UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 12 | Issue 11 | November 2025

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Volume 12 Issue 11
November-2025
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2511105


Registration ID:
571107

Page Number

b39-b48

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Title

ARTIFICIALINTELLIGENCE IN CYBERSECURITY AND THREAT DETECTION: A COMPREHENSIVE REVIEW OF TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS

Abstract

The exponential increase in cyberattacks, ranging from sophisticated ransomware to state-sponsored espionage, has highlighted the limitations of traditional rule-based security mechanisms. Artificial Intelligence (AI) has emerged as a transformative force capable of augmenting cybersecurity defenses by learning complex patterns, detecting anomalies in real time, and autonomously responding to threats. This review presents a comprehensive overview of the integration of AI techniques in cybersecurity and threat detection. It analyzes the evolution of AI-driven defensive systems, compares machine learning (ML) and deep learning (DL) approaches for intrusion and malware detection, and evaluates their performance using standard datasets such as NSL-KDD and CICIDS2017. The study further explores advanced paradigms like reinforcement learning and natural language processing in phishing prevention, alongside hybrid models that combine multiple algorithms for improved robustness. In addition, the article examines major challenges—including data imbalance, adversarial evasion, and explainability—as well as ethical and privacy concerns. Finally, it identifies emerging trends such as federated learning, quantum-enhanced AI, and explainable AI frameworks for autonomous cyber defense. The synthesis aims to provide researchers and practitioners with a consolidated understanding of how AI is reshaping cybersecurity, outlining both its potential and its inherent vulnerabilities.

Key Words

Artificial intelligence, cybersecurity, threat detection, machine learning, deep learning, adversarial attacks, explainable AI

Cite This Article

"ARTIFICIALINTELLIGENCE IN CYBERSECURITY AND THREAT DETECTION: A COMPREHENSIVE REVIEW OF TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.b39-b48, November-2025, Available :http://www.jetir.org/papers/JETIR2511105.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"ARTIFICIALINTELLIGENCE IN CYBERSECURITY AND THREAT DETECTION: A COMPREHENSIVE REVIEW OF TECHNIQUES, CHALLENGES, AND FUTURE DIRECTIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppb39-b48, November-2025, Available at : http://www.jetir.org/papers/JETIR2511105.pdf

Publication Details

Published Paper ID: JETIR2511105
Registration ID: 571107
Published In: Volume 12 | Issue 11 | Year November-2025
DOI (Digital Object Identifier):
Page No: b39-b48
Country: KOLKATA, WEST BENGAL, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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