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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Jaquar Search Algorithm (JSA) based Feature Selection with Long Short Term Memory (LSTM) Deep Neural Network (JSA – LSTM) for Flow-Based Encrypted Network Traffic Classification towards Intrusion Detection System |
| Authors Name: | Dr. B. NARASIMHAN , Dr. M. THENMOZHI , Dr. V. JAIGANESH |
| Download E-Certificate: | Download |
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IJNRD_211125
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| Published Paper Id: | IJNRD2312361 |
| Published In: | Volume 8 Issue 12, December-2023 |
| DOI: | |
| Abstract: | This paper presents a novel approach to address the complexities of encrypted network traffic analysis: Jaquar Search Algorithm (JSA) based Feature Selection with Long Short-Term Memory (LSTM) Deep Neural Network (JSA – LSTM) for Flow-Based Encrypted Network Traffic Classification towards Intrusion Detection System. This study investigates the combination of Long Short-Term Memory (LSTM) Deep Neural Networks (DNNs) and the Jaquar Search Algorithm (JSA) to improve flow-based encrypted network traffic classification. This study looks into the difficulties that encrypted traffic patterns present for efficient threat detection in network communications. Through combining the adaptive feature selection mechanism of JSA with the sequential data processing capability of LSTM, the study seeks to maximize feature selection and identify temporal trends in encrypted flows. By efficiently differentiating between benign and harmful traffic, the proposed system aims to increase classification accuracy greatly and strengthen cybersecurity measures. In order to help cybersecurity professionals proactively identify and mitigate potential threats in encrypted network traffic, this paper aims to introduce a sophisticated methodology for encrypted traffic analysis through the integration of JSA and LSTM-based DNNs. This will help to advance resilient cybersecurity measures in the face of evolving encryption techniques. Simulation findings improved performance and provided insights. |
| Keywords: | Jaquar Search Algorithm, LSTM, Deep Neural Network, Network, Traffic, Classification, Data Science, Data Analytics, Research, Intrusion Detection System |
| Cite Article: | "Jaquar Search Algorithm (JSA) based Feature Selection with Long Short Term Memory (LSTM) Deep Neural Network (JSA – LSTM) for Flow-Based Encrypted Network Traffic Classification towards Intrusion Detection System", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 12, page no.d547-d555, December-2023, Available :http://www.ijnrd.org/papers/IJNRD2312361.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2312361 Registration ID: 211125 Published In: Volume 8 Issue 12, December-2023 DOI (Digital Object Identifier): Page No: d547-d555 Country: coimbatore, Tamilnadu, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2312361 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2312361 |
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ISSN: 2456-4184
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