Authors : Mohammed Hasan Ali, Mohamad Fadli Zolkipli, Ngahzaifa Binti Ab. Ghani 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) have 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 focus on design intrusion detection system based hybrid Extreme Learning Machine (ELM) and Genetic Algorithm (GA). ELM is randomly generated the parameters because that this research proposes use GA to provide the ELM parameters to find the best classifier that work as IDS. This model will test in Knowledge Discovery and Data Mining Contest 1999 (KDD Cup 99) and Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD) data set. Evaluate the performance of the proposed hybrid by using standard evaluate matrices.
Mohammed Hasan Ali, Mohamad Fadli Zolkipli, Ngahzaifa Binti Ab. Ghani and Mohammed Abdulameer Mohammed, 2016. Intrusion Detection System Framework Based on Machine Learning for Cloud Computing. Journal of Engineering and Applied Sciences, 11: 3279-3284.