A Novel Scheme for Malicious Nodes Detection in Cloud Markets Based on Fuzzy Logic Technique - Alsarhan, Ayoub; Al-Ghuwairi, Abdel-Rahman; Alshdaifat, Esra'a; Idhaim, Hasan; alkhawaldeh, Omar (International Journal of Interactive Mobile Technologies (iJIM), 2022)
Cloud security vulnerabilities have recently become more prevalent around the world, posing a threat to cloud service providers' (CSPs) ability to respond to client demands. In cloud market, the requests are announced by the client nodes to their CSP. A malicious node can alter a client's request, resulting in the next cloud market collapse, decreased reliability, and data leaking. To identify malicious nodes in the cloud market, a novel fuzzy multiple criterion decision making scheme is suggested. Authentication test, trust level, traffic size, and node activity levels are all taken into consideration simultaneously as the major criteria for identifying malicious nodes. For each node, the CSP uses fuzzy Integral to generate a composite value based on these criteria. The malicious node is then removed from the cloud market using this composite value. The simulation results demonstrated the potential of the proposed method to prevent nodes in the cloud market from running malware or software that can be used to degrade quality of service by exhausting resources in the cloud market.