Providing Confidentiality and Integrity in Ultra Low Power IoT Devices

Valea, Emanuele; Silva, Mathieu Da; Flottes, Marie-Lise; Natale, Giorgio Di; Dupuis, Sophie; Rouzeyre, Bruno

doi:10.1109/dtis.2019.8735090

Cited by 8 publications

(1 citation statement)

References 14 publications

(19 reference statements)

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“…All the intellectual properties that are present inside a system could potentially be stolen by an attacker having physical access to the hardware. For this reason, it is important to provide confidentiality of the memory content [7], as well as of the data that is exchanged with the other integrated circuits in the system [8].…”

Section: B Memory Encryptionmentioning

confidence: 99%

Evaluating the Code Encryption Effects on Memory Fault Resilience

Cantoro

Deligiannis

Reorda

et al. 2020

2020 IEEE Latin-American Test Symposium (LATS)

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In most safety-critical systems, the robustness and the confidentiality of the application code are crucial. Such code is generally stored into Non-Volatile Memories (NVMs) that are prone to faults (e.g., due to radiation effects). Unfortunately, faults affecting the instruction code result very often into Silent Data Corruption (SDC). This condition lets faults remain undetected and it can lead to undesiderable errors that may compromise the system functionality. Thus, it is desirable that the system is able to detect faults affecting the code memory. To overcome this issue, designers often resort to expensive error detection/correction mechanisms. Furthermore, they also adopt memory encryption techniques to prevent unauthorized, hence malicious, access to the code or to protect it from any unauthorized copy. In this paper, we show that the presence of memory encryption alone is able to strongly reduce the probability of SDC, without the need of implementing expensive error detection.We have performed some experiments on the OpenRISC1200 microprocessor in order to evaluate the impact on reliability stemming from different encryption methods.

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Section: B Memory Encryptionmentioning

confidence: 99%

Evaluating the Code Encryption Effects on Memory Fault Resilience

Cantoro

Deligiannis

Reorda

et al. 2020

2020 IEEE Latin-American Test Symposium (LATS)

Self Cite

View full text Add to dashboard Cite

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Securing internet of things using machine and deep learning methods: a survey

Ghaffari,

Jelodari,

pouralish

et al. 2024

Cluster Comput

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The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative effect on integrating technology into people’s daily lives. IoT covers essential areas such as smart cities, smart homes, and health-based industries. However, security and privacy challenges arise with the rapid growth of IoT devices and applications. Vulnerabilities such as node spoofing, unauthorized access to data, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection have emerged as significant concerns. Recently, machine learning (ML) and deep learning (DL) methods have significantly progressed and are robust solutions to address these security issues in IoT devices. This paper comprehensively reviews IoT security research focusing on ML/DL approaches. It also categorizes recent studies on security issues based on ML/DL solutions and highlights their opportunities, advantages, and limitations. These insights provide potential directions for future research challenges.

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