ttp://iaeme.com/Home/journal/IJCET 181 editor@iaeme.com h International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 5, September-October 2018, pp. 181 189, Article ID: IJCET_09_05_021 – Available online at ttp://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5 h Journal Impact Factor (2016 9.3590(Calculated by GISI) www.jifactor.com ): ISSN Print: 0976-6367 and ISSN Online: 0976 6375 – © IAEME Publication PERFORMANCE EVOLUTION OF MACHINE LEARNING ALGORITHMS FOR NETWORK INTRUSION DETECTION SYSTEM Kushal Jani M.E Student, GTU Cyber Security, School of Engineering & Technology Gandhinagar 382028, Gujarat, India Punit Lalwani Project Scientist, Bhaskaracharya Institute for Space Applications and Geo-Informatics, Gandhinagar 382007, India Deepak Upadhyay Assistant Professor, GTU Cyber Security, School of Engineering & Technology Gandhinagar 382028, Gujarat, India Dr. M. B. Potdar Project Director, Bhaskaracharya Institute for Space Applications and Geo-Informatics, Gandhinagar 382007, India ABSTRACT Network Intrusion Detection System (NIDS) is one of the best solutions against network attacks. Attackers also dynamically change tools and technologies. However, implementing an associated accepted NIDS system is an additional challenge. This paper conducts and analyzes many experiments to evaluate numerous machine learning techniques that support the NSL-KDD intrusion data set. We have succeeded in identifying a number of performance metrics to judge the chosen technology. The main focus was on accuracy, precision and recall performance metrics to enhance the detection rate of network intrusion detection systems. Experimental results show that the deep learning approach achieves the highest accuracy and detection rate, while false negatives and false positives are rarely achieved. Key words: Network Intrusion Detection System, Machine learning, Network Security, Performance Evolution. Cite this Article: Kushal Jani, Punit Lalwani, Deepak Upadhyay, Dr. M.B. Potdar, Performance Evolution of Machine Learning Algorithms for Network Intrusion Detection System. International Journal of Computer Engineering and Technology, 9(5), 2018, pp. 181 189. - ttp://iaeme.com/Home/issue/IJCET?Volume=9&Issue=5 h