Enhancing Malicious Domain Detection Using Advanced Machine Learning Techniques

The necessity of comprehensive cybersecurity measures cannot be emphasized in today's quickly expanding digital world. Due to the rising complexity and frequency of cyber attacks, detecting malicious domains has become an urgent problem. Conventional methods often struggle to accurately identify sophisticated attacks, highlighting the need for innovative solutions. This research aims to address the urgent issue of detecting malicious domains by introducing a novel approach that harnesses the strengths of both the HistGradientBoosting-Classifier and Artificial Neural Networks. Through the synergistic combination of diverse algorithms, we propose a solution that has the potential to redefine cybersecurity, enhancing both accuracy and effectiveness. It is crucial to emphasize the significance of this problem within the current cybersecurity landscape, where threat actors continuously adapt their tactics to exploit vulnerabilities. Our study involves the meticulous curation of a dataset, enriched with essential features, showcasing substantial progress in enhancing detection accuracy. Through rigorous comparative analysis, we establish that the integration of various machine learning algorithms and domain name system features has the potential to revolutionize malicious domain detection. The implications of these findings extend well beyond the realm of academic research and hold a profound impact on practical cybersecurity. The incorporation of the Histogram Gradient Boosting Classifier and Artificial Neural Network represents a forward-thinking approach that can proactively address emerging challenges. By integrating these machine learning-driven insights into cyber-security strategies, organizations can significantly bolster their capabilities in safezuardina diaital infrastructures.