TY  - JOUR
AU  - A Manikandan
AU  - Vanaparthi Kiranmai
PY  - 2025
DA  - 2025/06/27
TI  - A Study on AI-Powered Threat Intelligence Systems for Proactive Cyber Defence
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 5
AB  - This study evaluates the comparative performance of traditional versus AI-powered threat intelligence
systems in the content of proactive cyber defence. Traditional threat intelligence systems, characterized
by manual processes and reliance on signature-based detection, exhibit limitations in terms of detection
rate, response time, and overall accuracy. In contrast, AI-powered systems leverage advanced
technologies such as machine learning and deep learning to significantly enhance threat detection
and response capabilities. Our experimental results reveal that AI-powered systems achieve a higher
detection rate (92.3%) compared to traditional systems (78.5%), coupled with a lower false positive
rate (8.7% versus 15.2%) and faster average response time (15.2 seconds versus 45.0 seconds). The
AI systems also demonstrate superior accuracy (94.5%) and are capable of detecting a greater volume
of threats (320 per day) while automating a higher percentage of responses (75.0%). These findings
underscore the advantages of integrating AI into threat intelligence systems to improve the efficiency
and effectiveness of cybersecurity measures.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1151
DO  - 10.33425/3066-1226.1151