On-Chip Trust Evaluation Utilizing TDC-Based Parameter-Adjustable Security Primitive

Ma, Haocheng; He, Jiaji; Liu, Yanjiang; Kuai, Jun; Li, He; Liu, Leibo; Zhao, Yiqiang

doi:10.1109/tcad.2020.3035346

Cited by 12 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, for FPGA research, Ma et al. [ 50 ] proposed an on-chip security framework that can be used to verify whether an HT has tampered with or corrupted the original design. Mohd et al.…”

Section: Related Workmentioning

confidence: 99%

“…Cho et al [49] proposed a bidirectional mechanism to detect hardware Trojans in FPGAs at any stage in order to efficiently detect them. Similarly, for FPGA research, Ma et al [50] proposed an on-chip security framework that can be used to verify whether an HT has tampered with or corrupted the original design. Mohd et al [51] implemented a run-time monitoring design for lightweight ciphers in RCDs on an FPGA platform.…”

Section: Detection and Prevention For Specific Chipsmentioning

confidence: 99%

See 1 more Smart Citation

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

Dong,

Yao,

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Detection and Prevention For Specific Chipsmentioning

confidence: 99%

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

Dong,

Yao,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…ime-to-digital converters (TDCs) are simply high-precision time-interval meters, converting a time interval (TI) into a digital code. (R2, comment 1) They are widely used in industrial and scientific applications, including light detection and ranging (LiDAR) for automatic vehicles and robotics [1]- [3], 3-D imaging [4]- [6], surveying [7], Raman spectroscopy [8], [9], hardware trojan detection [10], temperature sensing [11], [12], random number generation [13], [14], particle physics [15]- [17], positron emission tomography (PET) [18], fluorescence lifetime imaging microscopy (FLIM) [19] and space sciences [20].…”

Section: Introductionmentioning

confidence: 99%

Low-Hardware Consumption, Resolution-Configurable Gray Code Oscillator Time-to-Digital Converters Implemented in 16 nm, 20 nm, and 28 nm FPGAs

Wang

Xie

Chen

et al. 2023

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

“…At the meantime, the security of the IoT has attracted the attention of research. As the embedded core of IoT, it is vulnerable to hardware Trojan [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] and code tamper attacks [14] , [15] , [16] , which could have serious consequences, including functional changes, denial of service (DoS), information leakage, and even system crashes. Thus, the security issue of embedded processor becomes extremely prominent, the recovery mechanism after attack detection is an important scheme to guarantee the security of embedded processor.…”

Section: Introductionmentioning

confidence: 99%