Teknologi Optimization of Honeypots for Real-Time Cyberattack Mitigation Using AI and Log Analysis to Detect New Attacks

Rekayasa Keamanan Siber

Abstract

Cybersecurity has become a major concern due to the increasing threats to digital systems and network infrastructure. In response to these threats, the honeypot technique, which serves to lure attackers, has advanced rapidly with the integration of artificial intelligence (AI) and machine learning. AI-based honeypots offer the ability to detect attacks more accurately and quickly compared to traditional systems. This research aims to explore the effectiveness of AI-based honeypots in detecting and analyzing attacks in real-time, using HoneyShield as the primary platform. The results of the study show that the system successfully detected attack types such as SQL Injection, Brute Force, XSS, and DDoS, with a higher accuracy rate, as well as the ability to read attack logs to identify new attack patterns. This research also identifies the challenges and solutions in implementing AI-based honeypots to counter increasingly sophisticated attacks.