An Energy-Efficient Selective Forwarding Attack Detection Scheme Using Lazy Detection in Wireless Sensor Networks

2012 ◽  
pp. 157-164 ◽  
Author(s):  
Junho Park ◽  
Dong-ook Seong ◽  
Myungho Yeo ◽  
Byung-yup Lee ◽  
Jaesoo Yoo
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Attack Detection ◽  
Wireless Sensor ◽  
Detection Scheme ◽  
Selective Forwarding

Research on Selective Forwarding Attack Detection Scheme in Wireless Sensor Networks

2012 ◽  
Vol 433-440 ◽  
pp. 5298-5302
Author(s):  
Ying Wang ◽  
Guo Rui Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Attack Detection ◽  
Wireless Sensor ◽  
Detection Scheme ◽  
Selective Forwarding ◽  
Critical Facilities ◽  
Mission Critical ◽  
Detection Schemes ◽  
Lower False Alarm Rate

Selective forwarding attacks in wireless sensor networks may corrupt some mission critical applications such as military surveillance and critical facilities monitoring. They are very difficult to be detected and distinguished from normal packet drops in wireless sensor networks. We propose an improved sequential mesh test based detection scheme in this paper. The scheme extracts a small quantity of samples to run the test, instead of regulating the total times of test in advance. We show through experiments that our scheme can provide higher detection accurate rate and lower false alarm rate than the existing detection schemes. Meanwhile, less communication and computation power are required to detect the selective forwarding attacks.

scholarly journals Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881504 ◽  
Author(s):  
Hongliang Zhu ◽  
Zhihua Zhang ◽  
Juan Du ◽  
Shoushan Luo ◽  
Yang Xin
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Adaptive Learning ◽  
Packet Loss ◽  
Learning Automata ◽  
Attack Detection ◽  
Wireless Sensor ◽  
Communication Quality ◽  
Selective Forwarding

Wireless sensor networks face threats of selective forwarding attacks which are simple to implement but difficult to detect. It is difficult to distinguish between malicious packet dropping and the normal packet loss on unstable wireless channels. For this situation, a selective forwarding attack detection method is proposed based on adaptive learning automata and communication quality; the method can eliminate the impact of normal packet loss on selective forwarding attack detection and can detect ordinary selective forwarding attack and special cases of selective forwarding attack. The current and comprehensive communication quality of nodes are employed to reflect the short- and long-term forwarding behaviors of nodes, and the normal packet loss caused by unstable channels and medium-access-control layer collisions is considered. The adaptive reward and penalty parameters of a detection learning automata are determined by the comprehensive communication quality of the node and the voting of its neighbors to reward normal nodes or punish malicious ones. Simulation results indicate the effectiveness of the proposed method in detecting ordinary selective forwarding attacks, black-hole attacks, on-off attacks, and energy exhaustion attacks. In addition, the communication overhead of the method is lower than that of other methods.

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