A Calibratable Detector for Invasive Attacks

Weiner, Michael P.; Wieser, Wolfgang; Lupon, Emili; Sigl, Georg; Manich, Salvador

doi:10.1109/tvlsi.2019.2892408

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…Rahman et al [23] investigated hardware security technologies based on complementary metal-oxidesemiconductors (CMOSs) and analyzed their response coverage for attacks. Weiner et al [24] and Weiner et al [25] proposed a low-area probing detector as a response to micro-probing security threats and compared it with shield or bus encryption methods in terms of area overhead and latency. Lee et al proposed [26] a robust secure shield structure to respond to invasive attacks, such as focused ion beam (FIB) circuit editing.…”

Section: A Hardware Securitymentioning

confidence: 99%

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Design and Implementation of Secure Cryptographic System on Chip for Internet of Things

Lee

Kim

2022

IEEE Access

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Due to the 4th industrial revolution and the strength of the 5 th Generation (5G) era, the Internet of Things (IoT) industry is growing significantly. As a result, the number of IoT devices in various industries, such as smart cars, smart homes, and smart healthcare, and the importance of security for these devices are increasing. This study proposes a design method for a secure cryptographic system on a chip (SecSoC) that can be used in the IoT industry and presents the results of the performance and security evaluations of the implemented chipset. The experimental results demonstrated that the SecSoC is a low-power highperformance cryptographic chip that is safe from external attacks. Compared to conventional smart card integrated circuits, the proposed design includes intrusion detection circuits that can respond to external attacks. At the same time, it supports a physical unclonable function for hiding secret data and cryptographic logic for maintaining integrity and confidentiality. The SecSoC ensured a fast transfer rate up to 110 Mbps and consumed only 95.8 mW when operating at maximum frequency.

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Section: A Hardware Securitymentioning

confidence: 99%

“…Weiner et al [24] and Weiner et al [25] proposed a low-area probing detector as a response to micro-probing security threats Lee et al proposed [26] a robust secure shield structure to respond to invasive attacks Semi-invasive attack Rahman et al [23] using complementary metal-oxide-semiconductors (CMOSs)…”

Section: Attack Type Countermeasures Invasive Attackmentioning

confidence: 99%

Design and Implementation of Secure Cryptographic System on Chip for Internet of Things

Lee

Kim

2022

IEEE Access

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“…There is a disconnect between the theoretical probing leakage models of the masking community and the practical probing [160] countermeasures described in the literature. For example, probing sensors [168] are only applied to buses, while active shields/meshes are inefficiently used to protect the entire IC. Since probing models and tools can identify nets of interest for an attacker to probe, they can be used to distribute these physical countermeasures in a more efficient way.…”

Section: Evaluation Of Probing Sensors and Physical Countermeasuresmentioning

confidence: 99%

Circuit Masking: From Theory to Standardization, A Comprehensive Survey for Hardware Security Researchers and Practitioners

Covic,

Ganji,

Forte

2021

Preprint

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Side-channel attacks extracting sensitive data from implementations have been considered a major threat to the security of cryptographic schemes. This has elevated the need for improved designs by embodying countermeasures, with masking being the most prominent example. To formally verify the security of a masking scheme, numerous attack models have been developed to capture the physical properties of the information leakage as well as the capabilities of the adversary. With regard to these models, extensive research has been performed to realize masking schemes. These research efforts have led to significant progress in the development of security assessment methodologies and further initiated standardization activities. However, since the majority of this work is theoretical, it is challenging for the more practice-oriented hardware security community to fully grasp and contribute to. To bridge the gap, these advancements are reviewed and discussed in this survey, mainly from the perspective of hardware security. In doing so, a clear taxonomy is provided that is helpful for a systematic treatment of the masking-related topics. By giving an extensive overview of the existing methods, this survey (1) provides a research landscape of circuit masking for newcomers to the field, (2) offers guidelines on which attack model and verification tool to choose when designing masking schemes, and (3) identifies interesting new research directions where masking models and assessment tools can be applied. Thus, this survey serves as an essential reference for hardware security practitioners interested in the theory behind masking techniques, the tools useful to verify the security of masked circuits, and their potential applications.

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“…This IIoT system is vulnerable across multiple layers. For instance, physical layers are susceptible through invasive hardware attacks [174] and side-channel attacks. Meanwhile, social attacks (e.g., phishing attacks) arise when people operate a CPPS.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

et al. 2022

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Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid IDSs are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.

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A Calibratable Detector for Invasive Attacks

Cited by 14 publications

References 16 publications

Design and Implementation of Secure Cryptographic System on Chip for Internet of Things

Design and Implementation of Secure Cryptographic System on Chip for Internet of Things

Circuit Masking: From Theory to Standardization, A Comprehensive Survey for Hardware Security Researchers and Practitioners

Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

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