@article{MAKHILLJEAS2016111414106,
    title = {Optimize Machine Learning Based Intrusion Detection for Cloud
Computing: Review Paper},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {11},
    number = {14},
    pages = {3254-3264},
    year = {2016},
    issn = {1816-949x},
    doi = {jeasci.2016.3254.3264},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2016.3254.3264},
    author = {Mohammed Hasan,Mohamad Fadli and},
    keywords = {powerful solutions,IDS,advantages and limitations,algorithms complement,Malaysia},
    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.}
    }