Malicious Activity Detection in Lightweight Wearable and IoT Devices Using Signal Stitching

Karabacak, Fatih; Ogras, Ümit Y.; Ozev, Sule

doi:10.3390/s21103408

Cited by 2 publications

(2 citation statements)

References 45 publications

(49 reference statements)

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“…The side-channel-dependent approach proposed by Karabacak et al. [ 43 ] was also based on self-referencing, and did not rely on plausible samples. Zhu et al.…”

Section: Related Workmentioning

confidence: 99%

“…With a fully automated detection framework, it can reliably detect minor hardware Trojans. The side-channel-dependent approach proposed by Karabacak et al [43] was also based on self-referencing, and did not rely on plausible samples. Zhu et al [44] proposed a Jintide architecture to verify the chip at runtime using a trusted external monitor.…”

Section: Nondestructive Detectionmentioning

confidence: 99%

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A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection

Dong,

Yao,

et al. 2023

Sensors

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The Cyber-Physical System and even the Metaverse will become the second space in which human beings live. While bringing convenience to human beings, it also brings many security threats. These threats may come from software or hardware. There has been a lot of research on managing malware, and there are many mature commercial products, such as antivirus software, firewalls, etc. In stark contrast, the research community on governing malicious hardware is still in its infancy. Chips are the core component of hardware, and hardware Trojans are the primary and complex security issue faced by chips. Detection of hardware Trojans is the first step for dealing with malicious circuits. Due to the limitation of the golden chip and the computational consumption, the existing traditional detection methods are not applicable to very large-scale integration. The performances of traditional machine-learning-based methods depend on the accuracy of the multi-feature representation, and most of the methods may lead to instability because of the difficulty of extracting features manually. In this paper, employing deep learning, a multiscale detection model for automatic feature extraction is proposed. The model is called MHTtext and provides two strategies to balance the accuracy and computational consumption. After selecting a strategy according to the actual situations and requirements, the MHTtext generates the corresponding path sentences from the netlist and employs TextCNN for identification. Further, it can also obtain non-repeated hardware Trojan component information to improve its stability performance. Moreover, a new evaluation metric is established to intuitively measure the model’s effectiveness and balance: the stabilization efficiency index (SEI). In the experimental results for the benchmark netlists, the average accuracy (ACC) in the TextCNN of the global strategy is as high as 99.26%, and one of its stabilization efficiency index values ranks first with a score of 71.21 in all comparison classifiers. The local strategy also achieved an excellent effect, according to the SEI. The results show that the proposed MHTtext model has high stability, flexibility, and accuracy, in general.

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“…The side-channel-dependent approach proposed by Karabacak et al. [ 43 ] was also based on self-referencing, and did not rely on plausible samples. Zhu et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Nondestructive Detectionmentioning

confidence: 99%

A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection

Dong,

Yao,

et al. 2023

Sensors

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Features of the assessment of malicious activity in the Smart City infrastructure based on information granulation and fuzzy granular calculations

Kotenko,

Parashchuk

2024

Vestnik of Astrakhan State Technical University. Series: Management, Computer Science and Informatics

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The object of the research is a new methodological approach to information granulation and fuzzy granular calculations, as a mathematical and methodological tool for improving the reliability of assessing the level of information security of the Smart City infrastructure. The proposed approach is one of the options for the practical application of elements of the theory of fuzzy sets in the tasks of search, identification and current assessment of signs of time-bearing activity. A detailed analysis of the features of this approach has been carried out, determining the expediency and conditions of its application for assessing malicious activity in the infrastructure of a Smart City. The theoretical aspects of the application of information granulation and fuzzy granular computing to the assessment of malicious activity combining various signs for various categories of potential threats to the infrastructure and subjects of a Smart City - the categories “cyberattack”, “malicious virus threat” or “data leakage (loss)” are studied and described. The analysis of the features of the proposed approach is carried out, which allows taking into account the opinions of experts and eliminating the vagueness associated with noise, disorder and lack of formalization of surveillance data collected and pre-processed in the interests of assessing threats and consequences of negative manifestations of malicious activity. A sequence of calculations and analytical expressions for calculating the estimated values of signs of harmful activity for various categories of potential threats to the infrastructure and subjects of a Smart City has been developed and described in detail. The approach assumes the practical possibility of evaluating signs of malicious activity using information granules formed on the basis of a minimum numerical distance between the values of membership functions characterizing vaguely specified data on the presence or absence of observed signs (attributes) of malicious activity, as well as granular summation and determination of the trace function of the granular sum. At the same time, the proposed approach makes it possible to obtain estimates of signs of malicious activity that are adequate to the tasks of monitoring the Smart City security policy and, ultimately, provides increased reliability of proactive threat control and analysis of the possible consequences of a negative manifestation of suspicious activity.

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Malicious Activity Detection in Lightweight Wearable and IoT Devices Using Signal Stitching

Cited by 2 publications

References 45 publications

A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection

A Cost-Driven Method for Deep-Learning-Based Hardware Trojan Detection

Features of the assessment of malicious activity in the Smart City infrastructure based on information granulation and fuzzy granular calculations

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