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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: July 2026
Volume 11 | Issue 7
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 122
Article Submitted : 25330
Article Published : 9497
Total Authors : 25208
Total Reviewer : 873
Total Countries : 172
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| Paper Title: | Optimizing Intrusion Detection Systems with Deep Learning & Hybrid Techniques |
| Authors Name: | Aman Naushad Shaikh , Karan Nitin Yewalekar , Samiksha Ravikumar Tantak , Pooja Mohbansi |
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IJRTI_211872
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| Published Paper Id: | IJRTI2606019 |
| Published In: | Volume 11 Issue 6, June-2026 |
| DOI: | |
| Abstract: | Although intrusion detection systems (IDS) are essential to network defense, conventional signature-based methods have poor flexibility to changing cyberthreats, high false alarm rates, and restricted scalability. This study suggests a hybrid ensemble machine learning-based Network Intrusion Detection System (NIDS) that combines an Isolation Forest model for unsupervised anomaly detection of unknown and zero-day threats with a Random Forest classifier for supervised detection of known attack types. Robust final classifications are obtained by combining the results of the two models using a weighted decision procedure. Additionally, the system includes a full-stack web application with a comprehensive analytics dashboard, GeoIP-based visualization, and real-time traffic monitoring. Training and assessment are conducted using the benchmark NSL-KDD and UNSW-NB15 datasets. The system demonstrated its efficacy for practical implementation in enterprise and cloud environments, achieving an accuracy of 99.97% with much lower false positive rates. |
| Keywords: | Intrusion Detection, Random Forest, Isolation Forest, Ensemble Learning, Anomaly Detection, SHAP Explainability, Network Security, Real-Time Monitoring |
| Cite Article: | "Optimizing Intrusion Detection Systems with Deep Learning & Hybrid Techniques", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.a159-a163, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606019.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: IJRTI2606019
Registration ID:211872
Published In: Volume 11 Issue 6, June-2026
DOI (Digital Object Identifier):
Page No: a159-a163 Country: Pune , Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2606019 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2606019 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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