ANOMALY DETECTION USING MACHINE LEARNING APPROACHES
- Hits: 580
Volume 3 (2), December 2020, Pages 196-206
Mausumi Das Nath, Tapalina Bhattasali
Due to the enormous usage of the Internet, users share resources and exchange voluminous amounts of data. This increases the high risk of data theft and other types of attacks. Network security plays a vital role in protecting the electronic exchange of data and attempts to avoid disruption concerning finances or disrupted services due to the unknown proliferations in the network. Many Intrusion Detection Systems (IDS) are commonly used to detect such unknown attacks and unauthorized access in a network. Many approaches have been put forward by the researchers which showed satisfactory results in intrusion detection systems significantly which ranged from various traditional approaches to Artificial Intelligence (AI) based approaches.AI based techniques have gained an edge over other statistical techniques in the research community due to its enormous benefits. Procedures can be designed to display behavior learned from previous experiences. Machine learning algorithms are used to analyze the abnormal instances in a particular network. Supervised learning is essential in terms of training and analyzing the abnormal behavior in a network. In this paper, we propose a model of Naïve Bayes and SVM (Support Vector Machine) to detect anomalies and an ensemble approach to solve the weaknesses and to remove the poor detection results.
Naïve Bayes, SVM, Hybrid Classifier, Ensemble, Anomaly Detection.
Abubakar, A., & Pranggono, B. (2017, September). Machine learning based intrusion detection system for software defined networks. In 2017 7th International Conference on Emerging Security Technologies (EST) (pp. 138-143). IEEE.
Almseidin, M., Alzubi, M., Kovacs, S., & Alkasassbeh, M. (2017, September). Evaluation of machine learning algorithms for intrusion detection system. In 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY) (pp. 000277-000282). IEEE.
Belavagi, M. C., & Muniyal, B. (2016). Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Computer Science, 89(2016), 117-123.
Dang, Q. V. (2019, November). Studying machine learning techniques for intrusion detection systems. In International Conference on Future Data and Security Engineering (pp. 411-426). Springer, Cham.
Esmaily, J., Moradinezhad, R., & Ghasemi, J. (2015, May). Intrusion detection system based on multi-layer perceptron neural networks and decision tree. In 2015 7th Conference on Information and Knowledge Technology (IKT) (pp. 1-5). IEEE.
Haq, N. F., Onik, A. R., Hridoy, M. A. K., Rafni, M., Shah, F. M., & Farid, D. M. (2015). Application of machine learning approaches in intrusion detection system: a survey. IJARAI-International Journal of Advanced Research in Artificial Intelligence, 4(3), 9-18.
Jabez, J., & Muthukumar, B. (2015). Intrusion detection system (IDS): anomaly detection using outlier detection approach. Procedia Computer Science, 48, 338-346.
Khan, L., Awad, M., & Thuraisingham, B. (2007). A new intrusion detection system using support vector machines and hierarchical clustering. The VLDB Journal, 16(4), 507-521.
Kumar, G., Thakur, K., & Ayyagari, M. R. (2020). MLEsIDSs: machine learning-based ensembles for intrusion detection systems – a review. The Journal of Supercomputing, 1-34.
Leung, K., & Leckie, C. (2005, January). Unsupervised anomaly detection in network intrusion detection using clusters. In Proceedings of the Twenty-eighth Australasian Conference on Computer Science-Volume 38 (pp. 333-342).
Li, Y., Xia, J., Zhang, S., Yan, J., Ai, X., & Dai, K. (2012). An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Systems with Applications, 39(1), 424-430.
Mohamad Tahir, H., Hasan, W., et al. (2015). Hybrid machine learning technique for intrusion detection system. In Proceedings of the 5th International Conference on Computing and Informatics (pp. 464-472).
Omar, S., Ngadi, A., & Jebur, H. H. (2013). Machine learning techniques for anomaly detection: an overview. International Journal of Computer Applications, 79(2), 33-41.
Othman, S. M., Ba-Alwi, F. M., Alsohybe, N. T., & Al-Hashida, A. Y. (2018). Intrusion detection model using machine learning algorithm on Big Data environment. Journal of Big Data, 5(1), 34.
Peddabachigari, S., Abraham, A., Grosan, C., & Thomas, J. (2007). Modeling intrusion detection system using hybrid intelligent systems. Journal of Network and Computer Applications, 30(1), 114-132.
Ren, J., Guo, J., Qian, W., Yuan, H., Hao, X., & Jingjing, H. (2019). Building an effective intrusion detection system by using hybrid data optimization based on machine learning algorithms. Security and Communication Networks, 1-11.
Sabhnani, M., & Serpen, G. (2003, June). Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context. In MLMTA (pp. 209-215).
Sen, J. (2010, July). An intrusion detection architecture for clustered wireless ad hoc networks. In 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks (pp. 202-207). IEEE.
Sinclair, C., Pierce, L., & Matzner, S. (1999, December). An application of machine learning to network intrusion detection. In Proceedings 15th Annual Computer Security Applications Conference (ACSAC’99) (pp. 371-377). IEEE.
Sultana, N., Chilamkurti, N., Peng, W., & Alhadad, R. (2019). Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications, 12(2), 493-501.
Tang, C., Xiang, Y., Wang, Y., Qian, J., & Qiang, B. (2016). Detection and classification of anomaly intrusion using hierarchy clustering and SVM. Security and Communication Networks, 9(16), 3401-3411.
Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). Intrusion detection by machine learning: A review. Expert Systems with Applications, 36(10), 11994-12000.
Vinchurkar, D. P., & Reshamwala, A. (2012). A Review of Intrusion Detection System Using Neural Network and Machine Learning. International Journal of Engineering Science and Innovative Technology, 1(2), 54-63.
Wahba, Y., ElSalamouny, E., & ElTaweel, G. (2015). Improving the performance of multi-class intrusion detection systems using feature reduction. arXiv preprint arXiv:1507.06692.
Wang, G., Hao, J., Ma, J., & Huang, L. (2010). A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Systems with Applications, 37(9), 6225-6232.
Yassin, W., Udzir, N. I., Muda, Z., & Sulaiman, M. N. (2013, August). Anomaly-based intrusion detection through k-means clustering and naives bayes classification. In Proc. 4th Int. Conf. Comput. Informatics, ICOCI (pp. 298-303).
Zhong, S., Khoshgoftaar, T. M., & Seliya, N. (2007). Clustering-based network intrusion detection. International Journal of Reliability, Quality and Safety Engineering, 14(02), 169-187.