International Journal on Science and Technology

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Encrypted Traffic Analytics (ETA): Machine Learning Approaches for Intrusion Detection Without Decryption

Author(s) Harshith Kumar Pedarla
Country United States
Abstract The fast adoption of encryption protocols like TLS 1.3 and HTTPS has resulted in a significant proportion of today's internet traffic being encrypted to maintain privacy and data protection since the beginning. But traditional intrusion detection systems (IDS) face tough challenges in this job. Those devices have deep package checking abilities on common protocols, thus posing a huge complexity when the data packets are encrypted (They fail when this data is encrypted). Encrypted Traffic Analytics (ETA) is now gaining wide acceptance as a strong solution to detect bad operations; however, the traffic is still encrypted, so there's no question about data confidentiality. In this paper, we explore machine learning-based approaches to intrusion detection in encrypted network environments. The paper comprises techniques that use statistical features, flow metadata, packet timing, and sequence patterns to identify benign and malicious traffic clearly. It also assesses several supervised and unsupervised models, specifically Random Forest, Support Vector Machines, and Deep Neural Networks, to evaluate the classification performance against known threats and false positive reduction. As a part of this paper, there are also considered trade-offs between detection performance, computational overhead, and privacy concerns. The findings additionally underscore that the reach of machine learning techniques to advanced ETA frameworks, as a result, offers network defence, strength, scalability of network security, and the power to conduct monitoring of what is happening in domains without breaking the qualifications of user privacy.
Keywords Encrypted Traffic Analytics (ETA), Machine Learning-based Intrusion Detection, Network Security, Privacy-Preserving Threat Detection, Encrypted Network Traffic Classification.
Field Engineering
Published In Volume 16, Issue 4, October-December 2025
Published On 2025-11-23
DOI https://doi.org/10.71097/IJSAT.v16.i4.9559
Short DOI https://doi.org/hbb8fv
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