Results 31 to 40 of about 422 (171)

A Multi-Layer Hardware Trojan Protection Framework for IoT Chips

open access: yesIEEE Access, 2019
Since integrated circuits are performed by several untrusted manufacturers, malicious circuits (hardware Trojans) can be implanted in any stage of the Internet-of-Things (IoT) devices.
Chen Dong   +4 more
doaj   +1 more source

Hardware Trojan Dataset of RISC-V and Web3 Generated with ChatGPT-4

open access: yesData
Although hardware trojans impose a relevant threat to the hardware security of RISC-V and Web3 applications, existing datasets have a limited set of examples, as the most famous hardware trojan dataset TrustHub has 106 different trojans.
Victor Takashi Hayashi   +1 more
doaj   +1 more source

Illegal Trojan design and detection in asynchronous NULL Convention Logic and Sleep Convention Logic circuits

open access: yesIET Computers & Digital Techniques, 2022
With Cyber warfare, detection of hardware Trojans, malicious digital circuit components that can leak data and degrade performance, is an urgent issue. Quasi‐Delay Insensitive asynchronous digital circuits, such as NULL Convention Logic (NCL) and Sleep ...
Kushal K. Ponugoti   +3 more
doaj   +1 more source

ENTT/ENTTR: A Family of Improved Emerging NVM-Based Trojan Triggers and Resets

open access: yesFrontiers in Nanotechnology, 2022
Hardware Trojans in Integrated Circuits (ICs), that are inserted as hostile modifications in the design phase and/or the fabrication phase, are a security threat since the semiconductor manufacturing process is increasingly becoming globalized.
Karthikeyan Nagarajan   +2 more
doaj   +1 more source

Hardware Trojans detection through automatic properties extraction and formal verification

open access: yesXibei Gongye Daxue Xuebao
To address the difficulties in obtaining golden reference models and the low accuracy when conducting pre-silicon hardware Trojans detection on a large-scale integrated circuit design, a formal detection method to quickly and accurately locate hardware ...
DAI Xiaojie   +3 more
doaj   +1 more source

A Deep Learning Approach in Optical Inspection to Detect Hidden Hardware Trojans and Secure Cybersecurity in Electronics Manufacturing Supply Chains

open access: yesFrontiers in Mechanical Engineering, 2021
Deep learning methods have been extensively studied and have been proven to be very useful in multiple fields of technology. This paper presents a deep learning approach to optically detect hidden hardware trojans in the manufacturing and assembly phase ...
Ameya Kulkarni, Chengying Xu
doaj   +1 more source

From Flybys to Sample Return: A Review of Space Probes and Robotic Sampling Technologies for Small Bodies

open access: yesJournal of Field Robotics, EarlyView.
ABSTRACT As a crucial puzzle piece of deep space exploration, exploring small bodies can provide significant scientific insights and valuable mineral resources. Unlike missions to the Moon and Mars, small‐body missions pose distinct technical challenges, including communication delays, weak gravity, and uncertain environments. This paper reviews a full
Xin Zhang   +3 more
wiley   +1 more source

Hardware Trojan Detection and Mitigation in NoC using Key authentication and Obfuscation Techniques

open access: yesEmitter: International Journal of Engineering Technology, 2022
Today's Multiprocessor System-on-Chip (MPSoC) contains many cores and integrated circuits. Due to the current requirements of communication, we make use of Network-on-Chip (NoC) to obtain high throughput and low latency.
Thejaswini P   +5 more
doaj   +1 more source

Hardware Trojan Identification and Detection

open access: yesInternational Journal on Cryptography and Information Security, 2017
ABSTRACT The majority of techniques developed to detect hardware trojans are based on specific attributes. Further, the ad hoc approaches employed to design methods for trojan detection are largely ineffective. Hardware trojans have a number of attributes which can be used to systematically develop detection techniques.
Moein, Samer   +3 more
openaire   +1 more source

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma   +4 more
wiley   +1 more source

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