Real-Time Detection of Cyber Threats in Encrypted Cloud Traffic Using CNN-LSTM Networks

A hybrid CNN-LSTM model for real-time intrusion detection in encrypted cloud traffic, achieving 99.64% accuracy while preserving user privacy using flow-based metadata.

Authors

Publication Details

Journal
International Journal on Science and Technology
Published
2025-05-29
DOI
https://doi.org/10.71097/ijsat.v16.i2.4962
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Real-Time Detection of Cyber Threats in Encrypted Cloud Traffic Using CNN-LSTM Networks

B. Teja, N. Saranya, K Harshith, G Phanidra -
International Journal on Science and Technology
Network Security and Intrusion Detection
article

Real-Time Detection of Cyber Threats in Encrypted Cloud Traffic Using CNN-LSTM Networks

B. Teja, N. Saranya, K Harshith, G Phanidra -
article en

Abstract

A hybrid CNN-LSTM model for real-time intrusion detection in encrypted cloud traffic, achieving 99.64% accuracy while preserving user privacy using flow-based metadata.

International Journal on Science and TechnologyVol. 16(2)
Openalex Percentile: Top 88%
Network Security and Intrusion Detection
DOIOpen Access PDFOpenAlex
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Real-Time Detection of Cyber Threats in Encrypted Cloud Traffic Using CNN-LSTM Networks — B. Teja, N. Saranya, et al. · International Journal on Science and Technology (2025) | TGRS Research Map | TGRS