The remarkable success of machine learning (ML) in a variety of research domains has inspired academic and industrial communities to explore its potential to address hardware Trojan (HT) attacks. While numerous works have been published over the past decade, few survey papers, to the best of our knowledge, have systematically reviewed the achievements and analyzed the remaining challenges in this area. To fill this gap, this article surveys ML-based approaches against HT attacks available in the literature. In particular, we first provide a classification of all possible HT attacks and then review recent developments from four perspectives, i.e., HT detection, design-for-security (DFS), bus security, and secure architecture. Based on the review, we further discuss the lessons learned in and challenges arising from previous studies. Despite current work focusing more on chip-layer HT problems, it is notable that novel HT threats are constantly emerging and have evolved beyond chips and to the component, device, and even behavior layers, therein compromising the security and trustworthiness of the overall hardware ecosystem. Therefore, we divide the HT threats into four layers and propose a hardware Trojan defense (HTD) reference model from the perspective of the overall hardware ecosystem, therein categorizing the security threats and requirements in each layer to provide a guideline for future research in this direction.

Abstract:In this paper, a simple and efficient fractal-based approach is presented for capturing the effects of initial void ratio on the soil-water characteristic curve (SWCC) in a deformable unsaturated soil. In terms of testing results, the SWCCs (expressed by gravimetric water content) of the unsaturated soils at different initial void ratios were found to be mainly controlled by the air-entry value (ψ a ), while the fractal dimension (D) could be assumed to be constant. As a result, in contrast to the complexity of existing models, a simple and efficient model with only two parameters (i.e., D and ψ a ) was established for predicting the SWCC considering the effects of initial void ratio. The procedure for determining the model parameters with clear physical meaning were then elaborated. The applicability and accuracy of the proposed model were well demonstrated by comparing its predictions with four sets of independent experimental data from the tests conducted in current work, as well as the literature on a wide range of soils, including Wuhan Clay, Hefei and Guangxi expansive soil, Saskatchewan silt, and loess. Good agreements were obtained between the experimental data and the model predictions in all of the cases considered.

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We seek to detect statistically significant temporal or spatial changes in either the underlying process the sensor network is monitoring or in the network operation itself. These changes may point to faults, adversarial threats, misbehavior, or other anomalies that require intervention. To that end, we introduce a new statistical anomaly detection framework that uses Markov models to characterize the "normal" behavior of the sensor network. We develop a series of Markov models, including tree-indexed Markov chains which can model its spatial structure. For each model, an anomaly-free probability law is estimated from past traces. We leverage large deviations techniques to develop optimal anomaly detection rules for each corresponding Markov model, assessing whether its most recent empirical measure is consistent with the anomaly-free probability law. A series of simulation results, some with real sensor data, validate the effectiveness of the proposed anomaly detection algorithms.

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Mn-doped TiO2 loaded on wood-based activated carbon fiber (Mn/TiO2-WACF) was prepared by sol–gel and impregnation method using MnSO4·H2O as manganese source. The structure of Mn/TiO2–WACF was characterized by SEM, XRD, FTIR, N2 adsorption and UV–Vis, and its photocatalytic activity for methylene blue degradation was investigated. Results show that Mn-doped TiO2 were loaded on the surface of wood-based activated carbon fiber with high-development pore structures. The crystallite sizes of Mn-doped TiO2 in composites were smaller than that of the undoped samples. With an increase of Mn doping content, Ti–O bending vibration intensity of Mn/TiO2–WACF increased and then decreased. Moreover, Ti–O–Ti and Ti–O–Mn absorption peaks increased upon doping of Mn. Mn/TiO2–WACF with low specific surface area, and pore volume was improved at 3.5–6.0 nm of mesopore distributions due to the Mn-doped TiO2 load. In addition, the UV–Vis showed that Mn/TiO2–WACF (photodegradation rate of 96%) has higher photocatalytic activity than the undoped samples for methylene blue degradation under visible light irradiation.

The relative hydraulic conductivity is one of the key parameters for unsaturated soils in numerous fields of geotechnical engineering. The quantitative description of its variation law is of significant theoretical and technical values. Parameters in a classical hydraulic conductivity model are generally complex; it is difficult to apply these parameters to predict and estimate the relative hydraulic conductivity under deformation condition. Based on the fractal theory, a simple method is presented in this study for predicting the relative hydraulic conductivity under deformation condition. From the experimental soil-water characteristic curve at a reference state, the fractal dimension and air-entry value are determined at a reference state. By using the prediction model of air-entry value, the air-entry values at the deformed state are then determined. With the two parameters determined, the relative hydraulic conductivity at the deformed state is predicted using the fractal model of relative hydraulic conductivity. The unsaturated hydraulic conductivity of deformable Hunan clay is measured by the instantaneous profile method. Values of relative hydraulic conductivity predicted by the fractal model are compared with those obtained from experimental measurements, which proves the rationality of the proposed prediction method.

Aims To identify and group hospitalization trajectory of alcohol use disorder (AUD) patients and its associations with service utilization, healthcare quality and hospital-level variations. Methods Inpatients with AUD as the primary diagnosis from 2012 to 2014 in Beijing, China, were identified. Their discharge medical records were extracted and analyzed using the sequence analysis and the cluster analysis. Results Eight-hundred thirty-one patients were included, and their hospitalization patterns were grouped into four clusters: short stay (n = 565 (67.99%)), mean psychiatric length of stay in 3 years: (32.25 ± 18.69), repeated short stay (n = 211 (25.39%), 137.76 ± 88.8 days), repeated long stay (n = 41 (4.93%), 405.44 ± 146.54 days), permanent stay (n = 14 (1.68%), 818.14 ± 225.22 days). The latter two clusters (6.61% patients) used 37.26% of the total psychiatric hospital days and 33.65% of the total psychiatric hospitalization expenses. All the patients in the permanent stay cluster and 41.77% of the patients in the short stay cluster were readmitted at least once within 3 years. Two-hundred thirty-four patients (28.16%) were admitted at least once for non-psychiatric reasons, primarily for diseases of circulatory and digestive systems. Cluster composition varied significantly among different hospitals. Conclusion Hospitalization pattern of patients with AUD varies greatly, and while most (>2/3) hospitalizations were short stay, those with repeated long stay and permanent stay used more than one third of the hospital days and expenses. Our findings suggest interventions targeting at certain patients may be more effective in reducing resource utilization.