AN INTELLIGENT NETWORK INTRUSION DETECTION USING DATA MINING TECHNIQUES
DOI:
https://doi.org/10.11113/jt.v76.5891Keywords:
Network intrusion, network security, data mining, classification, optimizationAbstract
Network Intrusion Detection is to detect malicious attacks to the networks for different uses from military to enterprise. Currently available approaches either rely on the known network attacks or have high proportion of normal network traffics that were erroneously reported as anomalous traffics. The aim of this paper is to develop an efficient algorithm for intrusion detection without prior knowledge of network attacks. Uniquely, our approach will integrate a newly developed data mining technique for data feature classification with techniques commonly used for human detection. The key idea is to achieve on-line and automated learning of new attacks for precise and real-time intrusion detection.
References
Shukran, M. A. M., Chung, Y. Y., Yeh, W. C., Wahid, N., & Zaidi, A. M. A. 2011. Artificial Bee Colony Based Data Mining Algorithms for Classification Tasks. Modern Applied Science. 5(4): 217.
Shukran, M. A. B. M., Yunus, M. S. F. B. M., Maskat, K. B., Shariff, W. S. S. B., & Ariffin, M. S. B. 2013. Pixel Value Graphical Password Scheme-Graphical Password Scheme. Australian Journal of Basic and Applied Sciences. 7(4): 688-695.
http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
D. E. Denning. 1987. An Intrusion-Detection Model. IEEE Transactions on Software Engineering. 13(2): 222–232.
T. Chou, K. K. Yen, J. Luo, N. Pissinou, K. Makki. 2007. Correlation-based Feature Selection for Intrusion Detection Design. IEEE Xplore.
http://www.ll.mit.edu/IST/ideval.
Shukran, M. A. M., Chung, Y. Y., Yeh, W. C., Wahid, N., & Zaidi, A. M. A. 2011. Image Classification Technique using Modified Particle Swarm Optimization. Modern Applied Science. 5(5): 150.
B. K. Sy. 2005. Signature-based Approach for Intrusion Detection. In: P. Perner, A. Imiya (eds.) LNAI, Vol. 3587. Proceedings of the 4th Intern. Conf. on Machine Learning and Data Mining in Pattern Recognition, Leipzig, July 9-11. 526-636,
D. Brumley, J. Newsome, D. Song, H. Wang, S. jha. 2006. Towards Automatic Generation of Vulnerability-Based Signatures. Proceedings of the IEEE Symposium on Security and Privacy (S&P'06), May. 2-16.
J. P. Anderson. 1980. Computer Security Threat Monitoring and Surveillance. Technical report, J. P. Anderson Co., Ft. Washington, Pennsylvania, Apr.
K. Wang and S. Stolfo, p. Chan. 1997. Learning Patterns from Unix Process Execution Traces for Intrusion Detection. AAAI Workshop: AI Approaches to Fraud Detection and Risk Management, July.
K. Wang and S. Stolfo, Kui Mok. 1999. A Data Mining Framework for building Intrusion Detection Models. Proceedings of the 1999 IEEE Symposium on Security and Privacy, Oakland, CA, May.
S. Mukkamala, G. Janoski, and A. Sung. 2002. Intrusion Detection Using Neural Networks and Support Vector Machine. In International Joint Conference on Neural Networks (IJCNN).
L. Ertoz, E. Eilertson, A. Lazarevic, P. Tan, J. Srivastava, V. Kumar, P. Dokas. 2004. The Minds- Minnesota Intrusion Detection System. Next Generation Data Mining, MIT Press.
X. Xu, X. Wang. 2005. An Adaptive Network Intrusion Detection Method Based on PCA and Support Vector Machines. Proc. of 1st International Conference on Advanced Data Mining and Applications (ADMA'05), Wuhan, china. July 22-24.
L. Ertoz, A. Lazarevic, J. Srivastava, V. Kumar, A. Ozgur. 2003. A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection. Proc. of 3rd SIAM Conference on Data Mining, San Francisco, May.
Q. Wang, V. Mega. 2005. A Clustering Algorithm for Intrusion Detection. Proceeding SPIE Defense, Security and Sensing: Materials, Systems and Devices.
Y, Guan, A, Ghorbani, N. Belacel. 2003. Y-means: A Clustering Method for Intrusion Detection. Proceeding of Canadian Conference on Electrical and Computer Engineering. Montreal, Quebec, Canada, 3-4 May.
R. K. Belew and M. D. Vose. 1997. Foundations of Genetic Algorithms. Morgan Kaufmann. 4: 117-139.
J. Kennedy, R. Eberhart. Particle Swarm Optimization. Proc. IEEE Int'l. Conf. on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ. IV: 1942-1948.
K. Wang and S. Stolfo. 2005. Anomalous Payload-Based Worm Detection and Signature Generation. International Symposium on Recent Advances in Intrusion Detection (RAID).
K. Wang, J. J. Parekh, and S. Stolfo. 2006. Anagram: A Content Anomaly Detector Resistant to Mimicry Attack. International Symposium on Recent Advances in Intrusion Detection (RAID).
W. Yeh, W. Chang, Y. Y. Chung. 2008. A New Hybrid Approach for Data Mining Breast Cancer Pattern Using Discrete Particle Swarm Optimization and Statistical Method. Paper submitted to Expert Systems and Applications, Elsevier. (Accepted 01/12/2008).
Rafael, C. Gonzalez and Paul, WintzA tutorial on Principal Components Analysis, mail.iiit.ac.in/~mkrishna/PrincipalComponents.pdf.
X. He. 2007. Journal of Network and Computer Applications. Elsevier. 30.
X. He and Z. Lu. 2007. International Journal on Agent-Oriented Software Engineering. Inderscience. 1(2).
Y. Y. Chung, E. Choi, Z. Zhao, M. Shukran, D. Shi, F. Chen. 2007. Application of Vector Quantization for Content Based Music Retrieval System. WSEAS Transactions on Computers. 5(6): 793-798. ISSN 1109-2750. (EI, MathSci).
Y. Y. Chung. 2004. Evaluation of Clustering Algorithms for Image Retrieval System. International Journal of Information Technology. 1(1-4): 198-201. ISSN: 1305-239x.
H-S.Wang, Wei-Chang Yeh, P-C. Huang, W-W. Chang. Using Association Rules and Particle Swarm Optimization Approach for Part Change. Expert Systems with Applications. doi: 10.1016/j.eswa2008.10.026.
C. Bae, W. Yeh, Y. Y. Chung, X. He. 2008. A New Universal Generating Function Method for Estimating the Novel Multi-Resource Multistate Information Network Reliability. IEEE Transactions on Reliability. (TR2008-085, under review 09/2008) (Tier A Journal in CORE list 2008).
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.