Eman Maali (Imperial College London), Omar Alrawi (Georgia Institute of Technology), Julie McCann (Imperial College London)

With the proliferation of IoT devices, network device identification is essential for effective network management and security. Many exhibit performance degradation despite the potential of machine learning-based IoT device identification solutions. Degradation arises from the assumption of static IoT environments that do not account for the diversity of real-world IoT networks, as devices operate in various modes and evolve over time. In this paper, we evaluate current IoT device identification solutions using curated datasets and representative features across different settings. We consider key factors that affect real-world device identification, including modes of operation, spatio-temporal variations, and traffic sampling, and organise them into a set of attributes by which we can evaluate current solutions. We then use machine learning explainability techniques to pinpoint the key causes of performance degradation. This evaluation uncovers empirical evidence of what continuously identifies devices, provides valuable insights, and practical recommendations for network operators to improve their IoT device identification in operational deployments.

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KernelSnitch: Side Channel-Attacks on Kernel Data Structures

Lukas Maar (Graz University of Technology), Jonas Juffinger (Graz University of Technology), Thomas Steinbauer (Graz University of Technology), Daniel Gruss (Graz University of Technology), Stefan Mangard (Graz University of Technology)

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Panel on “Security and Privacy Issues in New 5G...

Moderator: Arupjyoti (Arup) Bhuyan, Ph.D. Director, Wireless Security Institute, Idaho National Laboratory Panelists: Ted K. Woodward, Ph.D. Technical Director for FutureG, OUSD (R&E) Phillip Porras, Program Director, Internet Security Research, SRI Donald McBride, Senior Security Researcher, Bell Laboratories, Nokia

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Home Shield IoT Traffic Analyzer: A Comprehensive Analysis of...

Dhananjai Bajpai (Marquette University), Keyang Yu (Marquette University)

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Cellular Metasploit

Dr. Yongdae Kim, Director, KAIST Chair Professor, Electrical Engineering and GSIS, KAIST

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