NTP协议(Network Time Protocol)的出现就是为了解决网络内设备系统时钟的同步问题。不幸的是,在通常的互联网环境中,数据传输的延迟不是恒定的,即使相同的路由,从NTP服务器到NTP客户端延迟与从NTP客户端到NTP服务器延迟,即单向延迟(OWD...NTP协议(Network Time Protocol)的出现就是为了解决网络内设备系统时钟的同步问题。不幸的是,在通常的互联网环境中,数据传输的延迟不是恒定的,即使相同的路由,从NTP服务器到NTP客户端延迟与从NTP客户端到NTP服务器延迟,即单向延迟(OWD)不总是相同的。这对时间同步的准确性有很大的影响。目前广泛应用的PTP也同样存在这个问题。因此,为了提高时间的准确性,需要通过测量,提供有关实际传输OWD的时间分布和OWD的不对称性的研究。展开更多
Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential threat.In this paper,we propose HTDet,a novel HT detection method using information entropybase...Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential threat.In this paper,we propose HTDet,a novel HT detection method using information entropybased clustering.To maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the simulation.This implies that the regions with the low transitions will provide much more abundant and more important information for HT detection.The HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under detection.The DBSCAN is an unsupervised learning algorithm,that can improve the applicability of HTDet.In addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan logics.Experiments on circuit benchmarks demonstrate the effectiveness of HTDet.展开更多
文摘NTP协议(Network Time Protocol)的出现就是为了解决网络内设备系统时钟的同步问题。不幸的是,在通常的互联网环境中,数据传输的延迟不是恒定的,即使相同的路由,从NTP服务器到NTP客户端延迟与从NTP客户端到NTP服务器延迟,即单向延迟(OWD)不总是相同的。这对时间同步的准确性有很大的影响。目前广泛应用的PTP也同样存在这个问题。因此,为了提高时间的准确性,需要通过测量,提供有关实际传输OWD的时间分布和OWD的不对称性的研究。
文摘Hardware Trojans(HTs)have drawn increasing attention in both academia and industry because of their significant potential threat.In this paper,we propose HTDet,a novel HT detection method using information entropybased clustering.To maintain high concealment,HTs are usually inserted in the regions with low controllability and low observability,which will result in that Trojan logics have extremely low transitions during the simulation.This implies that the regions with the low transitions will provide much more abundant and more important information for HT detection.The HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise(DBSCAN)to detect all suspicious Trojan logics in the circuit under detection.The DBSCAN is an unsupervised learning algorithm,that can improve the applicability of HTDet.In addition,we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan logics.Experiments on circuit benchmarks demonstrate the effectiveness of HTDet.
