ISSN2320-2882
Impact Factor9.17
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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.

How To Cite

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IJCRT — journal style
"Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.14, Issue 2, page no.b188-b194, February 2026, Available :https://ijcrt.org/papers/IJCRT2602131.pdf
APA — 7th edition
Sharvanthvadivelan. (2025). Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning. International Journal of Creative Research Thoughts, 14(2), b188-b194. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602131
MLA — 9th edition
Sharvanthvadivelan. "Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning." International Journal of Creative Research Thoughts, vol. 14, no. 2, 2025, pp. b188-b194, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602131.
Chicago — 17th, bibliography
Sharvanthvadivelan. "Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning." International Journal of Creative Research Thoughts 14, no. 2 (2025): b188-b194. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602131.
Harvard — author–date
Sharvanthvadivelan (2025) 'Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning', International Journal of Creative Research Thoughts, 14(2), pp. b188-b194. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602131
IEEE — numbered reference
Sharvanthvadivelan, "Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning," IJCRT, vol. 14, no. 2, pp. b188-b194, Dec. 2025.
Vancouver — biomedical
Sharvanthvadivelan. Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning. IJCRT. 2025 Dec;14(2):b188-b194.
AMA — 11th edition
Sharvanthvadivelan. Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning. IJCRT. 2025;14(2):b188-b194. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602131
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2602131, author = {Sharvanthvadivelan}, title = {Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning}, journal = {International Journal of Creative Research Thoughts}, volume = {14}, number = {2}, pages = {b188-b194}, year = {2025}, month = {December}, issn = {2320-2882}, url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602131} }
RIS — EndNote, RefWorks
TY - JOUR AU - Sharvanthvadivelan TI - Real-Time AI Threat Detection in Encrypted Traffic Using Deep Learning T2 - International Journal of Creative Research Thoughts JA - IJCRT VL - 14 IS - 2 PY - 2025 SN - 2320-2882 UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2602131 SP - b188-b194 ER -

Issue

Volume 14 Issue 2, December-2025
Pages : b188-b194

Article Information

Pages
b188-b194
Published
December 2025
Registration ID
IJCRT_298441
Published Paper ID
IJCRT2602131
Research Area
Others area
Author Type
Indian Author
Location
Coimabtore, Tamil Nadu, India
Licence
CC BY 4.0 · Open Access
ISSN & Impact Factor
2320-2882 · IF 9.17
Downloads
254

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International Journal of Creative Research Thoughts Published by IJCRT Publication
2320-2882 ISSN
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Licence

© 2025 — Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
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Declarations

Funding

No external funding was received for this study.

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgements

The authors would like to thank the reviewers and the editorial board of International Journal of Creative Research Thoughts for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.

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