Anomaly based Intrusion Detection using Modified Fuzzy Clustering
DOI:
https://doi.org/10.9781/ijimai.2017.05.002Keywords:
Fuzzy, Clustering, Anomaly Detection, Intrusion Detection, Principal Component Analysis, Robust Spatial Kernel Fuzzy C-MeansAbstract
This paper presents a network anomaly detection method based on fuzzy clustering. Computer security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. As a result, Intrusion Detection System has become an indispensable component of computer security. The proposed method consists of three steps: Pre-Processing, Feature Selection and Clustering. In pre-processing step, the duplicate samples are eliminated from the sample set. Next, principal component analysis is adopted to select the most discriminative features. In clustering step, the network samples are clustered using Robust Spatial Kernel Fuzzy C-Means (RSKFCM) algorithm. RSKFCM is a variant of traditional Fuzzy C-Means which considers the neighbourhood membership information and uses kernel distance metric. To evaluate the proposed method, we conducted experiments on standard dataset and compared the results with state-of-the-art methods. We used cluster validity indices, accuracy and false positive rate as performance metrics. Experimental results inferred that, the proposed method achieves better results compared to other methods.Downloads
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Ahmed, M., Mahmood, A. N., and Hu, J. “A Survey of Network Anomaly Detection Techniques.” Journal of Network and Computer Applications, vol. 60, pp. 19–31, 2016.
Kumar, S. V. A., and Harish, B. S. “Segmenting MRI Brain Images Using Novel Robust Spatial Kernel FCM (RSKFCM).” In Eighth International Conference on Image and Signal Processing, pp. 38–44, 2014.
Bezdek, J. C., Ehrlich, R., and Full, W. “FCM: The Fuzzy C-Means Clustering Algorithm.” Computers & Geosciences, vol. 10, no. 2–3, pp. 191–203, 1984.
Bharti, K., Shukla, S., and Jain, S. “Intrusion Detection Using Unsupervised Learning.” International Journal on Computer Science and Engineering, vol. 1, no. 2, pp. 1865–1870, 2010.
Camacho, J. “Visualizing Big Data with Compressed Score Plots: Approach and Research Challenges.” Chemometrics and Intelligent Laboratory Systems, vol. 135, pp. 110–125, 2014.
Chimphlee, W., Abdullah, A. H., Sap, M. N. M., Srinoy, S., and Chimphlee, S. “Anomaly-Based Intrusion Detection Using Fuzzy Rough Clustering.” In IEEE International Conference on Hybrid Information Technology (ICHIT’06), vol. 1, pp. 329–334, 2006.
Denning, D. E. “An Intrusion-Detection Model.” IEEE Transactions on Software Engineering, no. 2, pp. 222–232, 1987.
Fukuyama, Y., and Sugeno, M. “A New Method of Choosing the Number of Clusters for Fuzzy C-Means Method.” In Proceedings of the Fifth Fuzzy Systems Symposium, pp. 247–250, 1989.
Ganapathy, S., Kulothungan, K., Yogesh, P., and Kannan, A. “A No Accuracy Weighted Fuzzy C-Means Clustering Based on Immune Genetic Algorithm for Intrusion Detection.” Procedia Engineering, vol. 38, pp. 1750–1757, 2012.
Guorui, F., Xinguo, Z., and Jian, W. “Intrusion Detection Based on the Semi-Supervised Fuzzy C-Means Clustering Algorithm.” In 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), IEEE, pp. 2667–2670, 2012.
Hameed, S. M., Saad, S., and AlAni, M. F. “An Extended Modified Fuzzy Possibilistic C-Means Clustering Algorithm for Intrusion Detection.” Lecture Notes on Software Engineering, vol. 1, no. 3, p. 273, 2013.
Hotelling, H. “Analysis of a Complex of Statistical Variables into Principal Components.” Journal of Educational Psychology, vol. 24, no. 6, p. 417, 1933.
Jianliang, M., Haikun, S., and Ling, B. “The Application on Intrusion Detection Based on K-Means Cluster Algorithm.” In Information Technology and Applications, 2009. IFITA ’09. International Forum on, vol. 1, pp. 150–152, 2009.
Jun-lan, Y. “Intrusion Detection Technology and Its Future Trend.” Information Technology, vol. 4, pp. 172–176, 2006.
Kushwaha, P., and Welekar, R. “Feature Selection for Image Retrieval Based on Genetic Algorithm.” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, pp. 16–21, 2016.
Karthik, G., and Nagappan, A. “Intrusion Detection System Using Kernel FCM Clustering and Bayesian Neural Network.” International Journal of Computer Science and Information Technology and Security, vol. 3, no. 6, pp. 391–399, 2013.
Khazaee, S., and Rad, M. S. “Using Fuzzy C-Means Algorithm for Improving Intrusion Detection Performance.” In 2013 13th Iranian Conference on Fuzzy Systems, pp. 27–29, 2013.
Khazaee, S., and Faez, K. “A Novel Classification Method Using Hybridization of Fuzzy Clustering and Neural Networks for Intrusion Detection.” International Journal of Modern Education and Computer Science, vol. 6, no. 11, p. 11, 2014.
Kumar, G. R., Mangathayaru, N., and Narsimha, G. “An Approach for Intrusion Detection Using Fuzzy Feature Clustering.” In IEEE International Conference on Engineering & MIS (ICEMIS), pp. 1–8, 2016.
Pal, N. R., and Bezdek, J. C. “On Cluster Validity for the Fuzzy C-Means Model.” IEEE Transactions on Fuzzy Systems, vol. 3, no. 3, pp. 370–379, 1995.
Pandeeswari, N. R., and Kumar, G. “Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN.” Mobile Networks and Applications, vol. 21, no. 3, pp. 494–505, 2016.
Bezdek, J. C. Pattern Recognition with Fuzzy Objective Function Algorithms. Springer Science & Business Media, 2013.
Ren, W., Cao, J., and Wu, X. “Application of Network Intrusion Detection Based on Fuzzy C-Means Clustering Algorithm.” In Third International Symposium on Intelligent Information Technology Application (IITA), vol. 3, IEEE, pp. 19–22, 2009.
Rustam, Z., and Talita, A. S. “Fuzzy Kernel C-Means Algorithm for Intrusion Detection Systems.” Journal of Theoretical and Applied Information Technology, vol. 81, no. 1, p. 161, 2015.
Sampat, R., and Sonawani, S. “Network Intrusion Detection Using Dynamic Fuzzy C-Means Clustering.” Network, vol. 2, no. 4, 2015.
Seth, H., and Bay, S. D. The UCI KDD Archive [http://kdd.ics.uci.edu
]. Irvine, CA: University of California, Department of Information and Computer Science, 1999.
Surana, S. “Intrusion Detection Using Fuzzy Clustering and Artificial Neural Network.” In Advances in Neural Networks, Fuzzy Systems and Artificial Intelligence, ISBN 978-960-474-379-7, 2013.
Syarif, I., Prugel-Bennett, A., and Wills, G. “Unsupervised Clustering Approach for Network Anomaly Detection.” In Networked Digital Technologies, pp. 135–145, 2012.
Wang, H., Zhang, Y., and Li, D. “Network Intrusion Detection Based on Hybrid Fuzzy C-Mean Clustering.” In Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 1, pp. 483–486, 2010.
Xie, X. L., and Beni, G. “A Validity Measure for Fuzzy Clustering.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 841–847, 1991.
Xie, L., Wang, Y., Chen, L., and Yue, G. “An Anomaly Detection Method Based on Fuzzy C-Means Clustering Algorithm.” In Second International Symposium on Networking and Network Security (ISNNS 2010), Jinggangshan, PR China, pp. 89–92, 2010.
Yaseen, W. L., Zulaiha, A. O., and Zakree, A. N. M. “Multi-Level Hybrid Support Vector Machine and Extreme Learning Machine Based on Modified K-Means for Intrusion Detection System.” Expert Systems with Applications, vol. 67, pp. 296–303, 2017.
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