Novel approach for security in Wireless Sensor Network using bio-inspirations

Exploring the symbiotic nature of biological systems can result in valuable knowledge for computer networks. Biologically inspired approaches to security in networks are interesting to evaluate because of the analogies between network security and survival of human body under pathogenic attacks. Wireless Sensor Network (WSN) is a network based on multiple low-cost communication and computing devices connected to sensor nodes which sense physical parameters. While the spread of viruses in wired systems has been studied in-depth, applying trust in WSN is an emerging research area. Security threats can be introduced in WSN through various means, such as a benevolent sensor node turning fraudulent after a certain period of time. The proposed research work uses biological inspirations and machine learning techniques for adding security against such threats. While it uses machine learning techniques to identify the fraudulent nodes, consecutively by deriving inspiration from human immune system it effectively nullify the impact of the fraudulent ones on the network. Proposed work has been implemented in LabVIEW platform and obtained results that demonstrate the accuracy, robustness of the proposed model.

[1]  Huafeng Wu,et al.  Agent-based Trust Model in Wireless Sensor Networks , 2007, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007).

[2]  Subhash Challa,et al.  Survey of trust models in different network domains , 2010, ArXiv.

[3]  Yenumula B. Reddy,et al.  TRUST -BASED APPROACH IN WIRELESS SENSOR NETWORKS USING AN AGENT TO EACH CLUSTER , 2012 .

[4]  Sanjeev Jain,et al.  A Survey of Routing Attacks and Security Measures in Mobile Ad-Hoc Networks , 2011, ArXiv.

[5]  J. Tsitsiklis.  Efficient algorithms for globally optimal trajectories , 1995, IEEE Trans. Autom. Control..

[6]  Lixia Zhang,et al.  A taxonomy of biologically inspired research in computer networking , 2010, Comput. Networks.

[7]  Félix Gómez Mármol,et al.  Providing trust in wireless sensor networks using a bio-inspired technique , 2011, Telecommun. Syst..

[8]  Julie Greensmith,et al.  Artificial Immune Systems (2010) , 2010, ArXiv.

[9]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[10]  Rodrigo Roman,et al.  Trust management systems for wireless sensor networks: Best practices , 2010, Comput. Commun..

[11]  Bu-Sung Lee,et al.  Cross-Layer Detection of Sinking Behavior in Wireless Ad Hoc Networks Using SVM and FDA , 2011, IEEE Transactions on Dependable and Secure Computing.

[12]  Huafeng Wu,et al.  Add Semantic Role to Dependency Structure Language Model for Topic Detection and Tracking , 2007 .

[13]  Mohammed Feham,et al.  Novel hybrid intrusion detection system for clustered wireless sensor network , 2011, ArXiv.

[14]  A C Fowler,et al.  Approximate solution of a model of biological immune responses incorporating delay , 1981, Journal of mathematical biology.

[15]  Guang Yang.  Introduction to TCP / IP Network Attacks , 1998 .

[16]  Gregorio Martínez Pérez,et al.  Providing trust in wireless sensor networks using a bio-inspired technique , 2011 .

[17]  Heena Rathore,et al.  Bio-inspired machine learning based Wireless Sensor Network security , 2013, 2013 World Congress on Nature and Biologically Inspired Computing.

[18]  Özgür B. Akan,et al.  A survey on bio-inspired networking , 2010, Comput. Networks.

[19]  Zhou Su,et al.  Malicious node detection in wireless sensor networks using weighted trust evaluation , 2008, SpringSim '08.

[20]  Anazida Zainal,et al.  A Survey of Intrusion Detection Schemes in Wireless Sensor Networks , 2012 .

[21]  Subhash Challa,et al.  Probabilistic Modelling and Recursive Bayesian Estimation of Trust in Wireless Sensor Networks , 2010 .

[22]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[23]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[24]  Abhay Samant,et al.  A system for building immunity in social networks , 2012, 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC).

[25]  J. Tsitsiklis,et al.  Efficient algorithms for globally optimal trajectories , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[26]  Azzedine Boukerche,et al.  Trust-based security for wireless ad hoc and sensor networks , 2007, Comput. Commun..

[27]  Tomasz Bednarz,et al.  Discovery of Malicious Nodes in Wireless Sensor Networks Using Neural Predictors , 2007 .

[28]  Turgay Korkmaz,et al.  A Machine Learning Based Reputation System for Defending Against Malicious Node Behavior , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.