Contribution to Abnormality Detection by Use of Clust-Density Algorithm

Chaimae Saadi; Habiba Chaoui; Hassan Erguig

doi:10.15866/irecos.v10i4.5699

Contribution to Abnormality Detection by Use of Clust-Density Algorithm

Chaimae Saadi^(1*), Habiba Chaoui⁽²⁾, Hassan Erguig⁽³⁾

^(*) Corresponding author

Authors' affiliations

DOI: https://doi.org/10.15866/irecos.v10i4.5699

Abstract

In recent years with the remarkable development of technologies, in particular the Internet, the number of attacks has increased significantly. For this purpose, the use of an intrusion detection system has become an indispensable need for the fight against cybercrime in complex environments. For this, the evolution of the IDS is required to study the abnormal behavior, tactics and motivations hackers and ensure a higher level of security. In this paper, we propose an abnormally detection system based on new data mining algorithm called clust-density. We also analyze and implement tests in real use case to prove correctness of our approach and performance compared to other algorithms.
Copyright © 2015 Praise Worthy Prize - All rights reserved.

Keywords

Anomaly Detection; Datamining; DBSCAN; K-Medoid; Clust-Density

Full Text:

PDF

References

G. Helmer, J.S.K. Wong, V.G. Honavar, and L. Miller. Automated Discovery of Concise Predictive Rules for Intrusion Detection. Journal of Systems and Software, 60(3):165–175, 2002.
http://dx.doi.org/10.1016/s0164-1212(01)00088-7

S. Stolfo, A.L. Prodromidis, S. Tselepis, W. Lee, D.W. Fan, and P.K. Chan. JAM: Java Agents for Meta-Learning over Distributed Databases, newport beach, california. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining,,pages 74–81, 1997.

A. S. Sodiya. Multi-Level and Secured Agent-based Intrusion Detection System. Journal Of Computing and InformationTechnology,14(3):217–223, 2006.
http://dx.doi.org/10.2498/cit.2006.03.05

M.-L. Shyu and V. Sainani. A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classiﬁcation, Springer Verlag US, Data Mining and Multi-agent Integration edition, chapter 8, pages 127–142.2009.
http://dx.doi.org/10.1007/978-1-4419-0522-2_8

Imen Brahmi, Sadok Ben Yahia, Pascal Poncelet, MAD-IDS: Novel Intrusion Detection System using Mobile Agents and Data Mining Approaches, Lecture Notes in Computer Science Volume 6122, 2010, pp 73-76.
http://dx.doi.org/10.1007/978-3-642-13601-6_9

Eugene H. Spafford and Diego Zamboni, Intrusion detection using autonomous agents, Computer Networks, 34(4):547-570, October 2000.
http://dx.doi.org/10.1016/s1389-1286(00)00136-5

Zhihao PENG, Guanyu LI, An Intelligent Immunity-based Model for Distributed Intrusion Detection, CAMAN 2012.

Guillaume CALAS Spécialisation Sciences Cognitives et InformatiqueAvancée 14-16 rueVoltaireLe Kremlin-Bicêtre,France, 94270

Aarti Singh, Dimple Juneja, A.K. Sharma, Agent Development Toolkits, International Journal of Advancements in Technology, Vol 2, No 1 (January 2011).
http://dx.doi.org/10.1109/csae.2011.5952430

Verma, M., Srivastava, M., Chack, N., Diswar, A. K., & Gupta, N. (2012). A Comparative Study of Various Clustering Algorithms in Data Mining, International Journal of Computer Applications (0975 – 8887) Volume 45– No.23, May 2012

Gandhi, G., & Srivastava, R., Review Paper: A Comparative Study on Partitioning Techniques of Clustering Algorithms, International Journal of Computer Applications, 87(9), 10–13. (2014).
http://dx.doi.org/10.5120/15235-3770

Erman, J., Arlitt, M., Mahanti, A., Methodologies, I. C., & Recognition, P. (n.d.). Traffic Classification Using Clustering Algorithms, ACM International Conference Proceeding Series (ICPS) 281–286.

Imen Brahmi, Sadok Ben Yahia, Pascal Poncelet AD-Clust : Détection des anomalies basée sur le Clustering. In: Actes de l’atelier Clustering Incrémental et méthodes de Détection de Nouveautés (CIDN) en conjonction avec la 11ème Conférence Francophone d’Extraction et de Gestion de Connaissances (EGC’11), Brest, France. pp. : 27–41 (2011)

Ankita Agarwal et all MULTI AGENT BASED APPROACH FOR NETWORK INTRUSION DETECTION USING DATA MINING CONCEPT Journal of Global Research in Computer Science Volume 3, No. 3, March 2012

O. Oriola and All Rights Reserved www.ajocict.net 3 Distributed Intrusion Detection System Using P2P Agent Mining Scheme Department of Computer Science Adekunle Ajasin University Akungba Akoko, Nigeria A.B. Adeyemo & A.B.C. Robert Department of Computer Science University of Ibadan Ibadan, Nigeria African Journal of Computing & ICT 2012.

Mahendra Tiwari1 Randhir Singh2 Comparative Investigation of K-Means and K-Medoid Algorithm on Iris Data International Journal of Engineering Research and Development eISSN : 2278-067X, pISSN : 2278-800X, www.ijerd.com Volume 4, PP. 69-72 Issue 8 (November 2012).

SANJAY CHAKRABORTY Prof. N.K.NAGWANI Analysis and Study of Incremental DBSCAN Clustering Algorithm International Journal of Enterprise Computing and Business Systems ISSN (Online) : 2230-8849 Vol. 1 Issue 2 July 2011

Hany Nashat Gabra Classification of IDS Alerts with Data Mining Techniques International Journal of Electronic Commerce Studies Vol.5, No.1 , pp.1-6, 2014

Dr. M.P.S Bhatia1 and Deepika Khurana Experimental study of Data clustering using k-Means and modified algorithms International Journal of Data Mining & Knowledge Management Process (IJDKP) DOI : 10.5121/ijdkp.2013.3302 17 Vol.3, No.3, May 2013
http://dx.doi.org/10.5121/ijdkp.2013.3302

M. Awawdeh, A. Fedi, MATLAB-Based Graphical User Interface (GUI) for Data Mining as a Tool for Environment Management, World Academy of Science, Engineering and Technology International Journal of Computer, Control, Quantum and Information Engineering Vol:8 No:1, 2014

Chaimae Saadi et al, Security Analysis Using IDs Based on Mobile Agents and Data Mining Algorithms / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (1) 597-602, 2015,

Prof. T. Ramanujam Ram Kumar Singh Intrusion Detection System Using Advanced Honeypots (IJCSIS) International Journal of Computer Science and Information Security, Vol. 2, No. 1, 2009.

Vinila Jinny, S., Jayakumari, J., Comparative analysis of intrusion detection system with mining, (2013) International Review on Computers and Software (IRECOS), 8 (10), pp. 2540-2544.

Bakla, A., El-Koutly, R., Intrusion detection system using artificial immune system and new multi core technology, (2013) International Review on Computers and Software (IRECOS), 8 (5), pp. 1072-1075.

Nirmaladevi, P., Tamilarasi, A., An efficient intrusion detection system based on GA to recognize attacks in user privileges, (2013) International Review on Computers and Software (IRECOS), 8 (8), pp. 1917-1922.

Refbacks

There are currently no refbacks.

Username
Password
Remember me