首页|Recent Findings in Machine Learning Described by Researchers from Shanghai University (Low-overhead Xor Multi-puf Against Machine Learning Attacks)
Recent Findings in Machine Learning Described by Researchers from Shanghai University (Low-overhead Xor Multi-puf Against Machine Learning Attacks)
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NETL
NSTL
Elsevier
Investigators discuss new findings in Machine Learning. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, "Physical unclonable functions (PUF) have been a hot research topic in hardware security in recent years. Still, most of the research has not focused on the design of balancing its resistance to machine learning attacks with resources." The news correspondents obtained a quote from the research from Shanghai University, "Therefore, in this paper, a new low-overhead XOR Multi-PUF design is proposed, which achieves the response of outputting different modes of PUFs with configurable bits to change the number of delays and inverters inside the circuit, completing the PUF design with less hardware overhead. It also introduces an input challenge obfuscation mechanism to improve its resistance to machine learning attacks. In this paper, the proposed PUF circuit design is verified on FPGA boards and good performance metrics are obtained." According to the news reporters, the research concluded: "The design has more challenge-response pairs (CRPs) and less hardware overhead than existing PUF low-overhead designs, improving resistance to machine learning attacks."
ShanghaiPeople's Republic of ChinaAsiaCybersecurityCyborgsEmerging TechnologiesMachine LearningShanghai University
NETL
NSTL
Elsevier
