2 Years Impact Factor:
2.85
5 Years Impact Factor: 1.53
Author: R.Saritha, Vasam Jahnavi, Terala Sudhansh, Sabavath Sai Kiran, Pashapu Siddartha Reddy
Abstract:
This paper explores the optimization of firewall parameters for attack detection using machine learning techniques, focusing on improving network security in dynamic environments. Traditional firewall systems often face limitations in detecting malicious traffic due to static rule sets and high false positive rates, particularly in real-world scenarios with evolving attack patterns. To overcome these challenges, this study applies neural networks with the rectified linear unit activation function (ReLU), which enables precise attack detection and real-time firewall policy adjustments. The proposed 5-5-4 neural network model, tested using real-world datasets, achieved an accuracy of 96.3%, outperforming alternative configurations. The analysis evaluated three scenarios: normal conditions, active attacks, and post-policy adjustment, confirming the effectiveness in enhancing detection and mitigation capabilities. The results highlight the potential of machine learning
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