Enhancing Network Security with AI-Based Threat Hunting Algorithms

Abstract

In an increasingly interconnected digital landscape, network security has become paramount to protect sensitive data from evolving threats. This paper presents a comprehensive exploration of enhancing network security through the implementation of AI-based threat hunting algorithms. We begin by examining the limitations of traditional security measures and the need for adaptive, intelligent solutions. The paper details various AI techniques, including machine learning and deep learning that enable proactive threat detection and response. We propose a novel framework for integrating these algorithms into existing security infrastructures, emphasizing real-time analysis and anomaly detection. Through empirical analysis and case studies, we demonstrate the effectiveness of our approach in identifying and mitigating potential threats before they can cause harm. The findings suggest that AI-based threat hunting significantly enhances network security, reduces response times, and minimizes the impact of cyberattacks. This work contributes to the ongoing discourse on the intersection of artificial intelligence and cybersecurity, providing valuable insights for researchers and practitioners in the field.