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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM45943

AI-Powered Cybersecurity: The Future of Threat Detection

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM45943

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM45943

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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Authors

SS

Sujeet Sharma1

SM

Suraj Mishra2

MKJ

Maneesh Kumar Jaiswal3

MVS

Mr. Vivek Singh4

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Abstract

This paper explores the transformative role of Artificial Intelligence (AI) in cybersecurity, focusing on its ability to enhance threat detection, automate responses, and mitigate evolving cyber threats. Traditional cybersecurity measures, reliant on predefined rules and signature-based detection, struggle to combat advanced threats like polymorphic malware and zero-day exploits. AI-powered solutions leverage machine learning, deep learning, and behavioral analytics to proactively identify and neutralize threats in real time. Key advantages include reduced human error, scalability, and predictive threat intelligence. However, challenges such as adversarial AI attacks, data privacy concerns, and ethical biases must be addressed. The study highlights real-world applications, emerging trends like Explainable AI (XAI), and the integration of AI with quantum computing. By analyzing current advancements and future prospects, this research underscores AI’s critical role in shaping the future of cybersecurity.

KEYWORDS

artificial Intelligence Machine Learning cybersecurity Threat Detection Intrusion Detection Systems (IDS) Anomaly Detection Network Security

Cite This Article

Sujeet Sharma1, Suraj Mishra2, Maneesh Kumar Jaiswal3, Mr. Vivek Singh4, "AI-Powered Cybersecurity: The Future of Threat Detection," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/IJSREM45943

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Publication Date

4/25/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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2

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5

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

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