Physical and Software Based Fault Injection Attacks Against TEEs in Mobile Devices: A Systemisation of Knowledge

• URL: http://arxiv.org/abs/2411.14878v1
• Date: Fri, 22 Nov 2024 11:59:44 GMT
• Title: Physical and Software Based Fault Injection Attacks Against TEEs in Mobile Devices: A Systemisation of Knowledge
• Authors: Aaron Joy, Ben Soh, Zhi Zhang, Sri Parameswaran, Darshana Jayasinghe,
• Abstract summary: Trusted Execution Environments (TEEs) are critical components of modern secure computing. They provide isolated zones in processors to safeguard sensitive data and execute secure operations. Despite their importance, TEEs are increasingly vulnerable to fault injection (FI) attacks.
• Score: 5.6064476854380825
• License:
• Abstract: Trusted Execution Environments (TEEs) are critical components of modern secure computing, providing isolated zones in processors to safeguard sensitive data and execute secure operations. Despite their importance, TEEs are increasingly vulnerable to fault injection (FI) attacks, including both physical methods, such as Electromagnetic Fault Injection (EMFI), and software-based techniques. This survey examines these FI methodologies, exploring their ability to disrupt TEE operations and expose vulnerabilities in devices ranging from smartphones and IoT systems to cloud platforms. The study highlights the evolution and effectiveness of non-invasive techniques, such as EMFI, which induce faults through electromagnetic disturbances without physical modifications to hardware, making them harder to detect and mitigate. Real-world case studies illustrate the significant risks posed by these attacks, including unauthorised access, privilege escalation, and data corruption. In addition, the survey identifies gaps in existing TEE security architectures and emphasises the need for enhanced countermeasures, such as dynamic anomaly detection and updated threat models. The findings underline the importance of interdisciplinary collaboration to address these vulnerabilities, involving researchers, manufacturers, and policymakers. This survey provides actionable insights and recommendations to guide the development of more robust TEE architectures in mobile devices, fortify FI resilience, and shape global security standards. By advancing TEE security, this research aims to protect critical digital infrastructure and maintain trust in secure computing systems worldwide.

Related papers

• Federated Learning-Driven Cybersecurity Framework for IoT Networks with Privacy-Preserving and Real-Time Threat Detection Capabilities [0.0]
  Traditional centralized security methods often struggle to balance privacy preservation and real-time threat detection in IoT networks. This study proposes a Federated Learning-Driven Cybersecurity Framework designed specifically for IoT environments. Secure aggregation of locally trained models is achieved using homomorphic encryption, allowing collaborative learning without exposing sensitive information.
  arXiv  Detail & Related papers  (2025-02-14T23:11:51Z)
• CryptoDNA: A Machine Learning Paradigm for DDoS Detection in Healthcare IoT, Inspired by crypto jacking prevention Models [0.0]
  The rapid integration of the Internet of Things (IoT) and Internet of Medical (IoM) devices in the healthcare industry has markedly improved patient care and hospital operations. DDoS attacks present significant dangers, jeopardizing operational stability and patient safety. This study introduces CryptoDNA, an innovative machine learning detection framework influenced by cryptojacking detection methods.
  arXiv  Detail & Related papers  (2025-01-30T18:22:16Z)
• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks. In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• Trustworthy Federated Learning: Privacy, Security, and Beyond [37.495790989584584]
  Federated Learning (FL) addresses concerns by facilitating collaborative model training across distributed data sources without transferring raw data. We conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.
  arXiv  Detail & Related papers  (2024-11-03T14:18:01Z)
• Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security [1.2369895513397127]
  Attacks against the Internet of Things (IoT) are rising as devices, applications, and interactions become more networked and integrated. To efficiently secure IoT devices, real-time detection of intrusion systems is critical. This paper investigates the latest research on machine learning-based intrusion detection strategies for IoT security.
  arXiv  Detail & Related papers  (2024-10-01T19:24:34Z)
• EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Trust-based Approaches Towards Enhancing IoT Security: A Systematic Literature Review [3.0969632359049473]
  This research paper presents a systematic literature review on the Trust-based cybersecurity security approaches for IoT. We highlighted the common trust-based mitigation techniques in existence for dealing with these threats. Several open issues were highlighted, and future research directions presented.
  arXiv  Detail & Related papers  (2023-11-20T12:21:35Z)
• Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future [6.422895251217666]
  This paper reviews forensic and security issues associated with IoT in different fields. Most IoT devices are vulnerable to attacks due to a lack of standardized security measures. To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system.
  arXiv  Detail & Related papers  (2023-09-06T04:41:48Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems [30.638615396429536]
  Motivated by the prevalence of safety-critical artificial intelligence, this work provides a survey of state-of-the-art safety validation techniques for CPS. We present and discuss algorithms in the domains of optimization, path planning, reinforcement learning, and importance sampling. A brief overview of safety-critical applications is given, including autonomous vehicles and aircraft collision avoidance systems.
  arXiv  Detail & Related papers  (2020-05-06T17:31:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.