Submit your paper : editorIJETjournal@gmail.com Paper Title : Intrusion detection system ISSN : 2395-1303 Year of Publication : 2020 10.29126/23951303/IJET-V6I4P7 MLA Style: -Ambermani Pratap Singh,Vanishree K. " Intrusion detection system" Volume 6 - Issue 4(31-39) July - August,2020 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: -Ambermani Pratap Singh,Vanishree K. " Intrusion detection system" Volume 6 - Issue 4(31-39) July - August,2020 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract - Intrusion detection system or IDS is an application device or software that controls and monitors over the network or system activities and ensures none malicious tasks are being carried out. IDS finds and checks over any unwanted activities being carried out. The need of IDS has increased to a great extent because of growth and increased interaction of web and internet throughout the complete globe. This raise has resulted into greater cause of concern regarding the network communication and ensuring the safety of many secured digital data and information. So it’s the primary job to preserve such important and secured information. Because of so much of upsurge of web globally hackers have many new techniques and practices in their bank to disregard the safety of our valuable info. So many of the intrusion detection system have devised several algorithms and techniques to help and safeguard against such attacks by hackers and intruders. The primary objective of this paper is to provide summarized study of IDS, techniques and algorithms behind the IDS, types of IDS available in the market, various ways of attacks, tools techniques and challenges faced, research and development against these challenges and many future scope of improvements in this field. Reference 1. Hodo, E., Bellekens, X., Hamilton, A., Tachtatzis, C. and Atkinson, R., 2017. Shallow and deep networks intrusion detection system: A taxonomy and survey. arXiv preprint arXiv:1701.02145. 2. Manzoor, M.A. and Morgan, Y., 2017. Network intrusion detection system using apache storm. Probe, 4107, p.4166. 3. Nassar, M., al Bouna, B., and Malluhi, Q., 2013, June. Secure outsourcing of network flow data analysis. In Big Data (BigData Congress), 2013 IEEE International Congress on (pp. 431- 432). IEEE. 4. Suthaharan, S., 2014. Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), pp.70-73. 5. Meng, F., Fu, Y. and Lou, F., 2018, March. A network threat analysis method combined with kernel PCA and LSTM-RNN. In Advanced Computational Intelligence (ICACI), 2018 Tenth International Conference on (pp. 508-513). IEEE. 6. Staudemeyer, R.C., 2015. Applying long short- term memory recurrent neural networks to intrusion detection. South African Computer Journal, 56(1), pp.136-154. 7. Shone, N., Ngoc, T.N., Phai, V.D. and Shi, Q., 2018. A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), pp.41-50. 8. Lee, B., Amaresh, S., Green, C. and Engels, D., 2018. Comparative Study of Deep Learning Models for Network Intrusion Detection. SMU Data Science Review, 1(1), p.8. 9. Mukkamala, S., Janoski, G. and Sung, A., 2002. Intrusion detection using neural networks and support vector machines. In Neural Networks, 2002. IJCNN’02. Proceedings of the 2002 International Joint Conference on (Vol. 2, pp. 1702-1707). IEEE. 10. Ahmed, U. and Masood, A., 2009, October. Host based intrusion detection using RBF neural networks. In Emerging Technologies, 2009. ICET 2009. International Conference on (pp. 48- 51). IEEE. Keywords Intrusion Detection System, Machine Learning, Deep Learning, Cyber Security, Network Intrusion Detection System(NIDS). |