Cloud Security: LKM and Optimal Fuzzy System for Intrusion Detection in Cloud Environment

被引:17
|
作者
Shyla, S. Immaculate [1 ,2 ]
Sujatha, S. S. [2 ,3 ]
机构
[1] ST Hindu Coll, Dept Comp Sci, Nagercoil, India
[2] Manonmaniam Sundaranar Univ, Tirunelveli, India
[3] ST Hindu Coll, Dept Comp Sci & Applicat, Nagercoil, India
关键词
Intrusion detection system; cloud computing; cloud security; greywolf optimization; leader-based k-means clustering; fuzzy logic system; KDD Cup 99;
D O I
10.1515/jisys-2018-0479
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In cloud security, intrusion detection system (IDS) is one of the challenging research areas. In a cloud environment, security incidents such as denial of service, scanning, malware code injection, virus, worm, and password cracking are getting usual. These attacks surely affect the company and may develop a financial loss if not distinguished in time. Therefore, securing the cloud from these types of attack is very much needed. To discover the problem, this paper suggests a novel IDS established on a combination of a leader-based k-means clustering (LKM), optimal fuzzy logic system. Here, at first, the input dataset is grouped into clusters with the use of LKM. Then, cluster data are afforded to the fuzzy logic system (FLS). Here, normal and abnormal data are inquired by the FLS, while FLS training is done by the grey wolf optimization algorithm through maximizing the rules. The clouds simulator and NSL-Knowledge Discovery and DataBase (KDD) Cup 99 dataset are applied to inquire about the suggested method. Precision, recall, and F-measure are conceived as evaluation criteria. The obtained results have denoted the superiority of the suggested method in comparison with other methods.
引用
收藏
页码:1626 / 1642
页数:17
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