Authors : Mohammed Hasan Ali, Mohamad Fadli Zolkipli and Mohammed Abdulameer Mohammed
Abstract: Security is a rich research area and there are many solutions create to protect the information and make the systems safer, intrusion detection is one of the powerful solutions in security. Current day network Intrusion Detection Systems (IDS) has several flaws such as low detection rates and high rates of false positive alerts and the need for constant human intervention and tuning. This research shows some of the related researchers based on IDS, shows the advantages and limitations of these researches also this research focus on IDS based hybrid as powerful more than the single systems. By use two or more methods and algorithms in one system, to take advantages from each of them as they algorithms complement the other. This research tries to analysis the data set. KDD99 is the most popular data set in the IDS. It’s facing some disadvantages even the new version NSL-KDD still facing some problems.
Mohammed Hasan Ali, Mohamad Fadli Zolkipli and Mohammed Abdulameer Mohammed, 2016. Optimize Machine Learning Based Intrusion Detection for Cloud Computing: Review Paper. Journal of Engineering and Applied Sciences, 11: 3254-3264.