Distributed Intrusion Detection System Using P2P Agent Mining ...

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Vol 5. No. 2, March, 2012 ISSN 2006-1781African Journal of Computing & ICT© 2012 Afr J Comp & ICT – All Rights Reservedwww.ajocict.netTable 3: Decision Tree AnalysisData Metrics Simple C4.5 NBTreeTypesCARTDoS Time to 10.71 1.05 31.81ModelSize of Tree 13 68 64Accuracy 100 100 100TP Rate 1 1 1FP Rate 0 0 0Probe Time to 1.22 0.17 10.82ModelSize of Tree 13 19 10Accuracy 99.7952 99.0964 100TP Rate 0.988 0.991 1FP Rate 0.007 0.007 0R2L Time to 1.87 0.35 13.52ModelSize of Tree 9 3 12Accuracy 100 99.8471 99.5413TP Rate 1 0.998 0.995FP Rate 0 0.002 0.005U2R Time to 0.04 0.01 4.000ModelSize of Tree 3 9 2Accuracy 72.7273 72.7273 62.634TP Rate 0.727 0.727 0.636FP Rate 0.152 0.152 0.303The experimental study of the proposed DIDS is currentlybeing carried out with a view to develop the variouscomponents of the system and implement the system in JavaAgent Development Environment. In future, the issues ofknowledge and cost management in the context of agentmining based DIDS can be examined. The scheme can as wellbe extended to Intrusion Response System.ChangeProcessData PreprocessingAgent ReactiveActivityProcessData Mining Activity5. CONCLUSIONThe process of securing network resources mostly indistributed network system at this information critical erarequires a well articulated and complex system. Although, theuse of agent and data mining is not new in distributedintrusion detection system, however, there interaction forintrusion is novel. So, an attempt has been made in this paperto present these two distinct areas in incorporating terms todetect and prevent attacks. The theoretical concept proposedfor the DIDS is peer-to-peer agent mining integration.Agent ProactiveActivityDeriveGoalKnowledgeBaseFig 3. UML Activity Diagram of the Proposed Agent andData Mining InteractionFig.3. UML Activity Diagram of the Proposed Agent andData Mining Interaction8
Vol 5. No. 2, March, 2012 ISSN 2006-1781African Journal of Computing & ICT© 2012 Afr J Comp & ICT – All Rights Reservedwww.ajocict.netLevel 3Level 2Level 1HIDSAgentNIDSTrustAssuranceAgentData AnalysisAgentData MiningHIDSMobility,Cooperation&CommunicationCooperation &CommunicationHIDSSecurityAgentNIDSData SharingAgentData MiningAgentFig. 4. Schematic Diagram of the System CompositionREFERENCES[1] A. Abraham , R. Jain, J. Thomas and S.Y. Han , “D-SCIDS: Distributed soft computing intrusion detectionsystem,” Journal of Network and Computer Applications,Vol. 30, 2007, pp 81–98.[2] M. Almeida and B. Mutina (2011) Defacement Statistics2002-2010. http://www.zone-h.org. Accessed May 5,2011.[3] S. Androutsellis-Theotokis and D. Spinellis, “A Survey ofPeer-to-Peer Content Distribution Technologies,” ACMComputing Surveys, vol. 36, No. 4, 2004, 335–371.[4] J. Barrus and N.C. Rowe, “A distributed autonomous-agentnetwork-intrusion detection and response system,” InCommand and Control Research and TechnologySymposium, pages 577–586, Monterey, CA, June 1998..[5] I. Brahmi, S.B.Yahia, and P. Poncelet “MAD-IDS: NovelIntrusion Detection System using Mobile Agents and DataMining approaches,”2010.[6] S.T. Brugger, “Data Mining Methods for NetworkIntrusion Detection,” Unpublished Ph. D. ThesisUniversity of California, Davis, 2004 [7] V.Chatzigiannakis., G. Androulidakis, M. Grammatikou andB. Maglaris, “A distributed intrusion detection prototypeusing security agents,” In 11th Workshop of theHPOVUA, June 2004.[8] J.C. Da Silva, C. Giannella, R. Bhargava, H. Karguptaand M. Klusch, “Distributed Data Mining and Agents,”9th international workshop on cooperative informationagents (CIA 2004)[9] M. Eid, H. Artail, A. Kayssi and A. Chehab, "Trends inmobile agent application. Journal of Research and Practicein Information Technology,37(4), 2005.[10] N. Einwechter (2001) "An Introduction to DistributedIntrusion Detection Systems,”http://online.securityfocus.com/infocus/1532. AccessedMay 6, 2011.[11] V. Engen, “Machine learning for network based Intrusiondetection: An investigation into discrepancies in findingswith the kdd cup ’99 data set and multi-objective evolutionof neural network classifier ensembles for imbalanceddata. Unpublished Ph.D. Thesis of BournemouthUniversity, 2010.[12] D. Frincke, D. Tobin, J. Mcconnell, J. Marconi and D.Polla, “A framework for cooperative intrusion detection”In 21st National Information Systems SecurityConference, pages 361– 373, October 1998.[13] C. Fung, “Collaborative Intrusion Detection Networksand Insider Attacks,” Journal of Wireless MobileNetworks, Ubiquitous Computing, and Dep endableApplications, volume: 2, number: 1, 2011, pp. 63-74[14] R. Gopalakrishna andE.H. Spafford, “A framework fordistributed intrusion detection using interest drivencooperating agents,” 2001.9
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