TY  - JOUR
T1  - Intrusion Detection System Based on Machine Learning in Cloud Computing
AU - Hasan Ali, Mohammed AU - Fadli Zolkipli, Mohamad AU - Musa Jaber, Mustafa AU - Abdulameer Mohammed, Mohammed 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 16
SP  - 4241
EP  - 4245
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.4241.4245
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.4241.4245
KW  - ELM
KW  -SVM
KW  -ANN
KW  -IDS
KW  -FLN
KW  -network
AB  - Detection of attacks in the computers and networks continues to be a relevant and challenging area
of researchers. Intrusion-detection system is an essential technology in network security. Currently, intrusion
detection still faces some challenges like large amounts of data to process, low detection rates and high rates
of false alarms, especially in cloud environment which more vulnerable to attacks. This study includes an
overview of intrusion-detection systems and introduces the reader to some fundamental concepts of IDS
methodology to work in cloud computing also discuss the primary intrusion-detection techniques and propose
a new classifier algorithm fast learning network to work based on the intrusion-detection system.
ER  -