Biblio

Image edge is considered to be the most important attribute to provide valuable image perception information. At present, video image data is developing towards high resolution and high frame number. The image data processing capacity is huge, so the processing speed is very strict to meet the real-time performance of image data transmission. In this context, we present a method to accelerate the real-time video image edge detection. FPGA is used as the development platform. The real-time edge detection algorithm of image data with 1280x720 resolution and 30 frame/s, combined with median filter, Sobel edge detection algorithm and corrosion expansion algorithm, makes the running time of image processing module shorter. The color image of the video image collected by camera is processed. The HDMI interface shows that the scheme has achieved ideal results in the FPGA hardware platform simulation model, greatly improves the efficiency of the algorithm, and provides a guarantee for the speed and stability of the real-time image processing system.

With the popularity of smart devices in the power grid and the advancement of data collection technology, the amount of electricity usage data has exploded in recent years, which is beneficial for optimizing service quality and grid operation. However, current data analysis is mainly based on cloud platforms, which poses challenges to transmission bandwidth, computing resources, and transmission delays. To solve the problem, this paper proposes a graph convolution neural networks (GCNs) based edge-cloud collaborative anomaly detection model. Specifically, the time series is converted into graph data based on visibility graph model, and graph convolutional network model is adopted to classify the labeled graph data for anomaly detection. Then a model segmentation method is proposed to adaptively divide the anomaly detection model between the edge equipment and the back-end server. Experimental results show that the proposed scheme provides an effective solution to edge anomaly detection and can make full use of the computing resources of terminal equipment.

This paper presents some of our first experiences and findings in the ARPA-E project ReNew100, which is to develop an operator support system to enable stable operation of power system with 100% non-synchronous (NS) generation. The key to 100% NS system, as found in many recent studies, is to establish the grid frequency reference using grid-forming (GFM) inverters. In this paper, we demonstrate in Electro-Magnetic-Transient (EMT) simulations, based on Hawai'i big island system with 100% NS capacity, that a system can be operated stably with the help of GFM inverters and appropriate controller parameters for the inverters. The dynamic security optimization (DSO) is introduced for optimizing the inverter control parameters to improve stability of the system towards N-1 contingencies. DSO is verified for five critical N-1 contingencies of big island system identified by Hawaiian Electric. The simulation results show significant stability improvement from DSO. The results in this paper share some insight, and provide a promising solution for operating grid in general with high penetration or 100% of NS generation.

Aiming at the problems of imperfect dynamic verification of power grid security and stability control strategy and high test cost, a reliability test method of power grid security control system based on BP neural network and dynamic group simulation is proposed. Firstly, the fault simulation results of real-time digital simulation system (RTDS) software are taken as the data source, and the dynamic test data are obtained with the help of the existing dispatching data network, wireless virtual private network, global positioning system and other communication resources; Secondly, the important test items are selected through the minimum redundancy maximum correlation algorithm, and the test items are used to form a feature set, and then the BP neural network model is used to predict the test results. Finally, the dynamic remote test platform is tested by the dynamic whole group simulation of the security and stability control system. Compared with the traditional test methods, the proposed method reduces the test cost by more than 50%. Experimental results show that the proposed method can effectively complete the reliability test of power grid security control system based on dynamic group simulation, and reduce the test cost.

This contribution provides the implementation of a holistic operational security assessment process for both steady-state security and dynamic stability. The merging of steady-state and dynamic security assessment as a sequential process is presented. A steady-state and dynamic modeling of a VSC-HVDC was performed including curative and stabilizing measures as remedial actions. The assessment process was validated by a case study on a modified version of the Nordic 32 system. Simulation results showed that measure selection based on purely steady-state contingency analysis can lead to loss of stability in time domain. A subsequent selection of measures on the basis of the dynamic security assessment was able to guarantee the operational security for the stationary N-1 scenario as well as the power system stability.

Concurrency programs often induce buggy results due to the unexpected interaction among threads. The detection of these concurrency bugs costs a lot because they usually appear under a specific execution trace. How to virtually explore different thread schedules to detect concurrency bugs efficiently is an important research topic. Many techniques have been proposed, including lightweight techniques like adaptive randomized scheduling (ARS) and heavyweight techniques like maximal causality reduction (MCR). Compared to heavyweight techniques, ARS is efficient in exploring different schedulings and achieves state-of-the-art performance. However, it will lead to explore large numbers of redundant thread schedulings, which will reduce the efficiency. Moreover, it suffers from the “cold start” issue, when little information is available to guide the distance calculation at the beginning of the exploration. In this work, we propose a Heuristic-Enhanced Adaptive Randomized Scheduling (HARS) algorithm, which improves ARS to detect concurrency bugs guided with novel distance metrics and heuristics obtained from existing research findings. Compared with the adaptive randomized scheduling method, it can more effectively distinguish the traces that may contain concurrency bugs and avoid redundant schedules, thus exploring diverse thread schedules effectively. We conduct an evaluation on 45 concurrency Java programs. The evaluation results show that our algorithm performs more stably in terms of effectiveness and efficiency in detecting concurrency bugs. Notably, HARS detects hard-to-expose bugs more effectively, where the buggy traces are rare or the bug triggering conditions are tricky.

Deep learning has made remarkable achievements in various domains. Active learning, which aims to reduce the budget for training a machine-learning model, is especially useful for the Deep learning tasks with the demand of a large number of labeled samples. Unfortunately, our empirical study finds that many of the active learning heuristics are not effective when applied to Deep learning models in batch settings. To tackle these limitations, we propose a density weighted diversity based query strategy (DWDS), which makes use of the geometry of the samples. Within a limited labeling budget, DWDS enhances model performance by querying labels for the new training samples with the maximum informativeness and representativeness. Furthermore, we propose a beam-search based method to obtain a good approximation to the optimum of such samples. Our experiments show that DWDS outperforms existing algorithms in Deep learning tasks.

The security issue of complex network systems, such as communication systems and power grids, has attracted increasing attention due to cascading failure threats. Many existing studies have investigated the robustness of complex networks against cascading failure from an attacker's perspective. However, most of them focus on the synchronous attack in which the network components under attack are removed synchronously rather than in a sequential fashion. Most recent pioneering work on sequential attack designs the attack strategies based on simple heuristics like degree and load information, which may ignore the inside functions of nodes. In the paper, we exploit a reinforcement learning-based sequential attack method to investigate the impact of different nodes on cascading failure. Besides, a candidate pool strategy is proposed to improve the performance of the reinforcement learning method. Simulation results on Barabási-Albert scale-free networks and real-world networks have demonstrated the superiority and effectiveness of the proposed method.

In quantum cryptography research area, quantum digital signature is an important research field. To provide a better privacy for users in constructing quantum digital signature, the stronger anonymity of quantum digital signatures is required. Quantum ring signature scheme focuses on anonymity in certain scenarios. Using quantum ring signature scheme, the quantum message signer hides his identity into a group. At the same time, there is no need for any centralized organization when the user uses the quantum ring signature scheme. The group used to hide the signer identity can be immediately selected by the signer himself, and no collaboration between users.Since the quantum finite automaton signature scheme is very efficient quantum digital signature scheme, based on it, we propose a new quantum ring signature scheme. We also showed that the new scheme we proposed is of feasibility, correctness, anonymity, and unforgeability. And furthermore, the new scheme can be implemented only by logical operations, so it is easy to implement.

With the emergence of quantum computers, traditional digital signature schemes based on problems such as large integer solutions and discrete logarithms will no longer be secure, and it is urgent to find effective digital signature schemes that can resist quantum attacks. Lattice cryptography has the advantages of computational simplicity and high security. In this paper, we propose an identity-based digital signature scheme based on the rejection sampling algorithm. Unlike most schemes that use a common Gaussian distribution, this paper uses a bimodal Gaussian distribution, which improves efficiency. The identity-based signature scheme is more convenient for practical application than the traditional certificate-based signature scheme.

Recently, federated learning (FL), as an advanced and practical solution, has been applied to deal with privacy-preserving issues in distributed multi-party federated modeling. However, most existing FL methods focus on the same privacy-preserving budget while ignoring various privacy requirements of participants. In this paper, we for the first time propose an algorithm (PLU-FedOA) to optimize the deep neural network of horizontal FL with personalized local differential privacy. For such considerations, we design two approaches: PLU, which allows clients to upload local updates under differential privacy-preserving of personally selected privacy level, and FedOA, which helps the server aggregates local parameters with optimized weight in mixed privacy-preserving scenarios. Moreover, we theoretically analyze the effect on privacy and optimization of our approaches. Finally, we verify PLU-FedOA on real-world datasets.

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.

Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (NILM) in the compressive sensing framework, and bridge the gap between theoretical accuracy of NILM inference and differential privacy's parameters. We then derive the valid theoretical bounds to offer insights on how the differential privacy parameters affect the NILM performance. Moreover, we generalize our conclusions by proposing the hierarchical framework to solve the multishot NILM problem. Numerical experiments verify our analytical results and offer better physical insights of differential privacy in various practical scenarios. This also demonstrates the significance of our work for the general privacy preserving mechanism design.

With the development of deep learning technology, a large number of new technologies for video anomaly detection have emerged. This paper proposes a video anomaly detection algorithm based on the future frame prediction using Generative Adversarial Network (GAN) and attention mechanism. For the generation model, a U-Net model, is modified and added with an attention module. For the discrimination model, a Markov GAN discrimination model with self-attention mechanism is proposed, which can affect the generator and improve the generation quality of the future video frame. Experiments show that the new video anomaly detection algorithm improves the detection performance, and the attention module plays an important role in the overall detection performance. It is found that the more the attention modules are appliedthe deeper the application level is, the better the detection effect is, which also verifies the rationality of the model structure used in this project.

Video analytics has many applications in traffic control, security monitoring, action/event analysis, etc. With the adoption of deep neural networks, the accuracy of video analytics in video streams has been greatly improved. However, deep neural networks for performing video analytics are compute-intensive. In order to reduce processing time, many systems switch to the lower frame rate or resolution. State-of-the-art switching approaches adjust configurations by profiling video clips on a large configuration space. Multiple configurations are tested periodically and the cheapest one with a desired accuracy is adopted. In this paper, we propose a method that adapts the configuration by analyzing past video analytics results instead of profiling candidate configurations. Our method adopts a lower/higher resolution or frame rate when objects move slow/fast. We train a model that automatically selects the best configuration. We evaluate our method with two real-world video analytics applications: traffic tracking and pose estimation. Compared to the periodic profiling method, our method achieves 3%-12% higher accuracy with the same resource cost and 8-17x faster with comparable accuracy.

This paper designs a data anomaly detection method for power grid data centers. The method uses cloud computing architecture to realize the storage and calculation of large amounts of data from power grid data centers. After that, the STL decomposition method is used to decompose the grid data, and then the decomposed residual data is used for anomaly analysis to complete the detection of abnormal data in the grid data. Finally, the feasibility of the method is verified through experiments.

This paper describes a Cyber Threat, Vulnerability and Defense Modeling and Simulation tool kit used for evaluation of systems and networks to improve cyber resiliency. This capability is used to help increase the resiliency of networks at various stages of their lifecycle, from initial design and architecture through the operation of deployed systems and networks. Resiliency of computer systems and networks to cyber threats is facilitated by the modeling of agile and resilient defenses versus threats and running multiple simulations evaluated against resiliency metrics. This helps network designers, cyber analysts and Security Operations Center personnel to perform trades using what-if scenarios to select resiliency capabilities and optimally design and configure cyber resiliency capabilities for their systems and networks.

The smart electricity grids have been evolving to a more complex cyber-physical ecosystem of infrastructures with integrated communication networks, new carbon-free sources of power generation, advanced monitoring and control systems, and a myriad of emerging modern physical hardware technologies. With the unprecedented complexity and heterogeneity in dynamic smart grid networks comes additional vulnerability to emerging threats such as cyber attacks. Rapid development and deployment of advanced network monitoring and communication systems on one hand, and the growing interdependence of the electric power grids to a multitude of lifeline critical infrastructures on the other, calls for holistic defense strategies to safeguard the power grids against cyber adversaries. In order to improve the resilience of the power grid against adversarial attacks and cyber intrusions, advancements should be sought on detection techniques, protection plans, and mitigation practices in all electricity generation, transmission, and distribution sectors. This survey discusses such major directions and recent advancements from a lens of different detection techniques, equipment protection plans, and mitigation strategies to enhance the energy delivery infrastructure resilience and operational endurance against cyber attacks. This undertaking is essential since even modest improvements in resilience of the power grid against cyber threats could lead to sizeable monetary savings and an enriched overall social welfare.

Highly distributed self-organizing CPS exhibit coordination schemata and communication requirements which are similar to structured overlay networks. To determine the resilience of such overlays, we analyze the connectivity of Kademlia, which has been successfully deployed in multiple applications with several thousands of nodes, e.g., BitTorrent. We measure the network connectivity within extensive simulations for different network configurations and present selected results.

Enabled by the industrial Internet, intelligent transportation has made remarkable achievements such as autonomous vehicles by carnegie mellon university (CMU) Navlab, Google Cars, Tesla, etc. Autonomous vehicles benefit, in various aspects, from the cooperation of the industrial Internet and cyber-physical systems. In this process, users in autonomous vehicles submit query contents, such as service interests or user locations, to service providers. However, privacy concerns arise since the query contents are exposed when the users are enjoying the services queried. Existing works on privacy preservation of query contents rely on location perturbation or k-anonymity, and they suffer from insufficient protection of privacy or low query utility incurred by processing multiple queries for a single query content. To achieve sufficient privacy preservation and satisfactory query utility for autonomous vehicles querying services in cyber-physical systems, this article proposes a novel privacy notion of client-based personalized k-anonymity (CPkA). To measure the performance of CPkA, we present a privacy metric and a utility metric, based on which, we formulate two problems to achieve the optimal CPkA in term of privacy and utility. An approach, including two modules, to establish mechanisms which achieve the optimal CPkA is presented. The first module is to build in-group mechanisms for achieving the optimal privacy within each content group. The second module includes linear programming-based methods to compute the optimal grouping strategies. The in-group mechanisms and the grouping strategies are combined to establish optimal CPkA mechanisms, which achieve the optimal privacy or the optimal utility. We employ real-life datasets and synthetic prior distributions to evaluate the CPkA mechanisms established by our approach. The evaluation results illustrate the effectiveness and efficiency of the established mechanisms.

Cyber-physical systems are particularly difficult to model and simulate because their components mix many different system modalities. In this paper we address the main technical challenges on system simulation taking into account by new characters of CPS, and provide a comprehensive view of the simulation modeling methods for integration of continuous-discrete model. Regards to UML and Simulink, two most widely accepted modeling methods in industrial designs, we study on three methods to perform the cooperation of these two kinds of heterogeneous models for co-simulation. The solution of an implementation of co-simulation method for CPS was designed under three levels architecture.

Cyber-Physical System(CPS) is now a new evolutional morphology of embedded systems. With features of merging computation and physical processes together, the traditional verification and simulation methods have being challenged recently. After analyzed the state-of-art of related research, a new simulation environment is studied according to the characters of a special autonomous cyber-physical system-Unmanned Aerial Vehicle, and designed to be scene-driven, modeling and reconfigurable. In this environment, a novel CPS-in-loop architecture, which can support simulations under different customized scenes, is studied firstly to ensure its opening and flexibility. And as another foundation, some dynamics models of CPS and atmospheric ones of relative sensors are introduced to simulate the motion of CPS and the change of its posture. On the basis above, the reconfigurable scene-driven mechanisms that are Based on hybrid events are mainly excogitated. Then, different scenes can be configured in terms of special verification requirements, and then each scene will be decomposed into a spatio-temporal event sequence and scheduled by a scene executor. With this environment, not only the posture of CPS, but also the autonomy of its behavior can be verified and observed. It will be meaningful for the design of such autonomous CPS.

In the era of big data, more and more users store data in the cloud. Massive amounts of data have brought huge storage costs to cloud storage providers, and data deduplication technology has emerged. In order to protect the confidentiality of user data, user data should be encrypted and stored in the cloud. Therefore, deduplication of encrypted data has become a research hotspot. Cloud storage provides users with data sharing services, and the sharing of encrypted data is another research hotspot. The combination of encrypted data deduplication and sharing will inevitably become a future trend. The current better-performing updateable block-level message-locked encryption (UMLE) deduplication scheme does not support data sharing, and the performance of the encrypted data de-duplication scheme that introduces data sharing is not as good as that of UMLE. This paper introduces the ciphertext policy attribute based encryption (CP-ABE) system sharing mechanism on the basis of UMLE, applies the CP-ABE method to encrypt the master key generated by UMLE, to achieve secure and efficient data deduplication and sharing. In this paper, we propose a permission verification method based on bilinear mapping, and according to the definition of the security model proposed in the security analysis phase, we prove this permission verification method, showing that our scheme is secure. The comparison of theoretical analysis and simulation experiment results shows that this scheme has more complete functions and better performance than existing schemes, and the proposed authorization verification method is also secure.

CP-ABE (ciphertext-policy attribute-based encryption) is a promising encryption scheme. In this paper, a highly expressive revocable scheme based on the key encryption keys (KEK) tree is proposed. In this method, the cloud server realizes the cancellation of attribute-level users and effectively reduces the computational burden of the data owner and attribute authority. This scheme embeds a unique random value associated with the user in the attribute group keys. The attribute group keys of each user are different, and it is impossible to initiate a collusion attack. Computing outsourcing makes most of the decryption work done by the cloud server, and the data user only need to perform an exponential operation; in terms of security, the security proof is completed under the standard model based on simple assumptions. Under the premise of ensuring security, the scheme in this paper has the functions of revocation and traceability, and the speed of decryption calculation is also improved.

In this paper, we study covert communications between a pair of legitimate transmitter-receiver against a watchful warden over slow fading channels. There coexist multiple friendly helper nodes who are willing to protect the covert communication from being detected by the warden. We propose an uncoordinated jammer selection scheme where those helpers whose instantaneous channel gains to the legitimate receiver fall below a pre-established selection threshold will be chosen as jammers radiating jamming signals to defeat the warden. By doing so, the detection accuracy of the warden is expected to be severely degraded while the desired covert communication is rarely affected. We then jointly design the optimal selection threshold and message transmission rate for maximizing covert throughput under the premise that the detection error of the warden exceeds a certain level. Numerical results are presented to validate our theoretical analyses. It is shown that the multi-jammer assisted covert communication outperforms the conventional single-jammer method in terms of covert throughput, and the maximal covert throughput improves significantly as the total number of helpers increases, which demonstrates the validity and superiority of our proposed scheme.