Improving the Performance of Intrusion Detection System using Machine Learning based Approaches
Helmi Rais, Tahir Md, Mehmood, Han-Ching Wu, Shou-Hsuan Stephen Huang, S Lin, K Ying, C Lee, Z Lee, J Jabez, B Muthukumar, Ahmad Javaid, Quamar Niyaz, Weiqing Sun, Mansoor Alam, Adel Eesa, Zeynep Sabry, Adnan Orman, Abdulazeez Mohsin, Brifcani, Koc, Thomas Levent, Shahram Mazzuchi, Sarkani, Rana Ashfaq, Xi-Zhao Raza, Joshua Wang, Haider Huang, Yu-Lin Abbas, He, Anish Halimaa, K Sundarakantham, Shadi Aljawarneh, Monther Aldwairi, Muneer Bani Yassein, Sharmila Wagh, Satish Kishor, Kolhe, Praveen Kollu, R Prasad, G Pavithra, P Abirami, S Bhuvaneshwari, S Dharani, B Haridharani, Ghanshyam Dubey, Rakesh Prasad, Bhujade Kumar
Abstract
The purpose of artificial intelligence is to make machine intelligence and machine learning enables them to acquire knowledge. Machine learning (ML) a branch of artificial intelligence, make machine self-learner. This self-learning ability will help to solve many complex problems. Using a Machine learning Intrusion detection system can make it more efficient and capable to detect new attack patterns by self-learning or acquiring knowledge. IDS are the first line of defense that obtain information or knowledge from a network and analyze it to determine elements that are responsible for violating the security policies of computer and networks. In this paper importance of machine learning is discussed because of the betterment of the intrusion detection system.