Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
With the rapid evolution of cyber threats, traditional rule-based security systems are increasingly inadequate for detecting sophisticated and zero-day attacks. Artificial Intelligence (AI), particularly machine learning, has emerged as a powerful tool for enhancing cyberattack detection. This study presents a comparative analysis of AI-driven cyberattack detection systems and traditional methods, such as signature-based and rule-based approaches. We evaluate their effectiveness, scalability, adaptability, and limitations using standard performance metrics. The results indicate that AI-driven approaches significantly outperform traditional systems in detecting novel and complex attacks, although challenges such as higher false positives and lack of interpretability remain.
"AI-Driven Cyberattack Detection vs Traditional Methods: A Comparative Study", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b182-b186, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604163.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator