Discrete R-Contiguous bit Matching mechanism appropriateness for anomaly detection in Wireless Sensor Networks

Resource exhaustion is one of the main challenges for the security of Wireless Sensor Networks (WSNs). The challenge can be addressed by using algorithms that are light weighted. In this paper use of light-weighted R-Contiguous Bit matching for attack detection in WSNs has been evaluated. Use of R-Contiguous bit matching in Negative Selection Algorithm (NSA) has improved the performance of anomaly detection resulting in low false positive, false negative and high detection rates. The proposed model has been tested against some of the attacks. The high detection rate has proved the appropriateness of R-Contiguous bit matching mechanism for anomaly detection in WSNs.

[1]  Antonio Alfredo Ferreira Loureiro,et al.  Decentralized intrusion detection in wireless sensor networks , 2005, Q2SWinet '05.

[2]  Yih-Chun Hu,et al.  Packet leashes: a defense against wormhole attacks in wireless networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[3]  Paul Helman,et al.  An immunological approach to change detection: algorithms, analysis and implications , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[4]  N. Jeyanthi,et al.  An Enhanced Entropy Approach to Detect and Prevent DDoS in Cloud Environment , 2013, Int. J. Commun. Networks Inf. Secur..

[5]  David A. Wagner,et al.  Secure routing in wireless sensor networks: attacks and countermeasures , 2003, Ad Hoc Networks.

[6]  Fabio A. González,et al.  An immunity-based technique to characterize intrusions in computer networks , 2002, IEEE Trans. Evol. Comput..

[7]  Jonathan Timmis,et al.  An interdisciplinary perspective on artificial immune systems , 2008, Evol. Intell..

[8]  Shahaboddin Shamshirband,et al.  Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks , 2014, J. Netw. Comput. Appl..

[9]  Ali Miri,et al.  An intrusion detection system for wireless sensor networks , 2005, WiMob'2005), IEEE International Conference on Wireless And Mobile Computing, Networking And Communications, 2005..

[10]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.

[11]  Zair Abdelouahab,et al.  EICIDS-Elastic and Internal Cloud-based Detection System , 2015 .

[12]  Dorothy E. Denning,et al.  An Intrusion-Detection Model , 1987, IEEE Transactions on Software Engineering.

[13]  Matt Welsh,et al.  Simulating the power consumption of large-scale sensor network applications , 2004, SenSys '04.

[14]  Hosam Soleman,et al.  SELF-PROTECTION MECHANISM FOR WIRELESS SENSOR NETWORKS , 2014 .

[15]  Stephanie Forrest,et al.  Coverage and Generalization in an Artificial Immune System , 2002, GECCO.

[16]  Ali Miri,et al.  A real-time node-based traffic anomaly detection algorithm for wireless sensor networks , 2005, 2005 Systems Communications (ICW'05, ICHSN'05, ICMCS'05, SENET'05).

[17]  Muhammad Zeeshan,et al.  An Immunology Inspired Flow Control Attack Detection Using Negative Selection with R-Contiguous Bit Matching for Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[18]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[19]  Anastasios A. Economides,et al.  ADLU: a novel anomaly detection and location-attribution algorithm for UWB wireless sensor networks , 2014, EURASIP J. Inf. Secur..