Hyper-heuristic multi-objective online optimization for cyber security in big data

The tremendous growth in data inside the Big data era has created data management challenges as well as data security concerns. These large data cyber security challenges can be successfully addressed with AI computations, with the SVM providing the best results on big data order issues. Master information in picking the kernel work and different boundaries is required to characterize the correct design of the SVM, and this can significantly advance its arrangement outcomes. The fake positive rate, bogus negative rate, and model unpredictability boundaries addressed using the SVM arrangement process is shown to be a multi-objective optimization problem in this study. The hyper-heuristic online particle swarm optimization (HHOPSO) computation with the SVM multi-objective optimization problem, a hyper-heuristic online particle swarm optimization system is produced was presented. The hyper-heuristic system comprises a high-level technique for directing the search process for determining low-level heuristics, and the low-level heuristics build new SVM setup configurations using various PSO standards. The proposed HHOPSO was evaluated on two cyber security datasets i.e., NSL-KDD and ISCX-IDS. The results revealed that the suggested approach is more effective than other calculations in refining the order of big data cyber security issues. The designed HHOPSO can classify cyber-attacks like DoS, Probe, R2L, and U2R with an accuracy of 93.23%.