Cybersecurity Challenges and AI-Powered Mitigation Strategies in CCTV Surveillance Systems

Abstract

CCTV surveillance systems are essential for security and crime prevention but are increasingly vulnerable to cyber threats such as unauthorized access, data breaches, and deep-fake manipulations. Traditional security measures often fail against sophisticated attacks, necessitating advanced protection mechanisms. This study explores cybersecurity challenges in CCTV networks and proposes an intelligent mitigation framework to enhance security. The research analyses existing vulnerabilities, including malware attacks and data interception, highlighting gaps in current security measures. To address these threats, we introduce a machine learning-based intrusion detection system (IDS) for real-time anomaly detection. Additionally, blockchain technology is integrated to secure CCTV footage integrity, preventing unauthorized alterations.The proposed approach is tested on real-world datasets, evaluating detection accuracy, false positives, and resilience against cyberattacks. Our findings contribute to intelligent cybersecurity solutions for CCTV systems, offering law enforcement and organizations a robust framework for securing surveillance infrastructures.