(HIADIoT): Hardware Intrinsic Attack Detection in Internet of Things; Leveraging Power Profiling

Mohammed, Hawzhin; Odetola, Tolulope A.; Hasan, Syed Rafay; Stissi, Sari; Garlin, Isaiah; Awwad, Falah

doi:10.1109/mwscas.2019.8885183

Cited by 12 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In some of our previously reported results [45] and [35], we were able to detect the IoT-ED attacker, provided that the attacks were predetermined. In this work, the attacks have been taken to another level.…”

Section: B Resultsmentioning

confidence: 87%

“…the start, duration, and end of the attacks on the HAN network are randomized. This randomness decreases the detection accuracy from 95.5% in [35] to be 67.73% using the PP data-set only. We come up with the message counting (NT data-set) to detect the hardware attacks and preserve privacy.…”

Section: B Resultsmentioning

confidence: 98%

“…An ARQ attack is uniquely attributed to the HT-based ICs. To the best of the authors' knowledge, such attacks have not been addressed in the literature other than a few handful of researchers [35], [37]. Although the traditional IDS and firewall can detect the ARQ and DoS attacks, they cannot differentiate between them.…”

Section: ) Arq Attackmentioning

confidence: 99%

“…Differences in power consumption occur as the IoT-ED performs various operations. It is a metric used for side-channel analysis in which the power consumption of the IoT-EDs has been studied [33]- [35].…”

Section: F Power Profiling (Pp)mentioning

confidence: 99%

See 3 more Smart Citations

Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks

2020

Self Cite

View full text Add to dashboard Cite

Internet of Things (IoT) devices have connected millions of houses around the globe via the internet. In the recent past, threats due to hardware Trojan (HT) in the integrated circuits (IC) have become a serious concern, which affects IoT edge devices (IoT-ED). In this paper, the possibility of the IoT-ED with embedded HT that can cause serious security, privacy, and availability problems to the IoT based Home Area Network (HAN) has been discussed. Conventional network attack detection techniques work at the network protocol layers, whereas IoT-ED with HT can lead to the peculiar manifestation of attack at the physical and/or firmware level. On the other hand, in the IC design, most of the HT-based attack detection techniques require design time intervention, which is expensive for many of the IoT-ED and cannot guarantee 100% immunity. The argument in this paper is that the health of modern IoT-ED requires a final line of defense against possible HT-based attacks that goes undetected during IC design and test. The approach is to utilize power profiling (PP) and network traffic (NT) data without intervening into the IC design to detect malicious activity in HAN. The proposed technique is to effectively identify multiple attacks concurrently and to differentiate between different types of attacks. The IoT-ED behaviors for five different types of random attacks have been studied, including covert channel, DoS, ARQ, power depletion, and impersonation attacks. Data fusion has been leveraged by combining the PP and NT data and is able to detect, without design time intervention, each of the five attacks individually with up to 99% accuracy. Moreover, the proposed technique can also detect all the attacks concurrently with 92% accuracy. To the best of authors' knowledge, this is the first work where multiple HT based attacks are concurrently detected in IoT-ED without requiring any design time intervention. INDEX TERMS Internet of Things, hardware security, home area network, hardware Trojan, machine learning, power profile, ARQ attack, DoS attack.

show abstract

Section: B Resultsmentioning

confidence: 87%

Section: B Resultsmentioning

confidence: 98%

Section: ) Arq Attackmentioning

confidence: 99%

Section: F Power Profiling (Pp)mentioning

confidence: 99%

See 2 more Smart Citations

Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The dynamic detection method applies specific stimulus to the circuit under test and observes the response from simulator or actual circuit, so as to effectively detect HTs in the circuit. Sidechannel signals analysis is a typical dynamic HTs detection method and have been widely researched in [3][4][5][6][7][8][9][10]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Hybrid multi‐level hardware Trojan detection platform for gate‐level netlists based on XGBoost

Zhang

Chen

et al. 2022

IET Computers & Digital Tech

View full text Add to dashboard Cite

Coping with the problem of malicious third-party vendors implanting Hardware Trojan (HT) in the circuit design stage, this paper proposes a hybrid-mode gate-level hardware Trojan detection platform based on the XGBoost algorithm. This detection platform is composed of multi-level HT localization and circuit structure based HT detection. Each wire of the circuit is regarded as a node in multi-level HT localization, and static characteristics of nodes are analysed, combining with dynamic detection to locate HT. The network structure features of the circuit are extracted in modular HT structure detection, aiming to identify HT accurately and rapidly. The hybrid-mode HT detection platform can efficiently meet various detection requirements, such as HT localization or rapid and accurate HT detection. The experiment results on Trust-Hub benchmark show that the multi-level localization can achieve 94.0% location accuracy, and the modular HT structure detection accuracy can achieve 100%. The modular HT structure detection is about four times as fast as the multi-level HT localization on feature extraction. Therefore, multi-level localization and modular HT structure detection can be respectively or cooperatively applied for specific HT detection issues, which proves that the proposed hybrid-mode gate-level HT detection scheme is practical and effective.

show abstract

AI-Based Hardware Security Methods for Internet-of-Things Applications

Dofe

Danesh

2022

Frontiers of Quality Electronic Design (QED)

View full text Add to dashboard Cite

(HIADIoT): Hardware Intrinsic Attack Detection in Internet of Things; Leveraging Power Profiling

Cited by 12 publications

References 14 publications

Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks

Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks

Hybrid multi‐level hardware Trojan detection platform for gate‐level netlists based on XGBoost

AI-Based Hardware Security Methods for Internet-of-Things Applications

Contact Info

Product

Resources

About