AUTOMATED SYBER ATTACK RESPONSE SYSTEMS BASED ON ARTIFICIAL INTELLIGENCE
Keywords:
artificial intelligence; cybersecurity; automated response systems; machine learning; deep learning; cyberattack detection; real-time securityAbstract
This paper investigates the theoretical foundations, operational mechanisms, and practical effectiveness of automated cyberattack response systems based on artificial intelligence. With the increasing speed, scale, and complexity of cyber threats in modern cyberspace, traditional security mechanisms demonstrate significant limitations in detecting and responding to sophisticated and unknown attacks. Consequently, there is a growing demand for intelligent and automated response systems capable of operating in real time without direct human intervention.The study explores automated response approaches employing machine learning and deep learning techniques for threat detection, classification, and mitigation. An intelligent system architecture consisting of data collection, threat analysis, and automated decision-making modules is analyzed. Experimental evaluation demonstrates that AI-based response mechanisms significantly reduce detection and response time, improve attack identification accuracy, and minimize false positive rates compared to conventional security solutions.
The results indicate that artificial intelligence–driven automated response systems enhance cybersecurity resilience by enabling rapid adaptation to emerging and zero-day attacks while reducing dependence on the human factor. These findings confirm the effectiveness of AI-based approaches in strengthening modern cybersecurity infrastructures and ensuring proactive threat mitigation.
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