Biblio

When a fault occurs in power grid, the analytic model for power grid fault diagnosis could generate multiple solutions under one or more protective relays (PRs) and/or circuit breakers (CBs) malfunctioning, and/or one or more their alarm information failing. Hence, this paper, calling the electrical quantities, presents an optimal solution discrimination method, which determines the optimal solution by constructing the electrical criteria of suspicious faulty components. Furthermore, combining the established electrical criteria with the existing analytic model, a hierarchical fault diagnosis mode is proposed. It uses the analytic model for the first level diagnosis based on the switching quantities. Thereafter, aiming at multiple solutions, it applies the electrical criteria for the second level diagnosis to determine the diagnostic result. Finally, the examples of fault diagnosis demonstrate the feasibility and effectiveness of the developed method.

Nowadays, the Internet of Things (IoT) is playing an important role in our life. This inevitably generates mass data and requires a more secure transmission. As blockchain technology can build trust in a distributed environment and ensure the data traceability and tamper resistance, it is a promising way to support IoT data transmission and sharing. In this paper, edge computing is considered to provide adequate resources for end users to offload computing tasks in the blockchain enabled IoT system, and the node pairing problem between end users and edge computing servers is researched with the consideration of wireless channel quality and the service quality. From the perspective of the end users, the objective optimization is designed to maximize the profits and minimize the payments for completing the tasks and ensuring the resource limits of the edge servers at the same time. The deep reinforcement learning (DRL) method is utilized to train an intelligent strategy, and the policy gradient based node pairing (PG-NP) algorithm is proposed. Through a deep neural network, the well-trained policy matched the system states to the optimal actions. The REINFORCE algorithm with baseline is applied to train the policy network. According to the training results, as the comparison strategies are max-credit, max-SINR, random and max-resource, the PG-NP algorithm performs about 57% better than the second-best method. And testing results show that PGNP also has a good generalization ability which is negatively correlated with the training performance to a certain extend.

With the development of the Internet, the network attack technology has undergone tremendous changes. The forms of network attack and defense have also changed, which are features in attacks are becoming more diverse, attacks are more widespread and traditional security protection methods are invalid. In recent years, with the development of software defined security, network anomaly detection technology and big data technology, these challenges have been effectively addressed. This paper proposes a data-driven software defined security architecture with core features including data-driven orchestration engine, scalable network anomaly detection module and security data platform. Based on the construction of the analysis layer in the security data platform, real-time online detection of network data can be realized by integrating network anomaly detection module and security data platform under software defined security architecture. Then, data-driven security business orchestration can be realized to achieve efficient, real-time and dynamic response to detected anomalies. Meanwhile, this paper designs a deep learning-based HTTP anomaly detection algorithm module and integrates it with data-driven software defined security architecture so that demonstrating the flow of the whole system.

The increasing adoption of 3D printing in many safety and mission critical applications exposes 3D printers to a variety of cyber attacks that may result in catastrophic consequences if the printing process is compromised. For example, the mechanical properties (e.g., physical strength, thermal resistance, dimensional stability) of 3D printed objects could be significantly affected and degraded if a simple printing setting is maliciously changed. To address this challenge, this study proposes a model-free real-time online process monitoring approach that is capable of detecting and defending against the cyber-physical attacks on the firmwares of 3D printers. Specifically, we explore the potential attacks and consequences of four key printing attributes (including infill path, printing speed, layer thickness, and fan speed) and then formulate the attack models. Based on the intrinsic relation between the printing attributes and the physical observations, our defense model is established by systematically analyzing the multi-faceted, real-time measurement collected from the accelerometer, magnetometer and camera. The Kalman filter and Canny filter are used to map and estimate three aforementioned critical toolpath information that might affect the printing quality. Mel-frequency Cepstrum Coefficients are used to extract features for fan speed estimation. Experimental results show that, for a complex 3D printed design, our method can achieve 4% Hausdorff distance compared with the model dimension for infill path estimate, 6.07% Mean Absolute Percentage Error (MAPE) for speed estimate, 9.57% MAPE for layer thickness estimate, and 96.8% accuracy for fan speed identification. Our study demonstrates that, this new approach can effectively defend against the cyber-physical attacks on 3D printers and 3D printing process.

Heterogeneous face recognition aims to identify or verify person identity by matching facial images of different modalities. In practice, it is known that its performance is highly influenced by modality inconsistency, appearance occlusions, illumination variations and expressions. In this paper, a new method named as ensemble of sparse cross-modal metrics is proposed for tackling these challenging issues. In particular, a weak sparse cross-modal metric learning method is firstly developed to measure distances between samples of two modalities. It learns to adjust rank-one cross-modal metrics to satisfy two sets of triplet based cross-modal distance constraints in a compact form. Meanwhile, a group based feature selection is performed to enforce that features in the same position of two modalities are selected simultaneously. By neglecting features that attribute to "noise" in the face regions (eye glasses, expressions and so on), the performance of learned weak metrics can be markedly improved. Finally, an ensemble framework is incorporated to combine the results of differently learned sparse metrics into a strong one. Extensive experiments on various face datasets demonstrate the benefit of such feature selection especially when heavy occlusions exist. The proposed ensemble metric learning has been shown superiority over several state-of-the-art methods in heterogeneous face recognition.