Revolutionizing network management with an AI-driven intrusion detection system

Abstract

The creation of methods and models that can learn and make predictions or judgments based on such learning is artificial intelligence (AI). By combining an AI-driven intrusion detection system (IDS) with the BAT optimization method and a Deep Convolutional Neural Network (DCNN), we provide a revolutionary strategy to the revolutionize network management. Utilizing the advantages of deep learning and BAT optimization, the goal is to increase the efficiency of intrusion detection in the network management. Here, the classification effectiveness of the DCNN is increased by using the BAT optimization strategy. The suggested framework combines a DCNN model, which is excellent in pattern data collection, pre-processing by using normalization, and prediction tasks, with the BAT Optimized with Deep Convolutional Neural Network (BATO-DCNN) method, recognized for its capacity to identify optimum solutions in the challenging search spaces. The suggested method effectively tunes the DCNN using BAT optimization, leading to the better convergence and accuracy. The results of the research show that the recommended methodology performs better than traditional approaches in terms of accuracy, precision, F1-score, and recall measures. The results of this study support the current research in the area of network security and open the door for improved network management systems.