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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23355
Article Published : 9033
Total Authors : 23952
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| Paper Title: | AI-Powered Real-Time Intrusion Detection System Using Hybrid Machine Learning Techniques |
| Authors Name: | Saar Patwari , Vraj Purohit , Yogita Sable , Divya Salve |
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IJRTI_211659
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| Published Paper Id: | IJRTI2604174 |
| Published In: | Volume 11 Issue 4, April-2026 |
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| Abstract: | The rapid escalation of cyber threats and the increasing complexity of network infrastructures have exposed the limitations of traditional signature-based Intrusion Detection Systems (IDS), particularly in detecting zero-day attacks and minimizing false positives. This paper presents an AI-powered real-time intrusion detection framework that integrates supervised and unsupervised machine learning techniques to enhance detection accuracy and adaptability. The proposed system employs Random Forest for multi-class classification of known attack patterns and Isolation Forest for anomaly detection of previously unseen threats. The models are trained and evaluated using the NSL-KDD benchmark dataset, achieving detection accuracies of 98.2% and 96.5% respectively, significantly outperforming conventional approaches. To ensure practical applicability, the system incorporates a real-time traffic analysis pipeline using Wireshark, a preprocessing module for feature engineering and normalization, and a graphical user interface for live threat visualization. Additionally, SIEM-compatible log generation enables seamless integration with Security Operations Center (SOC) workflows for efficient monitoring and response. The experimental results demonstrate that the proposed hybrid framework not only improves detection performance but also provides scalability and real-time capabilities required for modern network security environments. This work highlights the potential of combining machine learning with real-time analytics to build intelligent and proactive intrusion detection systems. |
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| Cite Article: | "AI-Powered Real-Time Intrusion Detection System Using Hybrid Machine Learning Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b275-b279, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604174.pdf |
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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: IJRTI2604174
Registration ID:211659
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b275-b279 Country: Jalgaon, Maharashtra, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604174 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604174 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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