Analysis of Intrusion Detection and Classification using Machine Learning Approaches


  • Anjum Khan M.Tech Scholar, Department of CSE, Sagar Institute of Research and Technology Bhopal, M.P., India
  • Anjana Nigam Professor Department of CSE,Sagar Institute of Research and Technology Bhopal, M.P., India



Intrusion Detection System, Anomaly Detection, Supervised learning, Unsupervised, Detection Rate.


 As the network primarily based applications are growing quickly, the network security mechanisms need a lot of attention to enhance speed and preciseness. The ever evolving new intrusion types cause a significant threat to network security. Though varied network security tools are developed, however the quick growth of intrusive activities continues to be a significant issue. Intrusion detection systems (IDSs) are wont to detect intrusive activities on the network. Analysis showed that application of machine learning techniques in intrusion detection might reach high detection rate. Machine learning and classification algorithms facilitate to design “Intrusion Detection Models” which might classify the network traffic into intrusive or traditional traffic. This paper discusses some usually used machine learning techniques in Intrusion Detection System and conjointly reviews a number of the prevailing machine learning IDS proposed by researchers at different times. in this paper an experimental analysis is performed to demonstrate the performance analysis of some existing techniques in order that they will be used further in developing Hybrid Classifier for real data packets classification. The given result analysis shows that KNN, RF and SVM performs best for NSL-KDD dataset.


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2. Garcia-Teodoro, P., “Anomaly-Based network intrusion detection: techniques”, systems and challenges. Comput. Security Vol. 28.Issue, pp. 18–28, 2009.

Sufyan T Faraj Al-Janabi, Hadeel Amjed Saeed, “A neural network based anomaly intrusion detection system”, IEEE, 2011.

J. Ryan, M. Lin, and R. Miikkulainen, “Intrusion Detection with Neural Networks,” Conference in Neural Information Processing Systems, 943–949.

A. K. Ghosh and A. Schwartzbard, “A Study in Using Neural Networks for Anomaly and Misuse Detection,” Conference on USENIX Security Symposium, Volume 8, pp. 12–12, 1999.

P. L. Nur, A. N. Zincir-heywood, and M. I. Heywood, “Host-Based Intrusion Detection Using Self-Organizing Maps,” in Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1714–1719, 2002.

K. Labib and R. Vemuri, “NSOM: A Real-Time Network-Based Intrusion Detection System Using Self-Organizing Maps,” 2000.

Sharma, R.K., Kalita, H.K., Issac, B., “Different firewall techniques: a survey”, International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, 2014.

Meng, Y.-X., “The practice on using machine learning for network anomaly intrusion detection”, International Conference on Machine Learning and Cybernetics (ICMLC), Vol. 2, IEEE, 2011.

Sumaiya Thaseen Ikram, Aswani Kumar Cherukuri, “Intrusion detection model using fusion of chi-square feature selection and multi class SVM”, Journal of King Saud University –Computer and Information Sciences, 2016.

Manjula C. Belavagi and Balachandra Muniyal, “Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection, Procedia Computer Science”, Elsevier, 2016.

Saad Mohamed Ali Mohamed Gadal and Rania A. Mokhtar, “Anomaly Detection Approach using Hybrid Algorithm of Data Mining Technique”, International Conference on Communication, Control, Computing and Electronics Engineering, IEEE, 2017.

Ibrahim, H. E., Badr, S. M., & Shaheen, M. A., “Adaptive layered approach using machine learning techniques with gain ratio for intrusion detection systems”, International Journal of Computer Applications, Vol. 56, Issue 7, pp. 10–16, 2012.

Wen Feng, Qinglei Zhang, Gongzhu Hu, Jimmy Xiang Huang, “Mining network data for intrusion detection through combining SVMs with ant colony networks”, Elsevier, Vol 37, pp 127-140, 2014.

Shi-JinnHorng, Ming-Yang Su, Yuan-Hsin Chen, Tzong-Wann Kao, Rong-Jian Chen, Jui-Lin Lai, Citra Dwi Perkasa, “A novel intrusion detection system based on hierarchical clustering and support vector machines” Expert Systems with Applications, Elsevier, Vol. 38, pp. 306–313, 2011.

Kuang, F., Xu, W., & Zhang, S., “A novel hybrid KPCA and SVM with GA model for intrusion detection”, Applied Soft Computing Journal, Vol. 18, pp. 178–184, 2014.

Prasanta Gogoi, D.K. Bhattacharyya, B. Borah1 and Juga, K. Kalita, “MLH-IDS: A Multi-Level Hybrid Intrusion Detection Method”, The Computer Journal, Vol. 57 Issue 4, pp. 602-623, 2014.

Wathiq Laftah Al-Yaseen , Zulaiha Ali Othman ,Mohd Zakree Ahmad Nazri, “Multi-Level Hybrid Support Vector Machine and Extreme Learning Machine Based on Modified K-means for Intrusion Detection System”, International Journal in Expert Systems With Applications, Elsevier, 2017.

He, L., “An improved intrusion detection based on neural network and fuzzy algorithm. Journal of Networks, Vol. 9, Issue 5, pp. 1274–1280, 2014.

Hoque, M. S., Mukit, M. A. , & Bikas, M. A. N., “An implementation of intrusion detection system using genetic algorithm”, International Journal of Network Security & Its Applications, Vol 4, Issue 2, pp. 109–120, 2012.




How to Cite

Khan, A., & Nigam, A. (2017). Analysis of Intrusion Detection and Classification using Machine Learning Approaches. SMART MOVES JOURNAL IJOSCIENCE, 3(10), 9–13.