Security Establishment In Cybersecurity Environment Using PSO Based Optimization
Abstract Cybersecurity based significant data context is considered a challenge in the research community. Machine Learning approaches are considered for dealing with the big data-based security problem. Here, Particle Swarm Optimization (PSO) is used for configuring a massive amount of data. This work formulates a solution for Multi-objective problems to fulfill accuracy, computational and model complexities. A novel Meta-heuristic framework for multi-objective optimization is developed for dealing with lower levels and higher-level heuristics. In the former group, various rules are generated for configuring PSO, and in the latter model, search performance to control the selection process is used for newer configurations of PSO, deal with this multi-objective function. Parento-Approximation (PA) approach is used for strengthening this framework. The proposed optimization approach can be used in cybersecurity problems like anomaly classification. The proposed model is expected to provide better results in contrast to other models.