Improved cuckoo search load distribution (ICS‐LD) and attack detection in cloud environment

• Published in: Concurrency and computation Vol. 33; no. 3
• Main Authors: Peter Soosai Anandaraj, A., Indumathi, G.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 10.02.2021
• Subjects: Algorithms; Artificial neural networks; attack detection and prevention; Bayesian analysis; Cloud computing; distributed denial of service (DDoS); fuzzy extreme learning machine (FELM); improved cuckoo search (ICS) algorithm; load distribution; Load distribution (forces); Machine learning; Optimization; Spoofing; Statistical inference; Stress concentration; TCP (protocol); Watermarking
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.5226

Summary For the purpose of obtaining an answer to a spoofing IP issue and related problems in the cloud computing (CC) environment, a novel path identifier is recommended, in which a new packet approach with a new identifier (ID) is produced to every user or client system. For the purpose of identification of the spoofing attack, any watermark image pattern is introduced. Following this, the hypervisor is employed to ascertain plausible trust relationships among the VMs by considering personal sources that can be trusted along with utilizing empirical Bayesian inference (EBI) for the purpose of combining the VMs. With a restricted budget of resources for the maximized detection of attacks, an optimal detection load distribution strategy by improved cuckoo search (ICS) over VMs can be used, which thus provides the intended solution to the hypervisor. The ICS algorithm directs a hypervisor so as to assert among the VMs the optimal load distribution, taking into consideration real time, which causes maximization of the DDoS attacks and detection of transmission control protocol (TCP) flood attacks. Lastly, a fuzzy extreme learning machine (FELM) classifier is proposed in order to detect the attacks.