IDS Using Machine Learning - Current State of Art and Future Directions

The prosperity of technology worldwide has made the concerns of security tend to increase rapidly. The enormous usage of Internetworking has raised the need of protecting systems as well as networks from the unauthorized access or intrusion. An intrusion is an activity of breaking into the system by compromising the security policies, and the process of analyzing the network data for the possible intrusions is Intrusion Detection. For the last two decades automatic intrusion detection system has been an important research topic. Up to the moment, researchers have developed Intrusion Detection Systems (IDS) capable of detecting attacks in several available environments. A boundlessness of methods for misuse detection as well as anomaly detection has been applied, most popular of the all is using machine learning techniques. In this work a survey of various research efforts spared towards the development of intrusion detection systems based on machine learning techniques in given. The surveyed works are presented in easy to understand tabular forms and for each work; technique employed, dataset used and the parameters evaluated are mentioned. Current achievements and limitations in developing intrusion detection system by machine learning and future directions for research are also given.