The Role of Artificial Intelligence in Predicting Cyber Attack Patterns and Offering Solutions to Mitigate Attacks in ISMS Compliant Environments
Abstract
This research investigates the efficiency of Artificial Intelligence (AI) techniques in forecasting cyberattack trends and contrasts their performance with traditional approaches in environments compliant with ISO/IEC 27001. Using simulation-based evaluations, various AI algorithms including Neural Networks, Random Forests, Support Vector Machines, and Bayesian Networks were tested alongside conventional threat detection methods such as signature-based detection and heuristic analysis. The results demonstrate that AI-driven methods surpass traditional ones in several critical metrics. Neural Networks achieved the highest detection accuracy of 97.0% and delivered the fastest incident response times at 1.2 seconds, outperforming traditional techniques that exhibited lower accuracy and slower response. Additionally, AI-based anomaly detection models like Isolation Forests successfully identified emerging attack patterns with superior detection rates and faster processing times. Bayesian Network models also provided enhanced risk assessments, aligning more closely with ISO/IEC 27001 compliance requirements compared to classical methods. Although AI solutions involve higher upfront costs, they offer improved cost-effectiveness and overall performance over the long term. This study highlights the significant benefits of integrating AI into cybersecurity frameworks, emphasizing its role in advancing threat detection, response efficiency, risk management, and regulatory adherence.
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Copyright (c) 2024 mostafa tamtaji; alireza Ekrami Kivaj, Sayed Gholam Hassan Tabatabaei (Author)
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