Analog hardware trojan design and detection in OFDM based wireless cryptographic ICs

Ali, Liakot; Farshad,

doi:10.1371/journal.pone.0254903

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…of legitimate nodes. Moreover, it is also possible for us to utilize hardware Trojan detecting methods [52][53][54][55] and design authentication mechanisms for sensor nodes to prevent possible attacks caused by wireless Trojan nodes connected by malicious users. (2) Error detection and data correction.…”

Section: Discussionmentioning

confidence: 99%

Butterfly: μW Level ULP Sensor Nodes with High Task Throughput

Zhang¹,

Lu²,

Song³

et al. 2022

Sensors

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The rapid development of Internet of Things (IoT) applications calls for light-weight IoT sensor nodes with both low-power consumption and excellent task execution efficiency. However, in the existing system framework, designers must make trade-offs between these two. In this paper, we propose an “edge-to-end integration” design paradigm, Butterfly, which assists sensor nodes to perform sensing tasks more efficiently with lower power consumption through their (high-performance) network infrastructures (i.e., a gateway). On the one hand, to optimize the power consumption, Butterfly offloads the energy-intensive computational tasks from the nodes to the gateway with only microwatt-level power budget, thereby eliminating the power-consuming Microcontroller (MCU) from the node. On the other hand, we address three issues facing the optimization of task execution efficiency. To start with, we buffer the frequently used instructions and data to minimize the volume of data transmitted on the downlink. Furthermore, based on our investigation on typical sensing data structures, we present a novel last-bit transmission and packaging mechanism to reduce the data amount on the uplink. Finally, we design a task prediction mechanism on the gateway to support efficient scheduling of concurrent tasks on multiple MCU-free Butterfly nodes. The experiment results show that Butterfly can speed up the task rate by 4.91 times and reduce the power consumption of each node by 94.3%, compared to the benchmarks. In addition, Butterfly nodes have natural security advantages (e.g., anti-capture) as they offload the control function with all application information up to the gateway.

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Section: Discussionmentioning

confidence: 99%

Butterfly: μW Level ULP Sensor Nodes with High Task Throughput

Zhang¹,

Lu²,

Song³

et al. 2022

Sensors

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show abstract

“…Most ICs are made up of multiple links on a global scale, and any one of these untrustworthy links can be implanted with malicious circuits, which are called hardware Trojans (HT). Both analog and digital circuits are vulnerable to hardware Trojans [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

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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Hardware Trojan Insertion in Finalized Layouts: From Methodology to a Silicon Demonstration

Perez

Pagliarini

2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Analog hardware trojan design and detection in OFDM based wireless cryptographic ICs

Cited by 3 publications

References 32 publications

Butterfly: μW Level ULP Sensor Nodes with High Task Throughput

Butterfly: μW Level ULP Sensor Nodes with High Task Throughput

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

Hardware Trojan Insertion in Finalized Layouts: From Methodology to a Silicon Demonstration

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