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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 14 | Issue 2 | Month- February 2026
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Published Paper Details:
Paper Title
Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning
Authors
Sharvanthvadivelan
Keywords
Encrypted Traffic Analysis, Deep Learning, Intrusion Detection, CNN-LSTM, Network Security, Real-Time Threat Detection.
Abstract
With more than 85% of today's network traffic encrypted using TLS/SSL, traditional Intrusion Detection Systems (IDS) fail to inspect encrypted packets and detect hidden threats. Attackers increasingly hide malware, botnets, ransomware, and command-and-control traffic inside encrypted channels. This paper presents a Real-Time AI Threat Detection Framework using a hybrid 1D-CNN + BiLSTM deep learning model to identify malicious encrypted traffic without decrypting it. The model analyzes metadata features such as packet lengths, inter-arrival times, TLS handshake behavior, flow statistics, and directional patterns. Experimental results on CIC-IDS-2018 and ISCX VPN datasets show an accuracy of 98.3%, outperforming classical ML models. The proposed system is scalable, privacy-preserving, and suitable for Zero Trust networks.
IJCRT's Publication Details
Unique Identification Number - IJCRT2602131
Paper ID - 298441
Page Number(s) - b188-b194
Pubished in - Volume 14 | Issue 2 | February 2026
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Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Sharvanthvadivelan, "Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 2, pp.b188-b194, February 2026, Available at :http://www.ijcrt.org/papers/IJCRT2602131.pdf
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Volume 14 | Issue 2
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February 2026
Volume 14 | Issue 2
Last Date :
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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