Optimized Machine Learning-Based ANN Framework for Secure Network Intrusion Detection

The rapid expansion of internet-based services, cloud computing, and interconnected digital infrastructures has significantly increased the vulnerability of modern networks to cyberattacks. Conventional intrusion detection systems (IDS) primarily rely on signature-based detection methods, which often fail to identify emerging or previously unseen attack patterns. To address these limitations, this study proposes an intelligent Artificial Neural Network (ANN)-based network intrusion detection system designed to enhance cybersecurity monitoring and improve detection accuracy. The proposed framework utilizes the pattern learning capability of neural networks to analyze network traffic and classify activities as normal or malicious. The system incorporates several stages including dataset acquisition, data preprocessing, feature encoding, normalization, and feature selection to improve data quality and reduce redundancy. A multilayer feedforward neural network architecture is implemented and trained using benchmark intrusion detection datasets containing multiple attack categories such as Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R) attacks. Experimental evaluation demonstrates that the proposed ANN model achieves 97.6% detection accuracy, with high precision and recall while maintaining a low false positive rate. Comparative analysis further shows that the ANN-based approach outperforms traditional machine learning algorithms such as Decision Trees, Support Vector Machines, and Random Forest classifiers. The results highlight the effectiveness of neural network-based approaches for detecting complex intrusion patterns and improving real-time network security in modern computing environments including cloud systems and Internet of Things (IoT) networks.