Results 31 to 40 of about 589,125 (203)

Defeating Untrustworthy Testing Parties: A Novel Hybrid Clustering Ensemble Based Golden Models-Free Hardware Trojan Detection Method

IEEE Access, 2019
Due to the globalization of the design and fabrication process of integrated circuits (ICs), ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works assume that the testing stage is trustworthy.
Mingfu Xue, Rongzhen Bian, Weiqiang Liu, Jian Wang   +3 more
doaj   +1 more source

Trojan Attack on the Initialization of Pseudo-Random Bit Generators Using Synchronization of Chaotic Input Sources

IEEE Access, 2021
This paper deals with a safety problem of entropy sources in deterministic random bit generators. An initialization input attack on a set of two pseudo-random generators using analog-digital hardware Trojan is presented.
Michal Melosik, Wieslaw Marszalek
doaj   +1 more source

Detecting Unknown Hardware Trojans in Register Transfer Level Leveraging Verilog Conditional Branching Features

IEEE Access, 2023
Hardware Trojans have concealed modifications to integrated circuits (ICs) that can alter their functions, performance, or security properties. Existing Trojan detection methods are designed primarily to detect Trojans at the gate-level IC abstraction ...
Sarwono Sutikno, Septafiansyah Dwi Putra, Fajar Wijitrisnanto, Muhamad Erza Aminanto   +3 more
doaj   +1 more source

Hardware Trojan Detection Using Machine Learning [PDF]

, 2021
The cyber-physical system’s security depends on the software and underlying hardware. In today’s times, securing hardware is difficult because of the globalization of the Integrated circuit’s manufacturing process.
., Daisy
core  

Magnetic Tunnel Junction Injection as a Hardware Trojan in an Inverter Chain

, 2023
The use of embedded hardware devices and Internet of Things devices is increasing rapidly in the current decade. Due to increased demand, many IC design and manufacturing companies rely on third-party vendors.
Massoud, Yehia Mahmoud, Divyanshu, Divyanshu, Kumar, Rajat, Amara, Selma, Khan, Danial   +4 more
core   +1 more source

A Survey on the Design, Detection, and Prevention of Pre-Silicon Hardware Trojans

IEEE Access
The complexity of the semiconductor design lifecycle and globalized manufacturing process creates concern over the threat of deliberate malicious alterations, or hardware Trojans, being inserted into microelectronic designs.
Jonathan Cruz, Jason Hamlet
doaj   +1 more source

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Sensors, 2020
Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software ...
Anju P. Johnson, Hussain Al-Aqrabi, Richard Hill   +2 more
doaj   +1 more source

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

IET Computers & Digital Techniques, 2022
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.
Ying Zhang, Sen Li, Xin Chen, Jiaqi Yao, Zhiming Mao, Jizhong Yang, Yifeng Hua   +6 more
doaj   +1 more source

Gate-Level Hardware Trojan Detection Method for Graph Neural Networks Based on Controllability Metrics [PDF]

Jisuanji gongcheng
With the continuous increase in globalization, third-party Intellectual Property (IP) core applications have become increasingly widespread. The gradual maturity of hardware Trojan attack technology enables the implantation of hardware Trojan in the chip
Yang ZHANG, Chang LIU, Shaoqing LI
doaj   +1 more source

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

CAAI 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, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source