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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM47095

Adaptive Cyber Defense: Leveraging AI for Real Time Threat Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM47095

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem47095

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Dr. Farheen Mohammed

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Abstract

As digital systems become more integral to modern life, the nature of cyber threats has grown increasingly sophisticated, demanding more advanced defense strategies. Artificial Intelligence (AI) has become a pivotal force in modernizing cybersecurity, bringing real-time detection and predictive analysis into the spotlight. This paper investigates the role of AI technologies—including machine learning, deep learning, and natural language processing—in identifying, interpreting, and countering cyber threats. With AI, security frameworks can more effectively recognize unusual activity, uncover previously unknown vulnerabilities, and react swiftly to stealthy, long-term attacks like advanced persistent threats (APTs). The research also explores how AI is being blended with conventional cybersecurity tools, while addressing major challenges such as minimizing false alarms, resisting adversarial manipulation, and maintaining models that evolve with emerging threats. Ethical considerations are also examined, with a focus on ensuring that AI decisions in security contexts remain transparent and accountable. Through an analysis of real-world use cases, this study demonstrates how AI is redefining cyber defense, equipping organizations with smarter, more resilient protection against an ever-expanding array of digital threats.

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Cite This Article

Dr. Farheen Mohammed, "Adaptive Cyber Defense: Leveraging AI for Real Time Threat Detection," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/ijsrem47095

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Publication Date

5/6/2025

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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