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
Article Identifiers
: Keywords
Jaquar Search Algorithm, LSTM, Deep Neural Network, Network, Traffic, Classification, Data Science, Data Analytics, Research, Intrusion Detection System
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.
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How To Cite (APA)
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
Issue
Volume 8 Issue 12, December-2023
Pages : d547-d555
Other Publication Details
Paper Reg. ID: IJNRD_211125
Published Paper Id: IJNRD2312361
Downloads: 000122052
Research Area: Science & Technology
Author Type: Indian Author
Country: coimbatore, Tamilnadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2312361.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2312361
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