Comparative Analysis of Conventional and AI-Driven Threat Detection Systems in Real-Time Cybersecurity Operations
Abstract
The increasing volume and sophistication of cyber threats have challenged the effectiveness of conventional detection systems in real-time security operations. Traditional approaches based on predefined signatures and static rules often fail to recognize novel and evolving threats, leading to delayed responses and high false positive rates. This study presents a comparative analysis of conventional and AI-driven threat detection systems in order to evaluate their relative effectiveness across critical cybersecurity performance indicators. Using a quantitative comparative design, the study assessed a signature-based intrusion detection system alongside supervised machine learning, deep learning, and hybrid intelligence models. Evaluation was conducted using structured network intrusion datasets and system log data, with performance measured through accuracy, precision, recall, F1-score, response time, scalability, and computational efficiency. The results show that AI-driven systems consistently outperform conventional systems across all major performance criteria. The hybrid AI model achieved the best results, particularly in anomaly detection and response speed. The study concludes that AI-driven cybersecurity offers a more proactive, scalable, and adaptive defense mechanism for real-time digital protection. These findings support the strategic adoption of AI in modern cyber defense frameworks.
How to Cite
Sakshi Sharma, Mamta Sharma, "Comparative Analysis of Conventional and AI-Driven Threat Detection Systems in Real-Time Cybersecurity Operations", Vol. 3, Issue 7, 29-10-2025, pp. 49-61.
