# Enhancing Network Intrusion Detection for TLS Traffic Using Deep Learning
> Muttaqien H.

URL kanonis: https://discover.unhas.ac.id/publications/enhancing-network-intrusion-detection-for-tls-traffic-using-deep-learning
Jurnal / Konferensi: Engineering Technology and Applied Science Research
Tahun terbit: 2025
DOI: https://doi.org/10.48084/etasr.13267
ISSN: 22414487
Kuartil SJR: Q2
Citations: 0

## Authors
- Muttaqien H.

## Abstract
The increased utilization of Transport Layer Security (TLS) encryption in contemporary network communication introduces new obstacles for Network Intrusion Detection Systems (NIDS), since encrypted traffic constrains the efficacy of traditional signature-based techniques. This study presents a real-time intrusion detection method for TLS traffic utilizing a combination of Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. CNNs are employed to derive spatial representations of TLS information from Suricata logs, including JA3 fingerprints, cipher suites, and connection statistics, and BiLSTM is utilized to capture bidirectional temporal dependencies of encrypted traffic to identify intricate anomaly patterns. This model was evaluated utilizing an extensive TLS dataset comprising both valid and malicious traffic, including Command-and-Control (C2) connections, malware communication, and data exfiltration. The experimental findings indicate that the CNN–BiLSTM model attained a detection accuracy of 98.7%, a False Positive Rate (FPR) of 1.4%, and an average processing time of 12.9 ms per session, rendering it appropriate for real-time application in corporate network security systems. This methodology enhances the capability of hybrid Deep Learning (DL) models to identify concealed dangers in TLS communication without requiring data decryption.

## Keywords
- Computer science
- Encryption
- Deep learning
- Intrusion detection system
- Artificial intelligence
- Data mining
- Convolutional neural network
- Rendering (computer graphics)
- Network security
- Anomaly detection
- Traffic classification
- Artificial neural network
- Machine learning
- Malware
- Transport Layer Security
- Cipher
- Traffic analysis
- Real-time computing
- Information security
- Computer network
- Data security
- SQL
- Layer (electronics)
- Pattern recognition (psychology)

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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