A Survey on Machine Learning Approaches for Developing Intrusion Detection System

Abstract

Artificial Intelligence is a cutting edge and profoundly modern technological application which has being recently used methodologies in the industry. This approach is inescapable and is broadly utilized for different applications. Machine Learning which is the well-known application of artificial intelligence is considered to be an essential requirement in numerous fields such as Account, Biomedical and Network Security research areas. Intrusion detection system is fundamentally utilized for securing system and data framework from malicious attacks. It screens the activity of host machine or a system. IDS keeps track of the state of hardware and software running in the networks for malicious activities that are planned for stealing data. Applying ML models can bring about low false alarm rate and high identification rate. Machine Learning methods can intelligently identify normal and malicious traffic with high accuracy. This research paper highlights different ML approaches utilized to create IDS. Through the broad study and investigation on current literature, the gap for improving and creating efficient IDS can be determined.