Deep Bypass

Overview

Providers of large, enterprise-class networks find it hard to track hosts, servers and other vulnerable assets in their networks. Network profiling systems provide valuable insight of the assets on a network and their purpose. A network profile enables providers to better consider how configuration changes will impact networks, and security administrators to identify suspicious activity. However, effective network profiling under real world conditions is increasingly challenging. Network speeds are continually increasing, and use of encryption is growing.

Project Deep Bypass will develop tools for profiling enterprise-class networks. This set of tools ranges from capturing network traffic at high-speed (>40Gbps) without altering information contained in the traffic, to the development of new traffic profiling techniques capable of understand both encrypted and clear traffic using deep learning algorithms on top of untrusted data. Overall this eclectic set of tools will be implemented using newly developed distributed architecture capable of leveraging the high level of concurrency in modern CPUs.

The primary focus of this research is to develop means to address issues in traffic profiling imposed by real-time constraints such as high-speed networking and ubiquitous encryption. The project aims to develop a network profiling method based on deep learning operating at high real-time speed using kernel bypass framework.

Specifically, some of the activities we propose include:

  • Development of deep learning solutions based on temporal, ever evolving, and sparsely labelled data
  • Implementation of a deep-learner for traffic classification of experimental datasets.;
  • Implementation of very fast packet sampling leveraging kernel bypass;
  • Adaptation of deep learner to real-world environments
  • Architecture real time traffic monitoring on concurrent platform.

Budget: $400k from NGTF (2018-present)

People

  • Dr. Guillaume Jourjon, Data61-CSIRO
  • Dr. Kanchana Thilakarathna, University of Sydney
  • Dr. Suranga Seneviratne, University of Sydney
  • A/Prof. Richard Xu, UTS
  • Darren Webb, DST group
  • Adriel Cheng, DST group
  • Ying Li, UTS
  • Yi Huang, UTS
  • K.N. Choi, University of Sydney

News

  • 2 papers accepted at CDNG 2020.
  • Poster accepted at IPSN 2020!
  • Paper accepted at WWW 2019!
  • Paper accepted at IEEE NCA 2018!
  • Paper accepted at ASPLOS 2018!

Dataset

The dataset from the IEEE NCA article can be found DC_dataset.

Publications

  • Jung-Chang Liou, Sajal Jain, Sooraj Randhir Singh, Dhit Taksinwarajan, Suranga Seneviratne, Side-Channel Information Leaks of Z-Wave Smart Home IoT Devices, Under review for Sensys 2020
  • Zhao, J., Masood, R., & Seneviratne, S., A Review of Computer Vision Methods in Network Security, Major Revision – IEEE COMST
  • Kwon Nung Choi, Harini Kolamunna, Akila Uyanwatta, Kanchana Thilakarathna, Suranga Seneviratne, Ralph Holz, Mahbub Hassan, Albert Y. Zomaya, LoRadar: LoRa Sensor Network Monitoring through Passive Packet Sniffing, to appear ACM SIGCOMM CCR
  • Kwon Nung Choi, Achintha Wijesinghe, Chamara Manoj Madarasingha Kattadige ,Kanchana Thilakarathna, Suranga Seneviratne and Guillaume Jourjon, SETA: Scalable Encrypted Traffic Analytics inMulti-Gbps Networks, In Proceedings of IEEE LCN 2020.
  • Thilini Dahanayaka, Guillaume Jourjon, and Suranga Seneviratne, Understanding Traffic Fingerprinting CNNs, In Proceedings of IEEE LCN 2020.
  • Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume Jourjon, A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store, IEEE Transaction on Mobile Computing
  • K.N. Choi, H. Kolamunna, A. Uyanwatta, K. Thilakarathna, S. Seneviratne, R. Holz, M. Hassan, A. Zomaya, Passive Packet Sniffing Tools for Enabling Wireless Situational Awareness, To be presented
    in Cyber Defence Next Generation Technology science Conference (CDNG), 2020.
  • G. Jourjon, A. Wijesinghe, K. Thilakarathna, and S. Seneviratne, Towards Flow Sampling for Deep Content Analysis, To be presented in Cyber Defence Next Generation Technology science Conference (CDNG), 2020
  • K.N. Choi, T. Dahanayaka, D. Kennedy, K. Thilakarathna, S. Seneviratne, S. Kanhere, P. Mohapatra, Poster Abstract: Passive Activity Classification of Smart Homes through Wireless Packet Sniffing, Proceedings of the 19th Information Processing in Sensor Networks (IPSN), 2020
  • Jathushan Rajasegaran, Naveen Karunanayake, Ashanie Gunathillake, Suranga Seneviratne, and Guillaume Jourjon. A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps. Proceedings of the 2019 World Wide Web Conference (WWW ’19), May 13– 17, 2019, San Francisco, CA, USA. ACM, New York, NY, USA, 7 pages
  • Li Ying, Yi Huang, Suranga Seneviratne, Kanchana Thilakarathna, Adriel Cheng, Guillaume Jourjon, Darren Webb and Richard Xu. DeepContent: Unveiling Video Streaming Content from Encrypted WiFi Traffic. Proceedings of IEEE NCA 2018
  • Stephen Mallon, Vincent Gramoli, and Guillaume Jourjon, “DLibOS: Performance and Protection with Network-on-Chip”, in ASPLOS 2018, the 23rd ACM International Conference on Architectural Support for Programming Languages and Operating Systems. March 2018, Williamsburg, VA, USA.