Firewall Defense and Response Policy towards Resisting Attacks on Network Logs - Taylor, Onate E.; Ezekiel, Promise S. (International Journal of Computing Sciences Research; Vol 8 (2024): Volume 8, 2024)
Background – In an era marked by escalating cyber threats, safeguarding network infrastructure and preserving the integrity of network logs have become paramount concerns for organizations worldwide Objective: This paper proposes a robust Firewall Defense and Response Policy leveraging a state-of-the-art Gradient Boost Classifier to achieve exceptional accuracy in detecting cyber threats. Methods – The proposed methodology combines advanced machine learning techniques with an in-depth analysis of network logs. The model was trained on a comprehensive dataset, downloaded from Kaggle.com, comprising 65,533 instances of diverse attack vectors. This training enables the model to discern subtle patterns indicative of cyber threats. Results – The Gradient Boost Classifier achieved an accuracy of 99.99% in identifying and thwarting malicious intrusion attempts. The Response Policy integrates an adaptive approach, dynamically adjusting countermeasures based on the severity and nature of detected anomalies. Conclusion – Through extensive experimentation and validation, the proposed approach demonstrates superior performance in detecting and mitigating a wide spectrum of attacks, including sophisticated and evasive tactics. This paper contributes a highly effective and resilient framework for bolstering network security, empowering organizations to fortify their defenses against evolving cyber threats and safeguard the integrity of their network logs. Recommendation – Organizations should implement the proposed Firewall Defense and Response Policy across various environments, regularly update the training dataset with new attack vectors, and periodically re-evaluate the model to maintain its effectiveness. Integrating this policy with existing security systems, training personnel, and promoting awareness about cyber threats will optimize its implementation. Continued research into advanced machine learning techniques will further enhance the system's accuracy and resilience.