a light-weight architecture for public key crptography in wsn

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Detecting Selective Forwarding Attack in WSN Using Neighborhood Information Presented By Leela Krishna Bysani 209CS2083 M. Tech (Information Security) Under the Supervision of Prof. Ashok Kumar Turuk Dept. of Computer Science & Engineering National Institute of Technology Rourkela

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Introduction Wireless Sensor Network(WSN)s consists of spatially distributed ad-hoc sensors to cooperatively detect or monitor some event. It is mostly designed for real-time data collection and analysis of data in hostile environments which makes them to be used mainly in monitoring and surveillance applications.

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Hence they are usually deployed in hostile environment where an adversary can compromise internal nodes and launch various inside attacks. One of the attacks is Selective Forwarding Attack.

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Selective Forwarding Attack In WSN nodes transmit data to the base station through intermediate nodes due to their limited range. In Selective Forwarding Attack malicious node present in the transmission path selectively drops some of the packets. If the malicious nodes drops all the packets, then it is called as Black hole attack.

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Categorization Of Selective Forwarding based on node count in WSN

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Related Work Multi-hop Acknowledgment Scheme: Multi Data Flow Topology Scheme:

Network Model: : 

Network Model: The network has to be densely populated. There has to be some key distribution scheme to provide secure communication between the sensors. The nodes use some authentication mechanism to trust each other.

Proposed Scheme: : 

Proposed Scheme: In our scheme, nodes monitor their neighbor nodes and if they act as malicious then broadcasts an alert packet. Our scheme relies on broadcast nature of sensor networks. Instead of discarding the packets, node monitors whether destination is forwarding the packet or not.

Neighborhood Information: : 

Neighborhood Information: In deployment phase, nodes collect information of their two hop neighbors and stores in a neighbor table. Every node stores a trust value for each neighbor. A node broadcasts a hello packet which contains three important fields source node id, intermediate node id and hop counter. At Source node: Source Node Id=Intermediate Node Id= Its own Id and Hop Counter=2 At one hop neighbor node: Stores Source node id as immediate neighbor. Intermediate Node Id=Its own id and hop counter=1 At Two hop neighbor node: Stores Source node id as two hop neighbor and intermediate node id as immediate neighbor. Hop counter=0 and hello packet is discarded

Detection Process: : 

Detection Process: Step 1: When an event raises, node transmits a packet to next node. Step 2: The next hop node/intermediate node receives the packet. At the same time, the neighbor nodes of the transmitted node also receives the packet. If the packets source and destination are its immediate neighbors , then it acts as monitor node and starts a timer t otherwise discards. Step 3: If an intermediate node drops a packet, it informs its immediate upstream node about dropping by a negative acknowledgment(NACK) packet. NACK has two flag values. They are: Buffer Overflow flag Collision flag

Contd… : 

Contd… Step 4: If it does not send NACK packet then monitor nodes increases the malicious value of the corresponding node and if it is greater than threshold value it generates an alarm packet. Step 5: If it receives a NACK packet, it increases NCK_OF value if flag value equal to buffer overflow. If this value is greater than another threshold value ‘γ’, then it calculates a value [(s-f)*t-k]. If this value is +ve then the node is behaving maliciously, so increase the malicious value. Here s is transmitting rate of intermediate node f is mean service rate of queue t is the time span in sec k is the buffer size.

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When a node receives an alert packet about its neighbor node from one of its two hop neighbor node, it stores the alert packet. If it receives k number of alert packets then it marks the node as malicious

Conclusion and Future Work : 

Conclusion and Future Work The above scheme detects selective forwarding attack effectively. It also reduces false detections and traffic overload due to alert packets. Learning network simulator Above scheme has to be implemented using a network simulator

Road Map : 

Road Map

References : 

References Jeremy Brown and Xiaojiang Du. Detection of selective forwarding attacks in heterogeneous sensor networks. In ICC, pages 1583–1587, 2008. Chris Karlof and David Wagner. Secure routing in wireless sensor networks: at-tacks and countermeasures. Ad Hoc Networks, 1(2-3):293–315, 2003. Hung-Min Sun, Chien-Ming Chen, and Ying-Chu Hsiao. An efficient countermeasure to the selective forwarding attack in wireless sensor networks. pages 1 –4, oct. 2007. Komathy K., Narayanasamy P, Corrective Approach to Alleviate the Sellishness of Mobile Nodes Indulging in Packet Drop, IEEE 2006, pages 82-87. Bo Yu and Bin Xiao. Detecting selective forwarding attacks in wireless sensor networks. In Parallel and Distributed Processing Symposium, 2006. IPDPS 2006. 20th International, page 8 pp., 2006. Bin Xiao, Bo Yu, and Chuanshan Gao. Chemas: Identify suspect nodes in selective forwarding attacks. J. Parallel Distrib. Comput., 67(11):1218–1230, 2007. Tran Hoang Hai and Eui nam Huh. Detecting selective forwarding attacks in wireless sensor networks using two-hops neighbor knowledge. In NCA, pages 325–331, 2008. Wang Xin-sheng, Zhan Yong-zhao, Xiong Shu-ming, and Wang Liang-min. Lightweight defense scheme against selective forwarding attacks in wireless sensor networks. pages 226 –232, oct. 2009. S. Kaplantzis, A. Shilton, N. Mani, and Y.A. Sekercioglu. Detecting selective forwarding attacks in wireless sensor networks using support vector machines. In Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007. 3rd International Conference on, pages 335 –340, 2007. Hemanta Kumar Kalita and Avijit Kar. Wireless sensor network security analysis. In International Journal of Next-Generation Networks, 2009.