Abstract
Abstract 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.
Highlights
Nowadays, cloud computing [23] renders data storage and computing services through the Internet
This paper suggests a novel intrusion detection system (IDS) established on a combination of a leader-based k-means clustering (LKM), optimal fuzzy logic system
Normal and abnormal data are inquired by the fuzzy logic system (FLS), while FLS training is done by the grey wolf optimization algorithm through maximizing the rules
Summary
Cloud computing [23] renders data storage and computing services through the Internet. Within a network, using an intrusion detection system (IDS) is one way of handling suspicious activities [22]. All known abnormal behavior is evaluated, and the system is trained to identify it in misuse detection. It works by equating arriving packet with features of known attack behavior. To provide a better detection technique for inquiring about the intrusion from the dataset by resolving the issues that currently exist in the literary works is a major aim of this research. We train the subset applying the optimal fuzzy logic system (OFLS) In this FLS, the optimal rules are selected using grey wolf optimization (GWO), which will be used to reduce the time complexity and increase the detection accuracy.
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