Machine Learning based Enhanced Firewall for Network Security

With increasing global interconnectivity, network security has become inevitable to protect digital valuables and confidential data. Traditional firewalls, as effective as they are, fail to identify and counter contemporary cyber threats, which are sophisticated and adaptive in nature. Traditional firewalls mainly operate as packet filters, intermediaries, or stateful inspectors but fall short against developing threats like polymorphic malware, advanced persistent threats (APTs), and zero-day attacks. This study proposes a Machine Learning-Based Network Security Enhanced Firewall that utilizes machine learning (ML) methods to enhance threat identification and mitigation. Contrary to conventional approaches, firewalls empowered by ML process network traffic constantly, detect anomalies, and respond to new threats in real-time. The suggested system utilizes ensemble-based anomaly detection techniques, i.e., Autoencoders and Isolation Forests, in combination with clustering algorithms like K-Means and DBSCAN. Also, classification algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forests are used to classify normal and abusive network activity. The ML-powered firewall immensely improves the real-time identification of threats as it constantly learns the patterns in the network traffic. Experimental output confirms the high efficacy of the model, indicating 99% accuracy, 99% precision, 99% recall, and a 99% F1-score on both classes. These results unveil the strong functionality of the firewall in identifying and resisting modern-day cyber-attacks as a strong option for current issues of network security.