University research initiative developing novel techniques for detecting network anomalies in encrypted communications while preserving privacy.
Explore Our ResearchOur academic investigation spans multiple disciplines in computer science and network security
Developing deep learning models that can accurately classify types of encrypted network traffic (e.g., video streaming, web browsing etc.) based solely on metadata and flow characteristics.
Creating novel neural network architectures that identify malicious patterns in TLS-encrypted communications without decrypting content, preserving user privacy.
Adapting transformer models from natural language processing to treat network flows as linguistic sequences, enabling semantic understanding of encrypted traffic.
Developing formal proofs that our analysis methods cannot reconstruct plaintext content, ensuring compliance with privacy regulations like GDPR and HIPAA.
Implementing distributed machine learning approaches that enable collaborative threat detection without centralized data collection.
Creating anomaly detection systems that identify previously unknown attack patterns in encrypted channels through behavioral analysis.
Our systematic approach to advancing encrypted traffic analysis
We've compiled a comprehensive dataset of encrypted network traffic from diverse sources, including university networks, public traces, and simulated environments. Each flow is meticulously labeled with ground truth classifications.
Developing novel feature extraction techniques that capture temporal patterns, packet size distributions, and flow characteristics without accessing encrypted content. Our features preserve privacy while maintaining detection efficacy.
Creating specialized neural network architectures including temporal convolutional networks, attention mechanisms, and hybrid models that process encrypted traffic as multivariate time series data.
Implementing formal methods to prove our techniques cannot reconstruct plaintext content. We employ information-theoretic analysis and adversarial testing to validate privacy preservation.
Rigorous testing against state-of-the-art baselines using standard metrics (precision, recall, F1) and novel privacy-preserving evaluation frameworks we've developed.
Common inquiries about our research project
Our academic approach focuses on fundamental advances rather than product development. We prioritize:
Unlike commercial solutions, we're not constrained by proprietary concerns or product timelines.
We utilize several standard academic datasets for encrypted traffic analysis:
We also generate synthetic datasets for specific attack scenarios and maintain rigorous IRB protocols for any data collection involving real users.
We welcome collaboration in several forms:
We're particularly interested in collaborations with researchers in privacy-preserving ML, network security, and NLP fields.
We take several measures to ensure ethical research practices:
Yes, we follow open science principles:
We believe transparency is essential for advancing the field and enabling reproducibility.
Get in touch for collaboration opportunities or more information
OracleTunnel is a research initiative based in the Computer Science Department at IHC.
For academic inquiries, potential collaborations, or dataset requests, please contact:
research@oracletunnel.spaceWe welcome inquiries from fellow researchers, students, and industry partners interested in advancing encrypted traffic analysis.