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Improved cuckoo search load distribution (ICS‐LD) and attack detection in cloud environment
Author(s) -
Peter Soosai Anandaraj A.,
Indumathi G.
Publication year - 2019
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5226
Subject(s) - computer science , cloud computing , hypervisor , virtual machine , spoofing attack , identifier , cuckoo search , identification (biology) , computer network , real time computing , data mining , computer security , machine learning , operating system , botany , particle swarm optimization , biology , virtualization
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.