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Abstract— With the increasing frequency and sophistication of cyberattacks, traditional cybersecurity threat detection methods are proving insufficient in addressing novel and evolving threats. Artificial Intelligence (AI), with its advanced capabilities in data processing and pattern recognition, has emerged as a vital component in enhancing cybersecurity defenses. This paper explores the application of AI in cybersecurity threat detection, beginning with a review of current developments in AI-based security technologies. It then focuses on the use of core techniques such as machine learning and deep learning for threat identification, while also examining the benefits of ensemble learning and multimodal data fusion. Finally, the paper discusses the ongoing challenges faced in this domain and outlines potential future directions. The goal is to contribute insights toward improving the accuracy, adaptability, and efficiency of cybersecurity threat detection systems.
Keywords:
data processing, pattern recognition, Security Threads
Cite Article:
"Artificial Intelligence in Cybersecurity Threat Detection: Methods, Challenges, and Future Directions", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.a72-a73, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508013.pdf
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000851
ISSN:
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