The task of ensuring cyber-security has grown increasingly challenging given the concerning expansion of Computing connection and furthermore, there are a large number of computer-related applications available. It also needs a strong defense mechanism towards various cyber-attacks. Identifying irregularities and dangers in such a computer Security measures (IDS) have been established to aid with information security. Particularly, ML approaches are a subset of artificial intelligence (ai). (AI), a useful data-driven anti - malware system was developed. Two alternative intrusion detection (ID) classification reaches were compared in this study, each with its own set of use cases. Before using the two classifiers for classification, the Particle Swarm Optimization (PSO) approach ware used for reduce dimensionality. The classification meets used to characterize network anomalies were studied in this study. PSO + ANN (Artificial neural network), PSO plus Decision Tree and PSO plus K-Nearest Neighbor are the three classifiers used. The Knowledge discovery in databases 99 datasets was used to corroborate the identification techniques' findings. On the result of the implementation, successful metrics like as the following metrics were used to analyze cyber-security databases for various kinds of cyber-attacks: specific, recall, f1-score, correctness, accuracy, and constancy. The two's respective precision, detection rate (DR), and totally bogus rate were also compared to see which one outperforms the other (FPR). The solution was then contrasted with the IDS that was already in place. In terms of detecting network anomalies, The outcomes show that PSO + ANN outperforms the PSO + KNN and PSO + DT classifier algorithms.