AI-enhanced Honeypots for Zero-Day Exploit Detection and Mitigation
Merlin Balamurugan - · International Journal For Multidisciplinary Research · 2024
Dive into the world of cybersecurity with this cutting-edge article, ‘AI-enhanced Honeypots for Zero-Day Exploit Detection and Mitigation, ‘designed to tackle the elusive zero-day exploits. Traditional honeypots, while valuable, often fall short of these sophisticated attacks. Enter artificial intelligence—machine learning algorithms—that dynamically empower honeypots to adapt to new threats. This innovative framework uses AI to scrutinize network traffic, spot unusual patterns, and predict exploit attempts in real-time, boosting detection accuracy and slashing false positives. AI model was crafted and trained on diverse datasets to catch even the subtlest signs of zero-day exploits. Testing in a controlled setting revealed impressive improvements in response times and detection rates over traditional methods. AI-enhanced honeypots achieved a detection rate of 92% for zero-day exploits, significantly outperforming traditional systems, which had a detection rate of 75%, and the average response time for identifying and mitigating threats was reduced to 2 seconds with AI-enhanced systems, compared to 5 seconds with traditional honeypots. Also, this AI-enhanced system isolates threats, preventing lateral movement within networks for robust mitigation. These findings spotlight AI's transformative potential in cybersecurity, paving the way for proactive defenses in a constantly shifting threat landscape. Future research aims to refine these AI models and explore their use across various industries, bolstering overall cyber resilience.