2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 2 , PP: 104-118, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Waleed Khalid Alzubaidi 1 *
Doi: https://doi.org/10.54216/FPA.210207
Cloud communication faces numerous disruptive cybersecurity threats. Various issues related to such disruption have been the subject of previous research, but detection attacks in the blade server (BS) in the cloud have not been studied. Therefore, this paper proposes an efficient intrusion detection system (IDS) framework for BS in the cloud. This framework uses Kerberos authentication-based exponential Mestre-Brainstrass curve cryptography, Sechsoftwave and sparsely centric gated recurrent unit (SSGRU). In this framework, cloud users are firstly registered to the network, and then incoming data are encrypted. The BS is then used to balance the incoming loads, and IDS is applied to detect attacks in the BS, with the data being pre-processed firstly and the big data being handled in the IDS. Afterwards, the features are extracted, from which optimal features are selected. Attacked and normal blades are classified by using the SSGRU classifier and then differentiated by generating a Sankey diagram. The attacked blades are then isolated, and the normal blades are used for load balancing on the cloud. Results indicate that this model achieved 99.43% accuracy, thus demonstrating superior performance to other models.
Cloud computing, Blade server , Cybersecurity threat detection , Set-union combiner-based Hadoop MapReduce , Grid-greedy initialization-based shark smell optimization , Lorentzy membership-based fuzzy logic system , Exploratory data analysis
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