Cloud services supported by the cyber-physical system (CPS) have been emerged as a proactive and efficient solution to facilitate the alliance of IoT devices. The CPS assimilates, analyses and shares the processed information among IoT applications. The traditional network infrastructure is not designed to support the increasing demand for high scalability and real-time delivery with ultra-low delay. The proposed system addresses these issues by utilising the optical resources as fog nodes and software-defined networking in the 5G network. A scalable OpticalFog node is proposed that creates the cyberspace near the IoT devices, thus providing an ultra-low delay, minimising the energy consumption. Moreover, for providing service assurance to the various CPS-based applications, an algorithm is proposed to place the task in the software-defined networking. Finally, the performance of the algorithm is evaluated through the simulation in iFogSim toolkit that is used to implement the OpticalFog node in the 5G environment. The results showed the effectiveness of the proposed system.

Electric shock current identification is essential for the safety in power distribution network. Moreover, as different categories of object have different electric shock current characteristic, a classification model for shock current is essential to be proposed before identification. Therefore, the authors proposed a two-stage framework, including the AdaBoost for the classification and an improved support vector machine (SVM) method for the identification. In the classification stage, the AdaBoost learns the hidden pattern of different electric shock current and generates a predictive model for current classification. Based on the classification results, a fusion method called SVM–NN is proposed in the identification stage, which is based on SVM and neural network (NN) to make fusion determination. The SVM–NN takes advantages of SVM and NN for integration analysis. Based on real data, these classification and identification methods are evaluated. Results show that the proposed method can significantly improve the identification accuracy of electric shock current signal comparing to traditional methods.

Insider attacks are one of the most serious threats for cyber-physical systems, they have potentials to inflict destructive damages on physical processes while remaining stealthy. This study dissects several insider attacks by examining their modes of data tampering. To set the scene, a general framework of a cyber-physical system is constructed, a pattern characterising insider attacks is introduced in the form of attack goals, resources, constraints, modes, and attack paths. The conditions under which the attackers can maintain stealthy are examined in both temporal and spatial domains. With the inside knowledge, an attacker can use an attack graph to exploit system vulnerabilities and determine the high impact targets. To demonstrate the effectiveness of this analysis, a cyber-physical system is constructed by using networks and a nuclear process control test facility with ports deliberately left open for attackers. Two attack scenarios are staged, and their characteristics and impacts are examined. This case study demonstrates how an insider attacker might mount an attack by using data tampering and how they can maintain stealthy before major damages are done to the physical system. The significance of this study is to uncover the techniques of insider attackers so that vulnerabilities can be mended.

As dynamic voltage and frequency scaling (DVFS) does not consider predicting system behaviour in the future stage, to improve efficiency of DVFS in fine-grained, the authors propose a central processing unit (CPU) utilisation prediction model based on radial basis function neural network. Their model first collects five typical system characteristics related to CPU utilisation during system running, then they use radial basis neural network to fit the non-linear relationship between these system characteristics and CPU utilisation in the next period. According to the predicted CPU utilisation, specific frequency scaling is performed to change frequency in real time. Finally, they evaluate their model with classical DVFS by means of different task sets. Experimental results show that their model could predict CPU utilisation in more fine-grained compared with other models, and changes frequency-scaling effect of traditional DVFS.

This study proposes an optimal bandwidth allocation algorithm for a Networked Control System (NCS) that includes a time-driven sensor, event-driven controller and random channels. The authors predict resource demands at the next time step for the random channels, then calculate the resource demands from the event-driven and the time-driven channels. They allocate bandwidths for each channel by solving a convex optimisation problem based on the resource demands. When the total traffic exceeds the total capacity of the network, only the random and event-driven network bandwidths are allocated optimally. For time-driven channels, they increase the sampling period to reduce traffic volume and satisfy the total capacity constraint. This maintains sufficiently slow sampling to improve the quality of service in NCS. An upper bound on the sampling period is set to guarantee sufficiently fast sampling for control dynamics. Simulation results show that the proposed approach minimises network congestion.

Water quality monitoring and prediction are important parts of Cyber Physical Systems. Considering the complexity, diversity, and strong non-linearity of water quality data, a single water quality prediction model is difficult to have a significant effect on different data. To solve this problem, a new water quality prediction method based on the preferred classification is proposed in this study. A preferred classifier is established to integrate back propagation neural network, support vector machines for regression and long short-term memory due to the fact that these three prediction models can take into account the different characteristics of water quality data. When new data input, the proposed method preferentially selects the prediction model that is most suitable for the data, and then uses the selected model for prediction. Finally, the proposed method is applied in two actual datasets: Songhua River and Victoria Bay. Experimental results demonstrate that the water quality prediction method based on preferred classification achieves better performance than any of the three single prediction models.

On the basis of the problem that the image background is simple and the traditional shooting equipment of fruit flies is too high, this study improved the convolutional neural network model. First, the authors changed Softmax classifier to support vector machine (SVM). Moreover, then used convolution layers for extracting features of fruit fly images. Finally, they fed features into SVM for training. Experiments show that the model has been classifying the Bactrocera dorsalis Hendel, Bactrocera cucurbitae, Bactrocera tau and Bactrocera scutellata with accuracy over 92.04%, accordingly making the effective classification of the complex background fruit fly images possible. Moreover, it also provides a good practical application prospect.

As the power grid continues to evolve with advanced wide-area monitoring, protection, and control (WAMPAC) algorithms, there is an increasing need for realistic testbed environments with industry-grade software and hardware-in-the-loop (HIL) to perform verification and validation studies. Such testbed environments serve as ideal platforms to perform WAMPAC prototyping, operator training, and also to study the impacts of different types of cyberattack scenarios on the operation of the grid. In this study, the authors introduce pacific northwest national laboratory(PNNL) cyber-physical systems testbed (PRIME): the testbed that integrates real-time transmission system simulator with commercial industry-grade energy management system software and remote HIL (RHIL). PRIME is an end-to-end, modular testbed that allows high-fidelity RHIL experimentation of a power system. We present two detailed case studies (fault location and clearing in the transmission system and operator training) to show the capabilities of their PRIME testbed. Finally, we briefly discuss some of the potential limitations of their testbed in terms of scalability and flexibility to set up larger test systems and identify directions for future work to address these limitations.

Mobile edge computing (MEC) is an effective assistant technology that can overcome some defects of cloud computing. For the sake of alleviating the clashes between the capability constraint of cloudlets and the needs of mobile devices (MDs) for reducing executing latency as well as decreasing the power consumption of MDs, a user-oriented use case in the MEC named computation offloading is taken into consideration. Computation offloading is capable of effectively making the MEC adapt to the resources of cloudlets and MDs in different environments, and it is very beneficial to the development of the internet of things. Owing to the finite computation capabilities of the MDs and the resources of cloudlets are heterogeneous and limited; a three-objective model is established to optimise the time consumption, and the energy consumption of MDs as well as the load balancing of cloudlets jointly. Technically, the authors propose an effective multi-user multi-application computation offloading method in the multi-cloudlet environment on the basis of improved non-dominated sorting genetic algorithm III. Finally, comprehensive experiments and analysis were conducted to validate the effectiveness and efficiency of the proposed method.

This article focuses on mitigation of zero dynamic attack in communication link-enabled droop-controlled hybrid AC/DC microgrids (MGs). To transmit setpoints for droop controllers and to also send measured AC and DC voltages and AC frequency in this sort of MGs, a communication link is required. Such links are exposed to cyber-attacks. First of all, this article tries to indicate that the system would be vulnerable to zero dynamic attack. In the second step, it is shown that zero dynamic attack can be mitigated by closing the secondary control loop. It is also shown that the attack can be distinguished from load/generation disturbances. In order to proceed with the challenge, the control signal in a closed loop system is used as the tricks of the trade. To achieve the goal, parity space as a kind of model-based fault detection approach is applied to a recently proposed dynamic model for droop-controlled hybrid AC/DC MGs. Evaluation of the detecting approach confirms that not only it can detect the attack effectively, but also it distinguishes from disturbance perfectly.

This study investigates the impacts of stealthy false data injection (FDI) attacks that corrupt the state estimation operation of power distribution systems (PDS). In particular, the authors analyse FDI attacks that target the integrity of distribution systems optimal power flow (DSOPF) in order to maximise the system operator losses. The branch current state estimation method is implemented to accurately model the PDS, and convex relaxations are applied to the DSOPF model. The effects of the FDI attacks are analysed on the IEEE 34-bus unbalanced radial distribution system, with distributed energy resources (DERs) along the feeder. A 24 h DSPOF is performed, and the results depict the changes in the voltage profile and the additional power injection from the DERs, which consequently lead to the increase of the DSOPF cost.

Internet-connected devices will represent an increasing proportion of the load served by electric power systems. As these devices could conceivably be hijacked and controlled remotely by a malicious actor, they could represent a new threat vector against the dynamic security of a power system. Such attack strategies have not been considered in the existing literature on power system cybersecurity. As an initial scoping exercise, the present case study explores whether such devices could be remotely hijacked and then maliciously power-cycled at particular frequencies to deliberately provoke harmful oscillations in an electrical grid. To gauge the broad feasibility of this novel style of attack, dynamic simulations are performed on two representative test power systems, at differing levels of attacker and defender resources. These simulations show that power-cycling just 1% of consumer loads at a system's resonant frequency may sometimes provoke harmful electromechanical oscillations throughout a national grid. This novel simulation exercise, therefore, implies that cybersecurity vulnerabilities at the consumer side could jeopardise the physical integrity of a nation's entire electricity supply.