SGSIA-In-network Data Preprocessing for Secure Grid-Sensor Integration
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DDoS Attack Detection in Wireless Sensor Networks
A recent paper authored by DART personnel has been accepted at the prestigious IEEE International Conference on Pattern Recognition (ICPR'06)
Title : A Pattern Recognition Scheme for Distributed Denial of
Service (DDoS) Attacks in Wireless Sensor Networks
Authors : Zubair A. Baig, Mohamed Baqer, and Asad I. Khan
Conference : IEEE International Conference on Pattern Recognition (ICPR'06)
Abstract
Wireless Sensor Networks (WSNs) are characterized as a network of light-weight devices called sensor nodes designated
with the task of sensing their environments. Considering the untrusted environments of operations of such networks, the threat of a potential Distributed Denial of Service (DDoS) attack against such networks is imminent. We define distinct attack patterns depicting DDoS attacks against target nodes within wireless sensor networks for three most commonly used network topologies. We propose a Graph Neuron (GN)-based, decentralized pattern recognition scheme for attack detection in sensor networks. The scheme does analysis of internal traffic flow of the network for DDoS attack patterns. The attack patterns depend on both the current energy levels, as well as the energy consumption rates of individual target nodes. The results of varying pattern update rates on the pattern recognition accuracies for the three network topologies are included in the end to test the effectiveness of our implementation.
References
[1] R. Chang. Defending against flooding-based distributed denial of service attacks: A tutorial. IEEE Communications Magazine, pages 42–51, Oct. 2004.
[2] A. I. Khan. A peer-to-peer associative memory network for intelligent information systems. In Proc. of the Thirteenth
Australasian Conf. on Information Systems, volume 1, 2002.
[3] A. I. Khan and P. Mihailescu. Parallel pattern recognition computations within a wireless sensor network. In Proc. of
the Int’l Conf. on Pattern Recognition, volume 1, pages 777–780, 2004.
[4] R. Min, M. Bhardwaj, S.-H. Cho, E. Shih, A. Sinha, A.Wang, and A. Chandrakasan. Low power wireless sensor networks.
Technical report, MIT, 2001.
[5] D. Song. Probabilistic modeling of leach protocol and computing sensor energy consumption rate in sensor networks.
Technical report, Texas A & M University, Feb. 2005.
[6] A. Wood and J. Stankovic. Denial of service in sensor networks. IEEE Computer Magazine, pages 54–62, Oct. 2002.
Authors : Zubair A. Baig, Mohamed Baqer, and Asad I. Khan
Conference : IEEE International Conference on Pattern Recognition (ICPR'06)
Abstract
Wireless Sensor Networks (WSNs) are characterized as a network of light-weight devices called sensor nodes designated
with the task of sensing their environments. Considering the untrusted environments of operations of such networks, the threat of a potential Distributed Denial of Service (DDoS) attack against such networks is imminent. We define distinct attack patterns depicting DDoS attacks against target nodes within wireless sensor networks for three most commonly used network topologies. We propose a Graph Neuron (GN)-based, decentralized pattern recognition scheme for attack detection in sensor networks. The scheme does analysis of internal traffic flow of the network for DDoS attack patterns. The attack patterns depend on both the current energy levels, as well as the energy consumption rates of individual target nodes. The results of varying pattern update rates on the pattern recognition accuracies for the three network topologies are included in the end to test the effectiveness of our implementation.
References
[1] R. Chang. Defending against flooding-based distributed denial of service attacks: A tutorial. IEEE Communications Magazine, pages 42–51, Oct. 2004.
[2] A. I. Khan. A peer-to-peer associative memory network for intelligent information systems. In Proc. of the Thirteenth
Australasian Conf. on Information Systems, volume 1, 2002.
[3] A. I. Khan and P. Mihailescu. Parallel pattern recognition computations within a wireless sensor network. In Proc. of
the Int’l Conf. on Pattern Recognition, volume 1, pages 777–780, 2004.
[4] R. Min, M. Bhardwaj, S.-H. Cho, E. Shih, A. Sinha, A.Wang, and A. Chandrakasan. Low power wireless sensor networks.
Technical report, MIT, 2001.
[5] D. Song. Probabilistic modeling of leach protocol and computing sensor energy consumption rate in sensor networks.
Technical report, Texas A & M University, Feb. 2005.
[6] A. Wood and J. Stankovic. Denial of service in sensor networks. IEEE Computer Magazine, pages 54–62, Oct. 2002.
