Biblio

High detection sensitivity in the presence of process variation is a key challenge for hardware Trojan detection through side channel analysis. In this work, we present an efficient Trojan detection approach in the presence of elevated process variations. The detection sensitivity is sharpened by 1) comparing power levels from neighboring regions within the same chip so that the two measured values exhibit a common trend in terms of process variation, and 2) generating test patterns that toggle each cell multiple times to increase Trojan activation probability. Detection sensitivity is analyzed and its effectiveness demonstrated by means of RPD (relative power difference). We evaluate our approach on ISCAS'89 and ITC'99 benchmarks and the AES-128 circuit for both combinational and sequential type Trojans. High detection sensitivity is demonstrated by analysis on RPD under a variety of process variation levels and experiments for Trojan inserted circuits.

Feedback loss can severely degrade the overall system performance, in addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications including stochastic and time-critical traffic patterns. Stochastic data has a randomness in its nature which make a great challenge to maintain the real-time control whenever the data is lost. In this paper, we propose a data recovery scheme, called the Efficient Temporal and Spatial Data Recovery (ETSDR) scheme for stochastic incomplete feedback of CPS. In this scheme, we identify the temporal model based on the traffic patterns and consider the spatial effect of the nearest neighbor. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithm regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.
 

The technology of vehicle video detecting and tracking has been playing an important role in the ITS (Intelligent Transportation Systems) field during recent years. The occlusion phenomenon among vehicles is one of the most difficult problems related to vehicle tracking. In order to handle occlusion, this paper proposes an effective solution that applied Markov Random Field (MRF) to the traffic images. The contour of the vehicle is firstly detected by using background subtraction, then numbers of blocks with vehicle's texture and motion information are filled inside each vehicle. We extract several kinds of information of each block to process the following tracking. As for each occlusive block two groups of clique functions in MRF model are defined, which represents spatial correlation and motion coherence respectively. By calculating each occlusive block's total energy function, we finally solve the attribution problem of occlusive blocks. The experimental results show that our method can handle occlusion problems effectively and track each vehicle continuously.
 

Wireless channel reciprocity can be successfully exploited as a common source of randomness for the generation of a secret key by two legitimate users willing to achieve confidential communications over a public channel. This paper presents an analytical framework to investigate the theoretical limits of secret-key generation when wireless multi-dimensional Gaussian channels are used as source of randomness. The intrinsic secrecy content of wide-sense stationary wireless channels in frequency, time and spatial domains is derived through asymptotic analysis as the number of observations in a given domain tends to infinity. Some significant case studies are presented where single and multiple antenna eavesdroppers are considered. In the numerical results, the role of signal-to-noise ratio, spatial correlation, frequency and time selectivity is investigated.