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Wireless sensor networks (WSNs) due to their deployment in open and unprotected environments become suspected to attacks. Most of the resource exhaustion occurs as a result of attacking the data flow control thus creating challenges for the security of WSNs. An Anomaly Detection System (ADS) framework inspired from the Human Immune System is implemented in this paper for detecting Sybil attacks in WSNs. This paper implemented an improved, decentralized, and customized version of the Negative Selection Algorithm (NSA) for data flow anomaly detection with learning capability. The use of R-contiguous bit matching, which is a light-weighted bit matching technique, has reduced holes in the detection coverage. This paper compares the Sybil attack detection performance with three algorithms in terms of false negative, false positive, and detection rates. The higher detection, and lower false positive and false negative rates of the implemented technique due to the R-contiguous bit matching technique used in NSA improve the performance of the proposed framework. The work has been tested in Omnet++ against Sybil attacks for WSNs.

An Immunology Inspired Flow Control Attack Detection Using Negative Selection with R-Contiguous Bit Matching for Wireless Sensor Networks