Results 111 to 120 of about 3,548,547 (167)

Gate-level hardware trojan detection method based on graph Transformer

网络与信息安全学报
Graph neural networks can effectively learn and detect maliciously implanted hardware trojans (HT) in gate-level netlists. However, existing methods were usually trained on isomorphic and same-process circuits, which resulted in the generalization ...
Xu Yuhang, Yu Hong, Zheng Rui
doaj  

Three-Basis Loop-Back QKD: A Passive Architecture for Secure and Scalable Quantum Mobile Networks. [PDF]

Entropy (Basel)
Lizama-Pérez LA, Morales-Calvo P.
europepmc   +1 more source

TCF-CBM: a three-stage collaborative poisoned sample filtering method based on composite backdoor mechanism. [PDF]

Sci Rep
Huang W, Gui W, Chen Y, Chen G, Lv Q, Du Z.   +5 more
europepmc   +1 more source

Cyber-physical security of biochips: A perspective. [PDF]

Biomicrofluidics
Baban NS, Bhattacharjee S, Song YA, Chakrabarty K, Karri R.   +4 more
europepmc   +1 more source

An Explainable Attention-Enhanced Deep Learning Model for Memory-Forensic Malware Detection. [PDF]

Sensors (Basel)
Adejoh J, Owoh NP, Ashawa M, Prasad R, Ezeamasiobi C, Singh RR.   +5 more
europepmc   +1 more source

Anomaly-Based Intrusion Detection for IoT Microcontrollers Using Power Side-Channel Fingerprinting. [PDF]

Sensors (Basel)
Shukry SMH, Kargl F, El-Shabrawy T, Abdel-Hamid AT.   +3 more
europepmc   +1 more source

Penetration testing of a quantum key distribution system as a black box. [PDF]

Natl Sci Rev
Huang A, Peng Q, Liu J, Yuan X, Peng C, Yang Z, Wu H, Chen Z, Sun G, Wang F, Makarov V.   +10 more
europepmc   +1 more source

Applicability and Design Considerations of Chaotic and Quantum Entropy Sources for Random Number Generation in IoT Devices. [PDF]

Entropy (Basel)
Marszalek W, Melosik M, Naumowicz M, Głowacki P.   +3 more
europepmc   +1 more source

Self-Defending FPGA Systems: Real-Time Hardware Trojan Detection and Autonomous Recovery Using Embedded Machine Learning

Hardware Trojans (HTs) are malicious modifications inserted into integrated circuits that can compromise system functionality, leak sensitive information, or cause system failures.
Said, Yasir, Ahmed, Salman
core  

MH-1M: A 1.34 Million-Sample Multi-Feature Android Malware Dataset with Rich Metadata. [PDF]

Sci Data
Bragança H, Kreutz D, Rocha V, Assolin J, Feitosa E.   +4 more
europepmc   +1 more source