Advanced Intrusion Detection Using Multilayer Perceptron Neural Networks Performance Benchmarking and Validation

Due to their reliance on signature-based techniques, which lead to high false positive rates and limited flexibility to changing threats, traditional intrusion detection systems (IDS) are inadequate and represent a persistent challenge in cybersecurity. The paper proposes an advanced intrusion detection method that uses Multilayer Perceptron Neural Networks (MLPNNs) to overcome these drawbacks. The technique reduces false positives and improves detection accuracy by combining advanced feature extraction techniques with deep learning algorithms. The proposed system outperforms existing systems in terms of performance metrics due to careful data collecting, preprocessing, model architecture design, and validation. The system's effectiveness in precisely recognizing intrusions while lowering false alarms is demonstrated by the results, which show a detection accuracy of 98.5%, a false positive rate of 2.1%, and an AUC of 0.975. The proposed system also shows reduced durations for training and validation in addition to a more thorough evaluation methodology, confirming its dependability and efficacy in enhancing network security against changing cyber threats.