Wireless Sensor Networks are to be widely deployed in the near future for data monitoring in commercial, industrial and military applications. Though much research has focused on making these networks feasible and useful security has received very little attention. Sensor networks are exposed to variety of attacks like eavesdropping, message tampering, selective forward, gray hole attack, and Wormhole and Sybil attacks. Watchdog is a kind of behaviour monitoring mechanism which is the base of many trust systems in Ad hoc and Wireless Sensor Network. Current watchdog mechanism only evaluates its next-hop’s behaviour and propagates the evaluation result to other nodes by broadcasting, which is neither energy efficient nor attack resilient. The fundamental problem of secure neighbour discovery is studied which is importunate in protecting the network from different forms of attacks. In this paper an improved watchdog monitoring mechanism is proposed by using the process of change point detection. By implementing this change point detection algorithm in watchdog mechanism, the limitations of the existing watchdog mechanism are overcome. From this the exact malicious node can be found out and the data will be routed through a secure path bypassing the malicious node. Finally to analyze the efficiency of this algorithm, the results obtained from the proposed algorithm and the existing algorithms are compared

KARUPPIAH, A. BABU

English

Interscience Research Network

Graduate Research in Engineering and Technology (GRET) Volume 1 Issue 1 Article 9 July 2013 FALSE MISBEHAVIOUR ELIMINATION IN WATCHDOG MONITORING SYSTEM USING CHANGE POINT IN A WIRELESS SENSOR NETWORK A. BABU KARUPPIAH Velammal College of Engineering and Technology, Madurai, India, a_babukaruppiah@gmail.com Follow this and additional works at: https://www.interscience.in/gret  Part of the Aerospace Engineering Commons, Business Commons, Computational Engineering Commons, Electrical and Computer Engineering Commons, Industrial Technology Commons, Mechanical Engineering Commons, and the Physical Sciences and Mathematics Commons Recommended Citation KARUPPIAH, A. BABU (2013) "FALSE MISBEHAVIOUR ELIMINATION IN WATCHDOG MONITORING SYSTEM USING CHANGE POINT IN A WIRELESS SENSOR NETWORK," Graduate Research in Engineering and Technology (GRET): Vol. 1 : Iss. 1 , Article 9. DOI: 10.47893/GRET.2013.1008 Available at: https://www.interscience.in/gret/vol1/iss1/9 This Article is brought to you for free and open access by the Interscience Journals at Interscience Research Network. It has been accepted for inclusion in Graduate Research in Engineering and Technology (GRET) by an authorized editor of Interscience Research Network. For more information, please contact sritampatnaik@gmail.com. Graduate Research in Engineering and Technology (GRET): An International Journal 31 FALSE MISBEHAVIOUR ELIMINATION IN WATCHDOG  MONITORING SYSTEM USING CHANGE POINT IN A WIRELESS  SENSOR NETWORK  A.BABU KARUPPIAH1, T.MEENAKSHI2, T.I.MANO RANJITHA3 & S.VIVITHA4  1Assistant Professor, 2,3,4Final Year Students, Velammal College of Engineering and Technology, Madurai, India   Abstract- Wireless Sensor Networks are to be widely deployed in the near future for data monitoring in commercial, industrial and military applications. Though much research has focused on making these networks feasible and useful security has received very little attention. Sensor networks are exposed to variety of attacks like eavesdropping, message tampering, selective forward, gray hole attack, and Wormhole and Sybil attacks. Watchdog is a kind of behaviour monitoring mechanism which is the base of many trust systems in  Ad  hoc  and  Wireless Sensor Network. Current watchdog mechanism only evaluates its next-hop’s behaviour and propagates the evaluation result to other nodes by broadcasting, which is neither energy efficient nor attack resilient. The fundamental problem of secure neighbour discovery  is studied which is importunate in protecting the network from different forms of attacks. In this paper an improved watchdog monitoring mechanism  is proposed  by using the process of change point detection. By implementing this change  point detection algorithm in watchdog mechanism, the limitations of the existing watchdog mechanism  are overcome. From this the exact malicious node can be found out and the data will  be routed through a secure path bypassing the malicious node. Finally to analyze the  efficiency of this algorithm, the results obtained from the proposed algorithm and the existing algorithms are compared.   I. INTRODUCTION  Wireless  Sensor Networks have been used in every type of environment due to their easy deployment. WSNs provide their users with fast and easy access to their data and services even in remote areas such as battlefield, forest and volcano. In these security sensitive deployments, keeping the network available for its intended use is essential. But communications over wireless channels are, by nature, insecure and easily susceptible to various kinds of threats. An adversary can control a sensor node undetectably by physically compromising the node and use the captured nodes to inject faulty or false data into the Network system disturbing the normal cooperation among nodes. Authentication and Cryptographic   mechanisms alone cannot be used to solve this problem because internal adversarial nodes will have valid cryptographic keys to access the other nodes of the networks. Insider threat is also an important security issue in wireless sensor networks because traditional security mechanisms cannot catch insider attackers as they are legal members of the network. These attackers can disrupt the network by dropping, modifying or misrouting the data packets. This is a very serious threat for many applications such as military surveillance system that monitors the battlefield and several   other infrastructures.   II. WATCHDOG MECHANISM   Watchdog is a monitoring mechanism introduced to identify the misbehaving nodes in the network [1][6]. In this approach each sensor node has its own watchdog that monitors and records its one hop neighbours behaviour such as packet transmission.    When sending node A sends a packet to its next node B, the watchdog in A verifies  whether B forwards the packet to the next node or not by using its overhearing ability within its transceiver range But watchdog has the limitation [2] of not being able to detect the misbehaving nodes in the following conditions.   Fig 1 Existing Watchdog Mechanism  1) Ambiguous collision:  Consider A forwards a packet to B and overhears whether b is forwarding it or not. When B forwards it to C, A may not overhear this transmission if other neighbours of A send packets to it at the same time. This may mislead A to conclude that B is malicious but this may not be correct.   2) Receiver collision: Collision may occur at the receiver side also (i.e.) C may not receive the packet. A can overhear that B has forwarded the packet, but it cannot tell whether C has received the packet.   3) Limited transmission power:  If B can adjust its False Misbehaviour Elimination in Watchdog Monitoring System using Change Point in a Wireless Sensor Network Graduate Research in Engineering and Technology (GRET): An International Journal 32 transmission power such that A can overhear but C does not receive, then B can drop packets and prove its trustworthiness.   4) False misbehavior:  A malicious node intentionally reports that other nodes are misbehaving. A can report that B is dropping packets although B is not. In this case A’s neighbor node S which cannot communicate directly with B, can consider B as malicious.   5) Partial dropping:   Instead of dropping all packets, B can drop only some packets such that the  failure tally will not exceed the detection threshold of A’s watchdog.     III. RELATED WORK   A mechanism based on signal strength [5] was proposed to detect the malicious nodes in a network. The idea was to compare the signal strength of a reception with its expected value. A signal is only detected by a receiving node if the received signal power is equal or greater than the received signal power threshold. If the signal power received is less than the threshold then the particular node is suspected to be malicious.   This may not be true for all cases. A signal power can be weakened due to various reasons like environmental factors, weak signal strength etc. In this case the probability of detecting or suspecting a regular node as malicious node is high.    Hence  the reliability of the system is not satisfactory.   To overcome the above said limitations watchdog monitoring system was improved by adding a threshold mechanism  [4][12].  In this mechanism, S stores all recently sent packets in its buffer, and compares each packet with the overheard packet to see whether there is a match. If yes, it means that the packet is forwarded by T and S will remove the packet from the buffer. If a packet remains in the buffer for a period longer than a pre-determined time, the watchdog considers that T fails to forward the packet and will increase its failure tally for T. If a neighbours’ failure tally exceeds a certain threshold, it will be considered as a misbehaving  node  by S.    IV. PROPOSED WORK   The objective is to improve the existing watchdog monitoring system by implementing the change point detection algorithm  [3]  in it, thereby detecting the exact malicious node in the network.   This algorithm will be able to detect the malicious nodes after some  attacks have occurred in the process of data collection.   Here the sink receives a collection of response packets from the nodes in the routing path and analyses these packets to detect the malicious nodes. The status of a node is represented by a  status bit. We consider that a node replies 1 for a negative packet and 0 for a positive packet. The node which does not send a reply is considered as  -1.So the sink receives all the response packets and  creates a list of  status bits for the nodes in the routing path. The False Misbehaviour Elimination in Watchdog Monitoring System using Change Point in a Wireless Sensor Network Graduate Research in Engineering and Technology (GRET): An International Journal 33 status for each round of response can be denoted by a status vector  bi=[b1,b2,...bn]  ∀  bi  ϵ  {-1,0,1}. From the status vector the sink groups all the nodes with status bits value of -1 into a separate set Sw.  This set contains the collection of nodes which do not respond to the sink. The nodes in Sw are considered as suspicious nodes rather than malicious nodes because some of the nodes in the routing path may fail to receive or send packets due to interference or low communication quality. Hence Sw is called as  suspicious set.  The sink pays more attention to these nodes in subsequent data collection. To  any,  if  bi-0=0 or  -1 and  bi=1, then  bi-1  is a  change point in B. The sensor node on the routing path where the value of status bit changes from 0 or -1 to 1  is referred as the change point. The node identified as the change point along with its immediate upstream and downstream nodes form the  malicious sequence. The neighbour nodes of the malicious nodes become the threatened nodes. Distinguishing the malicious and threatened nodes is not necessary because the threatened nodes are not secure for routing. Hence both nodes should be excluded from the routing path. In case of more than one malicious node in a routing path the above said analysis is performed several times to obtain a  malicious set. Our proposed algorithm involves the implementation of this change point algorithm in the existing watchdog mechanism.    Here  the responses from the watchdog nodes are taken into consideration. By implementing this algorithm, the nodes exhibiting false misbehavior can be exactly identified and excluded from the routing path. The malicious node might be able to change its transmission power and deceive the watchdog. But using our proposed algorithm even such nodes can be rightly identified. An added advantage is that the present threshold mechanism of the watchdog monitoring systems can be eliminated by using this algorithm. All the limitations of watchdog mechanism that have been mentioned above are found to be eliminated using our algorithm. The sink will broadcast the identities of the nodes found to be  k  malicious. So the other nodes will exclude these nodes from the routing path.    V.  EXPERIMENTAL RESULTS &  COMPARISON    In the network setup that is considered it is assumed that the source node is always a true node. The following are the simulation results. Fig 1 shows the  simulation result for change point algorithm.   Fig1 Simulation result for Change Point Detection  Algorithm  The following are the results for existing watchdog mechanism  involving the detection of malicious and true node. Fig2 shows the result of watchdog mechanism for a true node. Fig3 displays the result  of watchdog mechanism for malicious node. False Misbehaviour Elimination in Watchdog Monitoring System using Change Point in a Wireless Sensor Network Graduate Research in Engineering and Technology (GRET): An International Journal 34  Fig2 Result for Watchdog Mechanism showing true node   Fig3 Result for Watchdog Mechanism detecting a malicious node  In the case of watchdog mechanism though it declares a node as malicious, it is not  completely reliable due to the limitations cited above. On comparing both results, the change point algorithm is  found to be more efficient since it is found to  detect the exact malicious node under all circumstances.  Fig4 Comparison of existing with proposed algorithms.  Fig4 shows the comparison of the existing with the proposed algorithms. The graph is plotted by getting the response bits from each round for both the existing and the proposed algorithms. It  shows that the existing algorithm  has  both  the possibilities  of  detecting the malicious node  and also declaring a true node to be malicious. The result of the existing algorithm is not found to be accurate for all the rounds. By using the  proposed algorithm the exact malicious node is found to be identified in all the rounds. The malicious node detected  by the proposed algorithm is found to be accurate irrespective of the number of rounds conducted.   VI. CONCLUSION  Detection of malicious node is a major concern for security of any network. It is very essential to detect these nodes to prevent the network from loss or tampering of packets. This paper proposes a simple methodology to detect the exact malicious nodes in a Wireless Sensor Network. The future scope of this paper is to remove the exact  malicious nodes by implementing this algorithm and  to  prove that it is also energy efficient when compared with the existing mechanisms.   REFERENCES  [1]. An Improved Watchdog Technique based on Power-Aware Hierarchical Design for IDS in Wireless Sensor Networks A. Forootaninia1 and M. B. Ghaznavi-Ghoushchi,  International Journal of Network Security & Its Applications (IJNSA), 2012  [2]. Insider Threats against Trust Mechanism with Watchdog and Defending Approaches in Wireless Sensor Networks Youngho Cho and Gang Qu, IEEE Symposium on Security and Privacy Workshops ,2012  [3]. A Secure Mechanism for Data Collection in Wireless Sensor Networks  Yuxin  Mao, School  of Computer and Information Engineering,  Zhejiang Gongshang University,  Applied Mathematics & Information Sciences  – An International Journal ,2010. [4]. Extended Watchdog Mechanism for Wireless Sensor Networks Lei Huang +, Lixiang Liu, Journal of Information and Computing Science, 2007  False Misbehaviour Elimination in Watchdog Monitoring System using Change Point in a Wireless Sensor Network Graduate Research in Engineering and Technology (GRET): An International Journal 35 [5]. Malicious Node Detection  in Wireless  Sensor Networks Waldir Ribeiro Pires J´unior Thiago H. de Paula Figueiredo Hao Chi Wong,  18th International Parallel and Distributed Processing Symposium (IPDPS'04) , 2004   [6]. Reputation and Trust Mathematical Approach for Wireless Sensor Networks Haiguang Chen, Gangfeng Gu , Huafeng Wu, Chuanshan Gao,  International Journal of Multimedia and Ubiquitous Engineering,2007  [7]. J. Deng, R. Han and S. Mishra, INSENS: Intrusion-tolerant routing in wireless sensor networks. Computer Communications in Dependable Wireless Sensor Networks, 2006, 29(2), 216-230.   [8]. Y. Wang, G. Attebury and B. Ramamurthy, A survey of security issues in wireless sensor networks. IEEE Commun. Surveys Tutorials, 2006, 8(2), 2-23.   [9]. Y. Zhang and W. Lee, Intrusion detection in wireless ad-hoc networks, Proc. the 6th Annual International Conference on Mobile Computing and Networking, 2000, 275-283.   [10]. S. Sen, et al., "Power-aware intrusion detection in mobile ad hoc networks," Ad hoc networks, pp. 224-239, 2010.   [11]. Y. Wang, "Statistical Techniques for Network Security: Modern Statistically-Based Intrusion Detection and Protection" Idea Group Inc (IGI), 2008.  [12]. Sergio Marti, T.J. Giuli, Kevin Lai, and Mary Baker, “Mitigating Routing Misbehaviour in Mobile and Ad Hoc Networks,” In Proc. Of International Conference on Mobile Computing and Networking (Mobicom), 2000, pp. 255-265   [13]. Chris Karlof and David Wagner, “Secure routing in wireless sensor networks: attacks and countermeasures,” Ad Hoc Networks Journal, Vol.1, Issue 2-3, 2003, pp. 293-315  [14]. Y. Wang, G. Attebury and B. Ramamurthy, A survey of security issues in wireless sensor networks. IEEE Commun. Surveys Tutorials, 2006, 8(2), 2-23.  

FALSE MISBEHAVIOUR ELIMINATION IN WATCHDOG   MONITORING SYSTEM USING CHANGE POINT IN A WIRELESS   SENSOR NETWORK

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FALSE MISBEHAVIOUR ELIMINATION IN WATCHDOG MONITORING SYSTEM USING CHANGE POINT IN A WIRELESS SENSOR NETWORK

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