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

Registration ID: IJNRD_211125

Published ID: IJNRD2312361

DOI: Click Here to Get

Dr. B. NARASIMHAN , Dr. M. THENMOZHI , Dr. V. JAIGANESH

Jaquar Search Algorithm, LSTM, Deep Neural Network, Network, Traffic, Classification, Data Science, Data Analytics, Research, Intrusion Detection System

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.

Published Paper   E-Certificate

Dr. B. NARASIMHAN, Dr. M. THENMOZHI, & Dr. V. JAIGANESH (December-2023). 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, 8(12), d547-d555. https://ijnrd.org/papers/IJNRD2312361.pdf

Volume 8 Issue 12, December-2023

Pages : d547-d555

Paper Reg. ID: IJNRD_211125

Published Paper Id: IJNRD2312361

Downloads: 000121990

Research Area: Science & Technology

Country: coimbatore, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2312361.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312361

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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