Biblio

Reliable and secure grid operations become more and more challenging in context of increasing IT/OT convergence and decreasing dynamic margins in today's power systems. To ensure the correct operation of monitoring and control functions in control centres, an intelligent assessment of the different information sources is necessary to provide a robust data source in case of critical physical events as well as cyber-attacks. Within this paper, a holistic data stream assessment methodology is proposed using an expert knowledge based cyber-physical situational awareness for different steady and transient system states. This approach goes beyond existing techniques by combining high-resolution PMU data with SCADA information as well as Digital Twin and AI based anomaly detection functionalities.

The center-piece of this work is a software measurement physical unclonable function (PUF). It measures processor chip ALU silicon biometrics in a manner similar to all PUFs. Additionally, it composes the silicon measurement with the data-dependent delay of a particular program instruction in a way that is difficult to decompose through a mathematical model. This approach ensures that each software instruction is measured if computed. The SW-PUF measurements bind the execution of software to a specific processor with a corresponding certificate. This makes the SW-PUF a promising candidate for applications requiring Trusted Computing. For instance, it could measure the integrity of an execution path by generating a signature that is unique to the specific program execution path and the processor chip. We present an area and energy-efficient scheme based on the SW-PUF to provide a more robust root of trust for measurement than the existing trusted platform module (TPM). To explore the feasibility of the proposed design, the SW-PUF has been implemented in HSPICE using 45 nm technology and evaluated on the FPGA platform.

To improve the resilience of state estimation strategy against cyber attacks, the Compressive Sensing (CS) is applied in reconstruction of incomplete measurements for cyber physical systems. First, observability analysis is used to decide the time to run the reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over-completed dictionary by K-Singular Value Decomposition (K-SVD). Besides, due to the irregularity of incomplete measurements, sampling matrix is designed as the measurement matrix. Finally, the simulation experiments on 6-bus power system illustrate that the proposed method achieves the incomplete measurements reconstruction perfectly, which is better than the joint dictionary. When only 29% available measurements are left, the proposed method has generality for four kinds of recovery algorithms.

Usually, the air gap will appear inside the composite insulators and it will lead to serious accident. In order to detect these internal defects in composite insulators operated in the transmission lines, a new non-destructive technique has been proposed. In the study, the mathematical analysis model of the composite insulators inner defects, which is about heat diffusion, has been build. The model helps to analyze the propagation process of heat loss and judge the structure and defects under the surface. Compared with traditional detection methods and other non-destructive techniques, the technique mentioned above has many advantages. In the study, air defects of composite insulators have been made artificially. Firstly, the artificially fabricated samples are tested by flash thermography, and this method shows a good performance to figure out the structure or defects under the surface. Compared the effect of different excitation between flash and hair drier, the artificially samples have a better performance after heating by flash. So the flash excitation is better. After testing by different pollution on the surface, it can be concluded that different pollution don't have much influence on figuring out the structure or defect under the surface, only have some influence on heat diffusion. Then the defective composite insulators from work site are detected and the image of defect is clear. This new active thermography system can be detected quickly, efficiently and accurately, ignoring the influence of different pollution and other environmental restrictions. So it will have a broad prospect of figuring out the defeats and structure in composite insulators even other styles of insulators.

Standard classification procedures of both data mining and multivariate statistics are sensitive to the presence of outlying values. In this paper, we propose new algorithms for computing regularized versions of linear discriminant analysis for data with small sample sizes in each group. Further, we propose a highly robust version of a regularized linear discriminant analysis. The new method denoted as MWCD-L2-LDA is based on the idea of implicit weights assigned to individual observations, inspired by the minimum weighted covariance determinant estimator. Classification performance of the new method is illustrated on a detailed analysis of our pilot study of authentication methods on computers, using individual typing characteristics by means of keystroke dynamics.

Wireless information security generates shared secret keys from reciprocal channel dynamics. Current solutions are mostly based on temporal per-frame channel measurements of signal strength and suffer from low key generate rate (KGR), large budget in channel probing, and poor secrecy if a channel does not temporally vary significantly. This paper designs a cross-layer solution that measures noise-free per-symbol channel dynamics across both time and frequency domain and derives keys from the highly fine-grained per-symbol reciprocal channel measurements. This solution consists of merits that: (1) the persymbol granularity improves the volume of available uncorrelated channel measurements by orders of magnitude over per-frame granularity in conventional solutions and so does KGR; 2) the solution exploits subtle channel fluctuations in frequency domain that does not force users to move to incur enough temporal variations as conventional solutions require; and (3) it measures noise-free channel response that suppresses key bit disagreement between trusted users. As a result, in every aspect, the proposed solution improves the security performance by orders of magnitude over conventional solutions. The performance has been evaluated on both a GNU SDR testbed in practice and a local GNU Radio simulator. The cross-layer solution can generate a KGR of 24.07 bits per probing frame on testbed or 19 bits in simulation, although conventional optimal solutions only has a KGR of at most one or two bit per probing frame. It also has a low key bit disagreement ratio while maintaining a high entropy rate. The derived keys show strong independence with correlation coefficients mostly less than 0.05. Furthermore, it is empirically shown that any slight physical change, e.g. a small rotation of antenna, results in fundamentally different cross-layer frequency measurements, which implies the strong secrecy and high efficiency of the proposed solution.