Cloud‐Based Intrusion Detection With TFSEA : Utilizing Graylevel Radial Component Analysis and Threshold‐Based Kernel Extreme Learning Machine

ABSTRACT The emergence of cloud computing has revolutionized business operations by providing effective scalability and flexibility. Security concerns have intensified due to the vast amount of data processed and stored in the cloud; hence protecting cloud infrastructure from cyber threats is crucial. Intrusion detection system plays a pivotal role in seamless monitoring of network traffic for exhibiting unauthenticated or malicious attempts. Recent advancements in IDS highlight certain issues such as low classification accuracy, high false positive rate, as well as overfitting when processing various network data. The feature extraction uses graylevel radial component analysis (GRCA) to extract salient features, while dimensionality reduction is performed by introducing the radial basis function principal component analysis. In this work, the crossover boosted dynamic cheetah optimization algorithm is employed in the feature selection process, which integrates Cheetah Optimization with dynamic evolutionary strategies to improve the overall search efficiency and tackle local optimal issues. The detection and classification of intrusion are performed by proposing a novel threshold‐based kernel extreme learning machine, which uses different thresholds to enhance generalization capability. Extensive experimental and statistical analysis is carried out, and the results exhibit that the proposed framework achieves a classification accuracy, precision, recall, F 1 score, and security rate of 98.84%, 97.22%, 97%, 97.2%, and 98.85%, respectively, compared to all other existing models. Finally, the classified data is stored in cloud infrastructure that allows third‐party monitoring services to assess and analyze critical intrusions and also provide threat analysis.