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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | APPLYING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING MODELS TO ENHANCE RISK ANALYSIS AND THREAT DETECTION: A COMPREHENSIVE REVIEW |
| Authors Name: | Ilakiya Ulaganathan |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206060
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| Published Paper Id: | IJRTI2504326 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | https://doi.org/10.56975/ijrti.v10i4.206060 |
| Abstract: | Cyber treasures today sit on hi-tech sites; they get attacked by sophisticated methods, the incidence and complexity of attacks are on the rise: hence, traditional rule-based security systems are unable to prevent timely and accurate risk analysis and threat detection. With AI and ML as emerging technologies in cybersecurity, real-time monitoring, predictive analytics, anomaly detection, automatic response-making, and more are achievable. This review gives a thorough overview of AI and ML techniques that are applied to various domains in cybersecurity, including supervised and unsupervised learning, deep learning, reinforcement learning, and hybrid models. It studies their systems in methodologies of intrusion detection, malware classification, fraud prevention, and vulnerability assessment. The review further discusses the most used datasets, performance metrics, and limitations currently making the wide adoption slow: data quality, adversarial threat, model interpretability, and ethics. Future directions in research such as explainable AI, federated learning, and autonomous threat response systems are also discussed. This review may serve as a stepping stone for researchers and practitioners studying or utilizing AI/ML for cyber risk management in a broader sense. |
| Keywords: | Artificial Intelligence, Risk Analysis, Threat Detection, Cybersecurity, Intrusion Detection Systems, Predictive Analytics, Adversarial Machine Learning, Explainable AI |
| Cite Article: | "APPLYING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING MODELS TO ENHANCE RISK ANALYSIS AND THREAT DETECTION: A COMPREHENSIVE REVIEW ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.10, Issue 4, page no.d242-d252, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504326.pdf |
| Downloads: | 000205498 |
| 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 |
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Published Paper ID: IJRTI2504326
Registration ID:206060
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v10i4.206060
Page No: d242-d252 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504326 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504326 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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