Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices
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Abstract (arXiv)
Side-channel attacks on mobile devices have gained increasing attention since their introduction in 2007. While traditional side-channel attacks, such as power analysis attacks and electromagnetic analysis attacks, required physical presence of the attacker as well as expensive equipment, an (unprivileged) application is all it takes to exploit the leaking information on modern mobile devices. Given the vast amount of sensitive information that are stored on smartphones, the ramifications of side-channel attacks affect both the security and privacy of users and their devices. In this paper, we propose a new categorization system for side-channel attacks, which is necessary as side-channel attacks have evolved significantly since their scientific investigations during the smart card era in the 1990s. Our proposed classification system allows to analyze side-channel attacks systematically, and facilitates the development of novel countermeasures. Besides this new categorization system, the extensive survey of existing attacks and attack strategies provides valuable insights into the evolving field of side-channel attacks, especially when focusing on mobile devices. We conclude by discussing open issues and challenges in this context and outline possible future research directions.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Nov 2016.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 4; 2024: 1; 2025: 2.
10 papers.
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 387
- 2021Leaky Nets: Recovering Embedded Neural Network Models and Inputs through Simple Power and Timing Side-Channels -- Attacks and Defensescited by 11
- 2021Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channelcited by 8
- 2023Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systemscited by 6
- 2021Physical Side-Channel Attacks on Embedded Neural Networks: A Surveycited by 5
- 2025Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Reviewcited by 3
- 2023PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Acceleratorscited by 2
- 2025Exact Soft Analytical Side-Channel Attacks using Tractable Circuitscited by 1
- 2023Side Auth: Synthesizing Virtual Sensors for Authenticationcited by 0
- 2024A Novel Access Control and Privacy-Enhancing Approach for Models in Edge Computingcited by 0
- 2025Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review26 May 2025
- 2025Exact Soft Analytical Side-Channel Attacks using Tractable Circuits23 Jan 2025
- 2024A Novel Access Control and Privacy-Enhancing Approach for Models in Edge Computing6 Nov 2024
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly4 Dec 2023
- 2023Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems20 Nov 2023
- 2023PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators13 Apr 2023
- 2023Side Auth: Synthesizing Virtual Sensors for Authentication27 Jan 2023
- 2021Physical Side-Channel Attacks on Embedded Neural Networks: A Survey21 Oct 2021
- 2021Can one hear the shape of a neural network?: Snooping the GPU via Magnetic Side Channel15 Sep 2021
- 2021Leaky Nets: Recovering Embedded Neural Network Models and Inputs through Simple Power and Timing Side-Channels -- Attacks and Defenses26 Mar 2021