Trustworthy distributed collaborative intrusion detection using mobile agents

A distributed environment is one in which intrusions are prevalent and drastically affect the performance of the networks. Therefore a need for Intrusion Detection Networks arises. Collaborators could be used here to enhance the detection of attacks. But still this concept suffers from lack of trustworthiness in the distributed environment. We introduce Mobile Agents (MA) to handle this problem effectively. A DCE - TRUST architecture is proposed where MAs are used to migrate from one node to another autonomously within the range of collaborators in and out of each network to inform them about the detected intrusions accurately and in a timely manner. We also found that the proposed architecture enhances detection rate and minimizes the rate in which each node gets to know about the attacks.

[1]  Parag Kulkarni,et al.  Intrusion Detection System using Self Organizing Maps , 2009, 2009 International Conference on Intelligent Agent & Multi-Agent Systems.

[2]  C. Leckie,et al.  A peer-to-peer collaborative intrusion detection system , 2005, 2005 13th IEEE International Conference on Networks Jointly held with the 2005 IEEE 7th Malaysia International Conf on Communic.

[3]  R. S. Bhuvaneswaran,et al.  Design of genetic algorithm based IDS for MANET , 2012, 2012 International Conference on Recent Trends in Information Technology.

[4]  Raouf Boutaba,et al.  Robust and scalable trust management for collaborative intrusion detection , 2009, 2009 IFIP/IEEE International Symposium on Integrated Network Management.

[5]  Jingsha He,et al.  A Distributed Intrusion Detection Scheme for Wireless Sensor Networks , 2008, 2008 The 28th International Conference on Distributed Computing Systems Workshops.

[6]  Yu-Xin Meng,et al.  The practice on using machine learning for network anomaly intrusion detection , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[7]  Xenofontas A. Dimitropoulos,et al.  Histogram-based traffic anomaly detection , 2009, IEEE Transactions on Network and Service Management.

[8]  Risto Vaarandi Real-time classification of IDS alerts with data mining techniques , 2009, MILCOM 2009 - 2009 IEEE Military Communications Conference.

[9]  Bin-Xing Fang,et al.  A Lightweight Intrusion Detection Model Based on Feature Selection and Maximum Entropy Model , 2006, 2006 International Conference on Communication Technology.

[10]  Raouf Boutaba,et al.  Effective Acquaintance Management based on Bayesian Learning for Distributed Intrusion Detection Networks , 2012, IEEE Transactions on Network and Service Management.

[11]  Karsten P. Ulland,et al.  Vii. References , 2022 .

[12]  Christopher Krügel,et al.  Applying Mobile Agent Technology to Intrusion Detection , 2001 .

[13]  Raouf Boutaba,et al.  Dirichlet-Based Trust Management for Effective Collaborative Intrusion Detection Networks , 2011, IEEE Transactions on Network and Service Management.

[14]  R. Saravanan,et al.  INTELLIGENT INTRUSION DETECTION SYSTEM FRAMEWORK USING MOBILE AGENTS , 2009 .

[15]  International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, July 11-14, 2010, Proceedings , 2010, ICMLC.

[16]  Genetic Algorithms for Feature Selection in Data Mining , .