Biblio

The paper presents the design of fuzzy logic controller based unified power flow controller (UPFC) to improve power system security performance during steady state as well as fault conditions. Fuzzy interference has been design with two inputs Vref and Vm for the shunt voltage source Converter and two inputs for Series Id, Idref, Iq, Iqref at the series voltage source converter location. The coordination of shunt and series VSC has been achieved by using fuzzy logic controller (FLC). The comparative performance of PI based UPFC and fuzzy based UPFC under abnormal condition has been validated in MATLB domain. The combination of fuzzy with a UPFC is tested on multi machine system in MATLAB domain. The results shows that the power system security enhancement as well as oscillations damping.

Various approaches of microgrid operation have been proposed, albeit with noticeable issues such as power-sharing, control of frequency and voltage excursions, applicability on different grids, etc. This paper proposes a goal function-based, decentralized control that addresses the mentioned problems and secures the microgrid stability by constraining the frequency and node deviations across the grid while simultaneously supporting the desired active power exchange between prosumer nodes. The control algorithm is independent of network topology and enables arbitrary node connection, i.e. seamless microgrid expandability. To confirm the effectiveness of the proposed control strategy, simulation results are presented and discussed.

Power system robustness against high-impact low probability events is becoming a major concern. To depict distinct phases of a system response during these disturbances, an irregular polygon model is derived from the conventional trapezoid model and the model is analytically investigated for transmission system performance, based on which resiliency metrics are developed for the same. Furthermore, the system resiliency to windstorms is evaluated on the IEEE reliability test system (RTS) by performing steady-state and dynamic security assessment incorporating protection modelling and corrective action schemes using the Power System Simulator for Engineering (PSS®E) software. Based on the results of steady-state and dynamic analysis, modified resiliency metrics are quantified. Finally, this paper quantifies the interdependency of operational and infrastructure resiliency as they cannot be considered discrete characteristics of the system.

The use of Virtual Machine (VM) migration as support for software rejuvenation was introduced more than a decade ago. Since then, several works have validated this approach from experimental and theoretical perspectives. Recently, some works shed light on the possibility of using the same technique as Moving Target Defense (MTD). However, to date, no work evaluated the availability and security levels while applying VM migration for both rejuvenation and MTD (multipurpose VM migration). In this paper, we conduct a comprehensive evaluation using Stochastic Petri Net (SPN) models to tackle this challenge. The evaluation covers the steady-state system availability, expected MTD protection, and related metrics of a system under time-based multipurpose VM migration. Results show that the availability and security improvement due to VM migration deployment surpasses 50% in the best scenarios. However, there is a trade-off between availability and security metrics, meaning that improving one implies compromising the other.

Power system security assessment and enhancement in grids with high penetration of renewables is critical for pragmatic power system planning. Static Security Assessment (SSA) is a fast response tool to assess system stability margins following considerable contingencies assuming post fault system reaches a steady state. This paper presents a contingency ranking methodology using static security indices to rank credible contingencies considering severity. A Modified IEEE 9 bus system integrating renewables was used to test the approach. The static security indices used independently provides accurate results in identifying severe contingencies but further assessment is needed to provide an accurate picture of static security assessment in an increased time frame of the steady state. The indices driven for static security assessment could accurately capture and rank contingencies with renewable sources but due to intermittency of the renewable source various contingency ranking lists are generated. This implies that using indices in future grids without consideration on intermittent nature of renewables will make it difficult for the grid operator to identify severe contingencies and assist the power system operator to make operational decisions. This makes it necessary to integrate the behaviour of renewables in security indices for practical application in real time security assessment.

Static security assessment is of great significance to ensure the stable transmission of electric power and steady operation of load. The scale of power system trends to expand due to the development of interconnected grid, and the security analysis of the entire network has become time-consuming. On the basis of synthesizing the efficiency and accuracy, a new method is developed. This method adopts a novel dynamic power flow (DPF) model considering the influence of governor deadband and amplitude-limit on the steady state quantitatively. In order to reduce the computation cost, a contingency screening algorithm based on binary search method is proposed. Static security assessment based on the proposed DPF models is applied to calculate the security margin constrained by severe contingencies. The ones with lower margin are chosen for further time-domain (TD) simulation analysis. The case study of a practical grid verifies the accuracy of the proposed model compared with the conventional one considering no governor nonlinearity. Moreover, the test of a practical grid in China, along with the TD simulation, demonstrates that the proposed method avoids massive simulations of all contingencies as well as provides detail information of severe ones, which is effective for security analysis of practical power grids.

Smart grids are one of the most important applications of cyber-physical systems. They intelligently transmit energy to customers by information technology, and have replaced the traditional power grid and are widely used. However, smart grids are vulnerable to cyber-attacks. Once attacked, it will cause great losses and lose the trust of customers. Therefore, it is important to evaluate the trustworthiness of smart grids. In order to evaluate the trustworthiness of smart grids, this paper uses a generalized stochastic Petri net (GSPN) to model smart grids. Considering various security threats that smart grids may face, we propose a general GSPN model for smart grids, which evaluates trustworthiness from three metrics of reliability, availability, and integrity by analyzing steady-state and transient probabilities. Finally, we obtain the value of system trustworthiness and simulation results show that the feasibility and effectiveness of our model for smart grids trustworthiness.

In state estimation of steady-state power networks, a cyber attack that cannot be detected from the residual (i.e., the estimation error) is called a false data injection attack. In this paper, to enforce security of power networks, we propose a method of detecting a false data injection attack. In the proposed method, a false data injection attack is detected by randomly choosing sensors used in state estimation. The effectiveness of the proposed method is presented by two numerical examples including the IEEE 14-bus system.

Having a clear insight on the protocols carrying traffic is crucial for network applications. Deep Packet Inspection (DPI) has been a key technique to provide visibility into traffic. DPI has proven effective in various scenarios, and indeed several open source DPI solutions are maintained by the community. Yet, these solutions provide different classifications, and it is hard to establish a common ground truth. Independent works approaching the question of the quality of DPI are already aged and rely on limited datasets. Here, we test if open source DPI solutions can provide useful information in practical scenarios, e.g., supporting security applications. We provide an evaluation of the performance of four open-source DPI solutions, namely nDPI, Libprotoident, Tstat and Zeek. We use datasets covering various traffic scenarios, including operational networks, IoT scenarios and malware. As no ground truth is available, we study the consistency of classification across the solutions, investigating rootcauses of conflicts. Important for on-line security applications, we check whether DPI solutions provide reliable classification with a limited number of packets per flow. All in all, we confirm that DPI solutions still perform satisfactorily for well-known protocols. They however struggle with some P2P traffic and security scenarios (e.g., with malware traffic). All tested solutions reach a final classification after observing few packets with payload, showing adequacy for on-line applications.

Transient simulation is a critical task of validating the dynamic model of the power grid. We propose an off-line method for validating dynamic grid models and assessing the dynamic security of the grid in the presence of cyberattacks. Simulations are executed in PowerWorld and PSCAD/EMTDC to compare the impact on the grid of cyber-attacks. Generators in the IEEE 14-bus system have been modified to match the need of adjustment in modern power system operation. To get effective measurements for state estimation, SCADA polling model is reproduced in PSCAD/EMTDC by providing controlled sampling frequency. The results of a tripped line case and injecting false data to the loads caused by cyberattacks is presented and analyzed.

The determination of the damper cage health is a matter of great importance in those industries that use large synchronous motors in their processes. In the past, unexpected damages of that element implied economic losses amounting up to several million \$. The problem is that, in the technical literature, there is a lack of non-invasive techniques enabling the reliable condition monitoring of this element. This explains the fact that, in industry, rudimentary methods are still employed to determine its condition. This paper proposes the analysis of the stray flux as a way to determine the condition of the damper cage. The paper shows that the analysis of the stray flux under starting yields characteristic time-frequency signatures of the fault components that can be used to reliably determine the condition of the damper. Moreover, the analysis of the stray flux at steady-state operation under asynchronous mode could give useful information to this end. The paper also analyses the influence of the remanent magnetism in the rotor of some synchronous motors, which can make the damper cage diagnosis more difficult; some solutions to this problem are also suggested in the paper.

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 security of Cyber-physical systems has been a hot topic in recent years. There are two main focuses in this area: Firstly, what kind of attacks can avoid detection, i.e., the stealthiness of attacks. Secondly, what kind of systems can stay stable under stealthy attacks, i.e., the invulnerability of systems. In this paper, we will give a detailed characterization for stealthy attacks and detection criterion for such attacks. We will also study conditions for the vulnerability of a stochastic linear system under stealthy attacks.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting a given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed. Then, the finite IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a two-fold increase in the quality of information (QoI) compared to the baseline when the nodes are always transmitting.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system, in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents, which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite-IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information compared with a baseline scheme, in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared with the baseline.

Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.

We consider a moving-target defense of a proxied multiserver tenant of the cloud where the proxies dynamically change to defeat reconnaissance activity by a botnet planning a DDoS attack targeting the tenant. Unlike the system of [4] where all proxies change simultaneously at a fixed rate, we consider a more “responsive” system where the proxies may change more rapidly and selectively based on the current session request intensity, which is expected to be abnormally large during active reconnaissance. In this paper, we study a tractable “adversarial” coupon-collector model wherein proxies change after a random period of time from the latest request, i.e., asynchronously. In addition to determining the stationary mean number of proxies discovered by the attacker, we study the age of a proxy (coupon type) when it has been identified (requested) by the botnet. This gives us the rate at which proxies change (cost to the defender) when the nominal client request load is relatively negligible.

Mobile ad hoc networks (MANETs) are self-configuring, dynamic networks in which nodes are free to move. These nodes are susceptible to various malicious attacks. In this paper, we propose a distributed trust-based security scheme to prevent multiple attacks such as Probe, Denial-of-Service (DoS), Vampire, User-to-Root (U2R) occurring simultaneously. We report above 95% accuracy in data transmission and reception by applying the proposed scheme. The simulation has been carried out using network simulator ns-2 in a AODV routing protocol environment. To the best of the authors' knowledge, this is the first work reporting a distributed trust-based prevention scheme for preventing multiple attacks. We also check the scalability of the technique using variable node densities in the network.

On cloud computing systems consisting of task queuing and resource allocations, it is essential but hard to model and evaluate the global performance. In most of the models, researchers use a stochastic process or several stochastic processes to describe a real system. However, due to the absence of theoretical conclusions of any arbitrary stochastic processes, they approximate the complicated model into simple processes that have mathematical results, such as Markov processes. Our purpose is to give a universal method to deal with common stochastic processes as long as the processes can be expressed in the form of transition matrix. To achieve our purpose, we firstly prove several theorems about the convergence of stochastic matrices to figure out what kind of matrix-defined systems has steady states. Furthermore, we propose two strategies for measuring the rate of convergence which reflects how fast the system would come to its steady state. Finally, we give a method for reducing a stochastic matrix into smaller ones, and perform some experiments to illustrate our strategies in practice.

Although Stylometry has been effectively used for Authorship Attribution, there is a growing number of methods being developed that allow authors to mask their identity [2, 13]. In this paper, we investigate the usage of non-traditional feature sets for Authorship Attribution. By using non-traditional feature sets, one may be able to reveal the identity of adversarial authors who are attempting to evade detection from Authorship Attribution systems that are based on more traditional feature sets. In addition, we demonstrate how GEFeS (Genetic & Evolutionary Feature Selection) can be used to evolve high-performance hybrid feature sets composed of two non-traditional feature sets for Authorship Attribution: LIWC (Linguistic Inquiry & Word Count) and Sentiment Analysis. These hybrids were able to reduce the Adversarial Effectiveness on a test set presented in [2] by approximately 33.4%.

This paper presents a sequence switching control (SSC) scheme for buck converters with a series-inductor auxiliary circuit, aiming at improving the load transient response. During an unloading transient, the series inductor is controlled as a small equivalent inductance so as to achieve a fast transient regulation. While in the steady state, the series inductor behaves as a large inductance to reduce the output current ripple. Furthermore, on the basis of the proposed variable inductance circuit, a SSC control scheme is proposed and implemented in a digital form. With the proposed control scheme the unloading transient event is divided into n+1 sub-periods, and in each sub-period, the capacitor-charge balance principle is used to determine the switching time sequence. Furthermore, its feasibility is validated in experiment with a 12V-3.3V low-voltage high-current synchronous buck converter. Experimental results demonstrate that the voltage overshoot of the proposed SSC scheme has improved more than 74% compared to that of the time-optimal control (TOC) scheme.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting a given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information (QoI) compared to a baseline scheme in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared to the baseline.

The heterogeneous SIS model for virus spread in any finite size graph characterizes the influence of factors of SIS model and could be analyzed by the extended N-Intertwined model introduced in [1]. We specifically focus on the heterogeneous virus spread in the star network in this paper. The epidemic threshold and the average meta-stable state fraction of infected nodes are derived for virus spread in the star network. Our results illustrate the effect of the factors of SIS model on the steady state infection.

Security of control systems have become a new and important field of research since malicious attacks on control systems indeed occurred including Stuxnet in 2011 and north eastern electrical grid black out in 2003. Attacks on sensors and/or actuators of control systems cause malfunction, instability, and even system destruction. The impact of attack may differ by which instrumentation (sensors and/or actuators) is being attacked. In particular, for control systems with multiple sensors, attack on each sensor may have different impact, i.e., attack on some sensors leads to a greater damage to the system than those for other sensors. To investigate this, we consider sensor bias injection attacks in linear control systems equipped with anomaly detector, and quantify the maximum impact of attack on sensors while the attack remains undetected. Then, we introduce a notion of sensor security index for linear dynamic systems to quantify the vulnerability under sensor attacks. Method of reducing system vulnerability is also discussed using the notion of sensor security index.

In this paper, we study the security and system congestion in a risk-based checkpoint screening system with two kinds of inspection queues, named as Selectee Lanes and Normal Lanes. Based on the assessed threat value, the arrival crossing the security checkpoints is classified as either a selectee or a non-selectee. The Selectee Lanes with enhanced scrutiny are used to check selectees, while Normal Lanes are used to check non-selectees. The goal of the proposed modelling framework is to minimize the system congestion under the constraints of total security and limited budget. The system congestion of the checkpoint screening system is determined through a steady-state analysis of multi-server queueing models. By solving an optimization model, we can determine the optimal threshold for differentiating the arrivals, and determine the optimal number of security devices for each type of inspection queues. The analysis conducted in this study contributes managerial insights for understanding the operation and system performance of such risk-based checkpoint screening systems.