To study of Intrusion Detection System to prevent from R2L, U2R attacks and Improve False Alarm rate in Cyber Infrastructure Using ML and DM

Computer Science Engineering

With the limitation of traditional technologies like firewalls and thanks to the advancement within the era of technologies the network security are on high risk, which further emerges the necessity of latest technologies and more advanced solutions for cyber security. Many Intrusion detection systems which aren't very capable of identifying and classifying the attacks present within the network like DoS(Denial of Service), Probe, U2R(User to Root) and R2L(Remote to Local).This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets utilized in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and a few recommendations on when to use a given method are provided.

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