Adaptive Threat Detection: Leveraging Machine Learning for Real-Time Cybersecurity
Shaifali Sharma, Pritesh Tripathy, Shubham Kumar Yadav, Suvam Mohapatra, Pravakar Singh · 2024
In the dynamically changing cyber security domain, conventional mechanisms for defense often prove inadequate against advanced threats that adapt themselves to counter de-fenses. This paper presents a new paradigm in building cyber security with the induction of machine learning algorithms into its design for enhanced threat detection and response in real time. In essence, the system keeps learning and, therefore, analyzes network traffic, user behaviors, and system anomalies regularly to identify threats when they are emerging. Our methodology includes supervised and unsupervised learning techniques for known threats and the unveiling of new attack patterns. The proposed system will evolve with new data and be very potent against zero-day attacks and polymorphic malware. Further, feedback in the loop will help the system in refining the models built over time for better accuracy and reducing false positives. It will validate the effectiveness of this adaptive threat detection system by testing it at large in simulated environments, where it will way outperform the traditional methods in the identification and mitigation of a wide range of cyber threats. Results show how machine learning can actually transform cybersecurity to become proactive and dynamic about modern cyber defense challenges.