Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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.
How to cite this article:
Mohamad Fadli Zolkipli, Mohammed Hasan Ali and Mohammed Abdulameer Mohammed. Optimize Machine Learning Based Intrusion Detection for Cloud
Computing: Review Paper.
DOI: https://doi.org/10.36478/jeasci.2016.3254.3264
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2016.3254.3264