An Overview of Hardware Security and Trust: Threats, Countermeasures, and Design Tools

Hu, Wei; Chang, Chip-Hong; Sengupta, Anirban; Bhunia, Swarup; Kastner, Ryan; Li, Hai

doi:10.1109/tcad.2020.3047976

“…Finally, consumer devices are also susceptible to a variety of attacks at the hardware level [82]. These include architectural and system threats (e.g., secure boot attacks, firmware attacks, etc.…”

Section: ) Rq1: Smart Home Device Securitymentioning

confidence: 99%

“…Eavesdropping [64][65] Node capture and replay attacks [66] Sleep deprivation attack [67] DDoS attack [68] Internet pairing default password [70] Configuration and device authentication [71], [85] Legacy authentication mechanism [72] Exposed services [75] Overprivileged configuration Apps [76], [84] Insecure hardware interfaces [73] OTA Updates and upgradeability weakness [75][76] Critical vulnerabilities: side channel, spyware, and backdoor pin code injection [83] Voice spoofing [79] Communal acts like renting, lending, etc. [80] Hardware-level attacks [82] COTS obsolescence risk [81] RQ2: Network Authentication and communication [87], [89] Default password [85][86] Insecure protocols [24], [91] Susceptibility to MITM attack [88][89], [95] Inappropriate use of encryption [94] Protocol attacks [96], [106] Relay attack [97] Sensitive data [100] Prospective attacks [98][99] Social engineering [99] RQ3: Cloud Information disclosure and access control [116][117] Authentication and verification [119], [94] Exploitable services [112]…”

Section: Rq1: Devicementioning

confidence: 99%

Ranking Security of IoT-Based Smart Home Consumer Devices

AlLifah

¹

,

Zualkernan

²

2022

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Smart home consumer devices like home theatres, music players, voice-based assistants, smart lighting, and security cameras have widely adopted the Internet of Things (IoT). These devices pose a significant security risk to consumers because the devices are exposed to mobile applications and cloud-based services with known security vulnerabilities. IoT technologies also introduce additional vulnerabilities specific to smart home architectures. Most current home consumer devices provide little or no information about the level of security they afford. Since most consumers are not tech-savvy, it is currently difficult for a consumer to make an informed decision about which consumer device model (e.g., which smart television model?) has the best security. Hence, consumers need an objective security ranking of each type (e.g., security cameras) of home consumer devices. This paper proposes a novel methodology to systematically build such security rankings for home consumer devices. The proposed methodology can be applied by utilizing data from any security assessment study. The paper discusses previous efforts in applying Analytic Hierarchy Process (AHP) to rank security risks in general. The paper also presents a systematic survey of security vulnerabilities of smart home consumer devices when viewed from an IoT lens. Using the proposed methodology, a case study where an AHP model for ranking commonly used home consumer devices including home theatres, security cameras, smart lighting, smart speakers, video surveillance, smart switches, home automation systems, home security systems, smart routers, wireless doorbell cameras, and home audio systems, was developed. Relative security rankings for each type of consumer device were derived from the AHP model. According to the AHP model, network security was the primary driver of smart home device security with a priority of 0.6893 while application security had the least priority of 0.0591. Critical Vulnerabilities were the most important for device security (priority=0.4397), Man-in-The-Middle attacks for network security (priority=0.2019), exploitable services for cloud security (priority=0.26), and sensitive data for application security (0.7626). The AHP model was internally consistent (Consistency Ratio < 0.1). Sensitivity analysis showed that the AHP model was robust against pairing assumptions.INDEX TERMS AHP, consumer applications, cloud computing, home consumer devices, Internet of Things, network security, smart home.

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“…(CVEs) is just the tip of an iceberg" [2]. Attack scenarios are manifold and, albeit not considered an attack, hardware reverse engineering (RE) has proven its relevance in these [3].…”

Section: Introductionmentioning

confidence: 99%

CRESS: Framework for Vulnerability Assessment of Attack Scenarios in Hardware Reverse Engineering

Ludwig¹,

Hepp²,

Brunner³

et al. 2022

Preprint

0

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Trust and security of microelectronic systems are a major driver for game-changing trends like autonomous driving or the internet of things. These trends are endangered by threats like soft- and hardware attacks or IP tampering -- wherein often hardware reverse engineering (RE) is involved for efficient attack planning. The constant publication of new RE-related scenarios and countermeasures renders a profound rating of these extremely difficult. Researchers and practitioners have no tools or framework which aid a common, consistent classification of these scenarios. In this work, this rating framework is introduced: the common reverse engineering scoring system (CRESS). The framework allows a general classification of published settings and renders them comparable. We introduce three metrics: exploitability, impact, and a timestamp. For these metrics, attributes are defined which allow a granular assessment of RE on the one hand, and attack requirements, consequences, and potential remediation strategies on the other. The system is demonstrated in detail via five case studies and common implications are discussed. We anticipate CRESS to evaluate possible vulnerabilities and to safeguard targets more proactively.

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“….] the database of common vulnerabilities and exposures (CVEs) is just the tip of an iceberg" [2]. Attack scenarios are manifold and, albeit not considered an attack, hardware reverse engineering (RE) has proven its relevance in these [3].…”

Section: Introductionmentioning

confidence: 99%

CRESS: Framework for Vulnerability Assessment of Attack Scenarios in Hardware Reverse Engineering

Ludwig¹,

Hepp²,

Brunner³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Trust and security of microelectronic systems are a major driver for game-changing trends like autonomous driving or the internet of things. These trends are endangered by threats like soft- and hardware attacks or IP tampering -- wherein often hardware reverse engineering (RE) is involved for efficient attack planning. The constant publication of new RE-related scenarios and countermeasures renders a profound rating of these extremely difficult. Researchers and practitioners have no tools or framework which aid a common, consistent classification of these scenarios. In this work, this rating framework is introduced: the common reverse engineering scoring system (CRESS). The framework allows a general classification of published settings and renders them comparable. We introduce three metrics: exploitability, impact, and a timestamp. For these metrics, attributes are defined which allow a granular assessment of RE on the one hand, and attack requirements, consequences, and potential remediation strategies on the other. The system is demonstrated in detail via five case studies and common implications are discussed. We anticipate CRESS to evaluate possible vulnerabilities and to safeguard targets more proactively.

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An Overview of Hardware Security and Trust: Threats, Countermeasures, and Design Tools

Abstract: An overview of hardware security and trust : threats, countermeasures and design tools. IEEE Transactions On Computer-Aided Design of Integrated Circuits and Systems.

Cited by 98 publications

References 264 publications

Ranking Security of IoT-Based Smart Home Consumer Devices

Ranking Security of IoT-Based Smart Home Consumer Devices

CRESS: Framework for Vulnerability Assessment of Attack Scenarios in Hardware Reverse Engineering

CRESS: Framework for Vulnerability Assessment of Attack Scenarios in Hardware Reverse Engineering

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