6. If a Tagentidexpires, find its corresponding Cagentid.
2.6.1.If Cagentid>X then deploy the lost agents again asking them this time to
follow different route.//this algorithm can tolerate maximum X suspicious
nodes
2.6.2.Else if 0<Cagentid<X then ask recommendation from trusted neighborhood
regarding the suspected nodes (mentioned in the priority sub lists of
Cagentidlost agents).
2.7. Receive information from trusted neighborhood about other nodes and update
the indirect observation following equations 7 through 12.
2.8. Hence update comprehensive (b,d,u) for the nodes visited using equations 13
through 17.
2.9. Compute how much this node trusts others in the network following equation
18.
2.10. If the resulting trust level of any node falls below Trust_threshold demanded by
the deployer application, then append the node id to the suspected node list.
2.11. The PL for each agent containing trusted node ids is also formed and kept with
the owners.
2.12. Deploy the agents.
2.12.1. Equip the agents with the suspected node list (what to avoid) and a
priority list (what to follow).
3.Stop.
Update_Trust()
1. Uptate the results
2. Update direct observation of this node
2.1. If a node is found to be trusted, α is incremented according to equation 3
2.2. Otherwise β is updated according to equation 4
2.2.1.Also learn to avoid the existing route followed by the agents towards this
node
2.3. Using equations 2, 5 and 6 update yield values of bij, dij and uij for all j visited
by the agent
3. Update indirect observation of this node.
3.1. If any new entry is found in the suspected node list then
11. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
269
3.1.1.Update this information depending on how much the owner trusts the
information provider according to equations 7 through 12
4. Kill the agent (Algorithm – I, steps 1.4 and 1.5)
5. Return
Step 1.3 of algorithm-I is optional, it isneeded to protect the priority list (kept as part of
agent’s data) from corruption. Individual node failures are considered in step 2.1 of MN_code().
But we did not consider the fault tolerance of the nodes. Here a node failure is treated to be
irrecoverable. It may be pointed out that step 1.4 of Agent_code() actually corresponds to step
2.4 of MN_code(). Here we assume that a host eventually detects a malicious agent. Creation of
part agents will be continued unless all of them come back or decision can be made about the
nodes found in the priority lists of missing agents.
As can be seen, agents in our system migrate and collect feedback about the trustworthiness
of the nodes they visit. So, they work like watchdogs[6]. The reputation system at the nodes
based on the first hand and second hand information updates its view of the network and
accordingly guides (providing priority list and suspected node list) the agents it deploys.
5. EXPERIMENTAL RESULTS
The simulation is carried out in Java and can run in any platform. For simplicity, in our
simulation the PL tells the agents which nodes to visit. After visiting all the nodes from the PL
Figure 2(a) Network graph at time instant t=t0 and the position of the agents
(b) Network graph at time instant t=t0+∆t and the position of the agents
(c) Network graph at time instant t=t0+2∆t and the position of the agents
3
2
1
1
2
4
5
6
3
3
1
2
Node Status
3 1
4 0
5 0
Node Status
2 1
3 0
4 0
Node Status
4 -1
5 0
6 0
(a)
2
2
5
2
1
1
4
6
3
3
1
Node Status
3 1
4 0
5 1
Node Status
2 1
3 1
4 0
Attacked
(b)
2
2
5
2
1
1
4
6
3
3 1
NodeStatus
3 1
4 -1
5 1
NodeStatus
2 1
3 1
4 -1
(c)
Table 1. Default values of our configuration
Parameter Default Values Parameter Default Values
Mobility Model SRMM Length of priority
list
10
Time 80 min Trust View
default(b,d,u)
(0,0,1)
N 25 Trust threshold(k) 0.49
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successfully, the agent moves back to its owner. We have taken an instance where there are 6
nodes and 3 agents in the network. The connectivity graph, agents and their corresponding PL
are shown in figure 2. Due to smooth movement of the nodes (according to SRMM) no drastic
change can be observed in the connectivity graph in subsequent time instants. In our example
MN4 is treated as a malicious node that can launch routing attack that prevents visitor agents
from coming back to their owners. As can be observed agents 1, 2 and 3 eventually get stuck at
MN4.Thus according to step 2.4 of MN_Code() time out (6*average propagation delay) occurs
and the owners MN1, MN2 and MN3 spawn agents 1 and 4, 2 and 5, 3 and 6 respectively with
lesser number of nodes in PLs. Here we subdivide only one PL into two unequal parts and
spawn two agents accordingly. The division is carried out according to a factor that is initialized
to 0.5 but is decreased by 0.15 in each iteration till it reaches 0.2(in an order to group all
suspicious nodes in one sublist). Now nodes with (di
j(f)
-bi
j(f)
)> ε(=0.0028) are put in the smaller
sublist. Thus agent 1 now needs to visit MN2 and MN3 while agent 4 visits MN4.
Moreover,while visiting MN2 and MN3, agent 1 finds that some agent from the same owner
(MN1) has recently visited these nodes. This observation is reflected in the status (=2 instead of
1) of agent 1’s PL. Clearly this time direct observation (b14=0.2, d14=0.11, u14=0.7) of agent 1
gets reflected in the final observation(b1
4(f)
=0.05298,d1
4(f)
=0.02796, u1
4(f)
=0.91906)of its
owner’s (MN1) trust view. This process goes on. While updating direct observation in equation
5 and 6, for simplicity (major change is not expected in simulation time=80min) old and new
values are given equal weights. As soon as trust view of any node goes below 0.49, that node is
declared to be malicious and is appended in the suspected list of agents (removed from its PL as
well) spawned by the detector node. Thus the nodes try to overcome routing attacks without
any overhead of control messages.
We have done a series of experiments to validate our algorithm and found some interesting
results. For simplicity whenever an agent goes missing, 2 agents are spawned as is explained in
the example. The simulation parameters are summarized in table-I.Any change to it is explicitly
mentioned. We introduce a metric called the ratio of agents attracted during time period t that is
defined as follows
ܴܽ݀݁ݐܿܽݎݐݐܣݏݐ݊݁݃ܣ݂݅ݐሺݐሻ =
ே.௧௦௧௨௨௦ௗ௦௧௧௧
்௧.௧௦ௗ௬ௗ௧௧௧
(19)
Experiments are done to show timely variation of this metric while the MANET has 3
malicious nodes (MN3, MN4 and MN5) and 5 malicious nodes(MN3, MN4, MN5, MN11and
MN13). Results plotted in figure 3 clearly show that agents gradually overcome network
hostility. This is evident from the steady slope of the curve especially after T=8min. As number
of malicious nodes increases more agents are affected but eventually the agents detect them by
the trust calculation. Since the curve well stabilizes at around 80 min with 3 malicious nodes,
simulation time is kept at 80min in our experiments.
Also we show variation of the ratio (equation 19) with no. of nodes (N) in figure 4 while MN3
and MN4 launch blackhole/wormhole attack.It can be observed that as the network gets bigger,
the ratio gradually declines (due to increased amount of indirect observation) and eventually
(N=35 onwards) reaches a steady state.Also more number of agents (M=20) implies richer direct
observation resulting in even faster convergence of trust. Arrival of steady state for both M=10
and 20 indicates the scalability of our scheme with moderate accuracy.
Another metric named ratio of successful agents is defined as follows
13. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
271
ܴ݂ܽ݅ݐ ݈ݑ݂ݏݏ݁ܿܿݑݏ ݏݐ݊݁݃ܣሺݐሻ =
ே.௧௦ ௧ ௪௧௧௧
்௧.௧௦ௗ௬ௗ௧௧௧
(20)
All agents deployed by an owner may not come back within stipulated time as some of them
may be rerouted by malicious nodes (wormhole), engulfed by them (blackhole) or lost due to
network partitioning. Agent code/data may get modified also that can be detected by the owner
(by generating and checking hashcode[16]). Considering MN3 and MN4 to be malicious nodes,
the effect of MANET size on agent success is found Momentary drop in agent success can be
observed when M and N values are almost comparable in figure 5. But as MANET becomes
bigger, agents manage to provide a steady success rate. Both figures 4 and 5 confirm the fact that
bigger networks are not detrimental for agent success if the level of hostility remains same.
The next experiment again introduces another metric called the node success ratio defined as
ܰ݁݀ ݏݏ݁ܿܿݑݏ ݅ݐܽݎሺݐሻ =
ே.ேௗ௦ ௧௧ ௩௧ ௧ ௧௦ ௨௧ ௧௧௦௧௧௧
்௧.ௗ௦ ௪ ௧௧௧
(21)
The dependence of successful detection and subsequent deletion of malicious nodes from PL
by any node on the number of agents they deploy is indicated in figure 6. It is seen that with 50
agents, up to 3 malicious nodes can be successfully detected within 80 minutes and no nodes in
that case will be sending their agents to MN3, MN4 or MN5. Also it can be observed that all
curves reach a local maxima when number of agents is approximately equal to number of nodes
(=25). This is because at this point all nodes get the direct observation from agents, that is,
agents tend to cover the entire network.
In the next experiment our model is tested with increasing MAS size. We define a metric
called ratio of false negatives that is defined as follows:
Fig. 3 Timely Variation of ratio of agents attracted Fig. 4 Variation of ratio of agents attracted by the by
the malicious nodes malicious nodes as network gets bigger
0
0.1
0.2
0.3
0.4
0.5
1 4 5 6 7 7.5 8 15 36 71
RatioofAgentsAttracted
Time(min)
Ratio of Agents Passed vs Time
3 Malicious
Nodes
5 Malicious
Nodes
0
0.1
0.2
0.3
0.4
0.5
0 20 40 60 80
RatioofAgentsAttracted
No. of Nodes
Ratio of Agents Attracted as
Network Gets Bigger
M=10
M=20
Figure 5 Variation of agent success rate with no.Figure. 6 Variation of node’s success ratio with no. of
ofnodes(N) agents(M)
0
0.2
0.4
0.6
0.8
1
0 20 40 60 80
Ratioofsuccessfulagents
No. of Nodes
Variation of Agent Success Rate
with N
M=20
M=10
0.5
0.6
0.7
0.8
0.9
1
10 20 30 40 50
NodeSuccessRatio
M
Ratio of Node Success with
Increasing Agents
Malicious
Node=1(3)
Malicious
Node=1(3,
4)
Malicious
Node=1(3,
4, 5)
14. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
272
ܴܽ݅ݐ ݂ ݁ݏ݈ܽܨ ܰ݁݃ܽݏ݁ݒ݅ݐ =
ே. ௨ௗ௧௧ௗ ௨௦ ௗ௦
்௧ ே. ௨௦ ௗ௦
(22)
Let us presume we know the total number of malicious nodes in the network at a time instant.
So it is checked if our algorithm running at the nodes can successfully detect all the malicious
nodes. The results show(figure 7) that as more nodes participate for some job and hence deploy
agents (which in turn also gains direct experience) to traverse various parts of the network more
malicious nodes are eventually detected. Thus for greater MAS size the ratio ultimately drops to
0 indicating successful detection of all malicious nodes. For bigger network more agents are
needed to achieve the same value of false negatives hence needing more bandwidth. Interestingly
with M=2*N, the ratio of false negative hits 0. It also portrays correctness of our algorithm as all
malicious nodes can be detected by deploying sufficient no. of agents.
6. CONCLUSION
This paper provides a trust based framework for securing the hosts and preventing the agents
from visiting or passing through a compromised node specially a blackhole/wormhole trap (from
where the agents won’t make a successful return in time) in MANET. Possible modification in
data is detected by taking hash code of an agent’s data and code. Our model establishes trust
among the nodes in a totally distributed manner without any central coordinator (for example a
trusted third party). If an agent does not come back, new agents with smaller PLs are spawned to
get better visibility of the MANET. Perfect detection of malicious nodes relies on how minutely
the owners divide the PL of missing agents (and give it to new agents). Direct observations of
the agents that come back play a very important role in the detection process. If any node is
found to be malicious, its entry gets removed from the PL and appended in the suspected list of
agents that are further deployed. The scheme enables an agent to share information with others
about suspicious nodes, thus helping in faster convergence of trust. Hence nodes visited by an
agent can know about MANET hostilities without deploying agents. Trust is quantified using a
tuple (b,d,u). For faster convergence of trust (consistent (b,d,u)s), newer nodes may ask for
indirect information from trusted neighborhood. SRMM is used to simulate the movement of the
nodes. The protocol is validated and results are shown in section 5. It can be observed that
according to our scheme even for larger MANET, nodes can detect all the malicious nodes and
eventually prevent themselves and their agents from network hostilities.
Figure.7Success of the reputation model proposed in
detecting malicious nodes
0
0.2
0.4
0.6
0.8
0 10 20 30 40 50 60 70
RatioofFalseNegatives
No. of Agents
Success of Reputation Model with
Increasing Agents
N=25
N=30
15. International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.6, November 2011
273
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Authors
SarmisthaNeogyis faculty in Jadavpur University at present and is in teaching profession since last
eighteen years. She has been an active researcher in the areas of distributed systems, fault tolerance,
mobile computing and security in wireless networks.
ChandreyeeChowdhury is a junior faculty in the department of Computer Science and Engineering at
Jadavpur University. She received M. E in Computer Science and Engineering from Jadavpur University
in 2005. Currently she is pursuing Ph. D under the guidance of Dr. Neogy. Her research interests include
reliability and security in wireless networks.