Biblio

This paper addresses the allocation method of offensive resources for man-made attacks on power systems considering extreme weather conditions, which can help the defender identify the most vulnerable components to protect in this adverse situation. The problem is formulated as an attacker-defender model. The attacker at the upper level intends to maximize the expected damage considering all possible line failure scenarios. These scenarios are characterized by the combinations of failed transmission lines under extreme weather. Once the disruption is detected, the defender at the lower level alters the generation and consumption in the power grid using DC optimal power flow technique to minimize the damage. Then the original bi-level problem is transformed into an equivalent single-level mixed-integer linear program through strong duality theorem and Big-M method. The proposed attack resource allocation method is applied on IEEE 39-bus system and its effectiveness is demonstrated by the comparative case studies.

This paper is concered with the nonlinear cyber physical system (CPS) with uncertain parameters under false data injection (FDI) attacks. The interval type-2 (IT2) fuzzy model is utilized to approximate the nonlinear system, then the nonlinear system can be represented as a convex combination of linear systems. To detect the FDI attacks, a novel robust fuzzy extended state observer with H∞ preformance is proposed, where the fuzzy rules are utilized to the observer to estimate the FDI attacks. Utilizing the observation of the FDI attacks, a security control scheme is proposed in this paper, in which a compensator is designed to offset the FDI attacks. Simulation examples are given to illustrate the effecitveness of the proposed security scheme.

In this paper, a sliding mode control (SMC) based on nonlinear disturbance observer and intermittent control is proposed to maximize the security of cyber-physical systems (CPSs), aiming at the cyber-attacks and physical uncertainties of cyber-physical systems. In the CPSs, the transmission of information data and control signals to the remote end through the network may lead to cyber attacks, and there will be uncertainties in the physical system. Therefore, this paper establishes a CPSs model that includes network attacks and physical uncertainties. Secondly, according to the analysis of the mathematical model, an adaptive SMC based on disturbance observer and intermittent control is designed to keep the CPSs stable in the presence of network attacks and physical uncertainties. In this strategy, the adaptive strategy suppresses the controller The chattering of the output. Intermittent control breaks the limitations of traditional continuous control to ensure efficient use of resources. Finally, to prove the control performance of the controller, numerical simulation results are given.

The (IoT) paradigm’s fundamental goal is to massively connect the “smart things” through standardized interfaces, providing a variety of smart services. Cyber-Physical Systems (CPS) include both physical and cyber components and can apply to various application domains (smart grid, smart transportation, smart manufacturing, etc.). The Digital Twin (DT) is a cyber clone of physical objects (things), which will be an essential component in CPS. This paper designs a systematic taxonomy to explore different attacks on DT-based CPS and how they affect the system from a four-layer architecture perspective. We present an attack space for DT-based CPS on four layers (i.e., object layer, communication layer, DT layer, and application layer), three attack objects (i.e., confidentiality, integrity, and availability), and attack types combined with strength and knowledge. Furthermore, some selected case studies are conducted to examine attacks on representative DT-based CPS (smart grid, smart transportation, and smart manufacturing). Finally, we propose a defense mechanism called Secured DT Development Life Cycle (SDTDLC) and point out the importance of leveraging other enabling techniques (intrusion detection, blockchain, modeling, simulation, and emulation) to secure DT-based CPS.

In this paper, a novel method is proposed to assess the power system resilience considering the impacts of hurricanes. Firstly, the transmission line outage model correlated to wind speed is developed. Then, Probability Load Flow (PLF) considering the random outage of lines and the variation of loads is designed, and Latin Hypercube Sampling (LHS) is used to improve the efficiency of Monte Carlo Simulation (MCS) in solving PLF. Moreover, risk indices, including line overloading, node voltage exceeding limit, load shedding and system collapse, are established to assess the resilience of power systems during hurricanes. The method is tested with a modified IEEE 14-bus system, and simulation results indicate the effectiveness of the proposed approach.

One of the major concerns in the real-time monitoring systems in a smart grid is the Cyber security threat. The false data injection attack is emerging as a major form of attack in Cyber-Physical Systems (CPS). A False data Injection Attack (FDIA) can lead to severe issues like insufficient generation, physical damage to the grid, power flow imbalance as well as economical loss. The recent advancements in machine learning algorithms have helped solve the drawbacks of using classical detection techniques for such attacks. In this article, we propose to use Autoencoders (AE’s) as a novel Machine Learning approach to detect FDI attacks without any major modifications. The performance of the method is validated through the analysis of the simulation results. The algorithm achieves optimal accuracy owing to the unsupervised nature of the algorithm.

One major tool of Energy Management Systems for monitoring the status of the power grid is State Estimation (SE). Since the results of state estimation are used within the energy management system, the security of the power system state estimation tool is most important. The research in this area is targeting detection of False Data Injection attacks on measurements. Though this aspect is crucial, SE also depends on database that are used to describe the relationship between measurements and systems' states. This paper presents a two-stage optimization framework to not only detect, but also correct cyber-attacks pertaining the measurements' model parameters used by the SE routine. In the first stage, an estimate of the line parameters ratios are obtained. In the second stage, the estimated ratios from stage I are used in a Bi-Level model for obtaining a final estimate of the measurements' model parameters. Hence, the presented framework does not only unify the detection and correction in a single optimization run, but also provide a monitoring scheme for the SE database that is typically considered static. In addition, in the two stages, linear programming framework is preserved. For validation, the IEEE 118 bus system is used for implementation. The results illustrate the effectiveness of the proposed model for detecting attacks in the database used in the state estimation process.

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

This study aims to explore the security issues and computational intelligence of drone information system based on deep learning. Targeting at the security issues of the drone system when it is attacked, this study adopts the improved long short-term memory (LSTM) network to analyze the cyber physical system (CPS) data for prediction from the perspective of predicting the control signal data of the system before the attack occurs. At the same time, the differential privacy frequent subgraph (DPFS) is introduced to keep data privacy confidential, and the digital twins technology is used to map the operating environment of the drone in the physical space, and an attack prediction model for drone digital twins CPS is constructed based on differential privacy-improved LSTM. Finally, the tennessee eastman (TE) process is undertaken as a simulation platform to simulate the constructed model so as to verify its performance. In addition, the proposed model is compared with the Bidirectional LSTM (BiLSTM) and Attention-BiLSTM models proposed by other scholars. It was found that the root mean square error (RMSE) of the proposed model is the smallest (0.20) when the number of hidden layer nodes is 26. Comparison with the actual flow value shows that the proposed algorithm is more accurate with better fitting. Therefore, the constructed drone attack prediction model can achieve higher prediction accuracy and obvious better robustness under the premise of ensuring errors, which can provide experimental basis for the later security and intelligent development of drone system.

Cyber-Physical Systems (CPS) are complex systems of computational, physical, and human components integrated to achieve some function over one or more networks. The use of distributed simulation, or co-simulation, is one method often used to analyze the behavior and properties of these systems. High-Level Architecture (HLA) is an IEEE co-simulation standard that supports the development and orchestration of distributed simulations. However, a simple HLA federation constructed with the component simulations (i.e., federates) does not satisfy several requirements that arise in real-world use cases such as the shared use of limited physical and computational resources, the need to selectively hide information from participating federates, the creation of reusable federates and federations for supporting configurable shared services, achieving performant distributed simulations, organizing federations across different model types or application concerns, and coordinating federations across organizations with different information technology policies. This paper describes these core requirements that necessitate the use of multiple HLA federations and presents various mechanisms for constructing such integrated HLA federations. An example use case is implemented using a model-based rapid simulation integration framework called the Universal CPS Environment for Federation (UCEF) to illustrate these requirements and demonstrate techniques for integrating multiple HLA federations.

This paper deals with the implementation of robust control, based on the finite time Lyapunov stability theory, to solve the trajectory tracking problem of an unconventional quadrotor with rotating arms (also known as foldable drone). First, the model of this Unmanned Aerial Vehicle (UAV) taking into consideration the variation of the inertia, the Center of Gravity (CoG) and the control matrix is presented. The theoretical foundations of backstepping control enhanced by a Super-Twisting (ST) algorithm are then discussed. Numerical simulations are performed to demonstrate the effectiveness of the proposed control strategy. Finally, a qualitative and quantitative comparative study is made between the proposed controller and the classical backstepping controller. Overall, the results obtained show that the proposed control approach provides better performance in terms of accuracy and resilience.

Network attacks are becoming more intense and characterized by complexity and persistence. Mechanisms that ensure network resilience to faults and threats should be well provided. Different approaches have been proposed to network resilience; however, most of them rely on static policies, which is unsuitable for current complex network environments and real-time requirements. To address these issues, we present a Belief-Desire-Intention (BDI) based multi-agent resilience network controller coupled with blockchain. We first clarify the theory and platform of the BDI, then discuss how the BDI evaluates the network resilience. In addition, we present the architecture, workflow, and applications of the resilience network controller. Simulation results show that the resilience network controller can effectively detect and mitigate distributed denial of service attacks.

The resilience assessment of electric and gas networks gains importance due to increasing interdependencies caused by the coupling of gas-fired units. However, the gradually increasing scale of the integrated electricity and gas system (IEGS) poses a significant challenge to current assessment methods. The numerical analysis method is accurate but time-consuming, which may incur a significant computational cost in large-scale IEGS. Therefore, this paper proposes a resilience assessment method based on hetero-functional graph theory for IEGS to balance the accuracy with the computational complexity. In contrast to traditional graph theory, HFGT can effectively depict the coupled systems with inherent heterogeneity and can represent the structure of heterogeneous functional systems in a clear and unambiguous way. In addition, due to the advantages of modelling the system functionality, the effect of line-pack in the gas network on the system resilience is depicted more precisely in this paper. Simulation results on an IEGS with the IEEE 9-bus system and a 7-node gas system verify the effectiveness of the proposed method.

In this work, the security sliding mode control issue is studied for interval type-2 (IT2) fuzzy systems under the unreliable network. The deception attacks and the denial-of-service (DoS) attacks may occur in the sensor-controller channels to affect the transmission of the system state, and these attacks are described via two independent Bernoulli stochastic variables. By adopting the compensation strategy and utilizing the available state, the new membership functions are constructed to design the fuzzy controller with the different fuzzy rules from the fuzzy model. Then, under the mismatched membership function, the designed security controller can render the closed-loop IT2 fuzzy system to be stochastically stable and the sliding surface to be reachable. Finally, the simulation results verify the security control scheme.

While acquiring precise and intelligent state sensing and control capabilities, the cyber physical power system is constantly exposed to the potential cyber-attack threat. False data injection (FDI) attack attempts to disrupt the normal operation of the power system through the coupling of cyber side and physical side. To deal with the situation that stealthy FDI attack can bypass the bad data detection and thus trigger false commands, a system feature extraction method in state estimation is proposed, and the corresponding FDI attack detection method is presented. Based on the principles of state estimation and stealthy FDI attack, we analyze the impacts of FDI attack on measurement residual. Gaussian fitting method is used to extract the characteristic parameters of residual distribution as the system feature, and attack detection is implemented in a sliding time window by comparison. Simulation results prove that the proposed attack detection method is effectiveness and efficiency.

Information security construction is a social issue, and the most urgent task is to do an excellent job in information risk assessment. The bayesian neural network currently plays a vital role in enterprise information security risk assessment, which overcomes the subjective defects of traditional assessment results and operates efficiently. The risk quantification method based on fuzzy theory and Bayesian regularization BP neural network mainly uses fuzzy theory to process the original data and uses the processed data as the input value of the neural network, which can effectively reduce the ambiguity of language description. At the same time, special neural network training is carried out for the confusion that the neural network is easy to fall into the optimal local problem. Finally, the risk is verified and quantified through experimental simulation. This paper mainly discusses the problem of enterprise information security risk assessment based on a Bayesian neural network, hoping to provide strong technical support for enterprises and organizations to carry out risk rectification plans. Therefore, the above method provides a new information security risk assessment idea.

Cloud security has become a serious challenge due to increasing number of attacks day-by-day. Intrusion Detection System (IDS) requires an efficient security model for improving security in the cloud. This paper proposes a game theory based model, named as Game Theory Cloud Security Deep Neural Network (GT-CSDNN) for security in cloud. The proposed model works with the Deep Neural Network (DNN) for classification of attack and normal data. The performance of the proposed model is evaluated with CICIDS-2018 dataset. The dataset is normalized and optimal points about normal and attack data are evaluated based on the Improved Whale Algorithm (IWA). The simulation results show that the proposed model exhibits improved performance as compared with existing techniques in terms of accuracy, precision, F-score, area under the curve, False Positive Rate (FPR) and detection rate.

Compressive radar receiver has attracted a lot of research interest due to its capability to keep balance between sub-Nyquist sampling and high resolution. In evaluating the performance of compressive time delay estimator, Cramer-Rao bound (CRB) has been commonly utilized for lower bounding the mean square error (MSE). However, behaving as a local bound, CRB is not tight in the a priori performance region. In this paper, we introduce the Ziv-Zakai bound (ZZB) methodology into compressive sensing framework, and derive a deterministic ZZB for compressive time delay estimators as a function of the compressive sensing kernel. By effectively incorporating the a priori information of the unknown time delay, the derived ZZB performs much tighter than CRB especially in the a priori performance region. Simulation results demonstrate that the derived ZZB outperforms the Bayesian CRB over a wide range of signal-to-noise ratio, where different types of a priori distribution of time delay are considered.

The Compressive Sensing (CS) has wide range of applications in various domains. The sampling of sparse signal, which is periodic or aperiodic in nature, is still an out of focus topic. This paper proposes novel Sparse Spasmodic Sampling (SSS) techniques for different sparse signal in original domain. The SSS techniques are proposed to overcome the drawback of the existing CS sampling techniques, which can sample any sparse signal efficiently and also find location of non-zero components in signals. First, Sparse Spasmodic Sampling model-1 (SSS-1) which samples random points and also include non-zero components is proposed. Another sampling technique, Sparse Spasmodic Sampling model-2 (SSS-2) has the same working principle as model-1 with some advancements in design. It samples equi-distance points unlike SSS-1. It is demonstrated that, using any sampling technique, the signal is able to reconstruct with a reconstruction algorithm with a smaller number of measurements. Simulation results are provided to demonstrate the effectiveness of the proposed sampling techniques.

In this paper, we propose a new ordered statistics decoding (OSD) for linear block codes, which is referred to as local constraint-based OSD (LC-OSD). Distinguished from the conventional OSD, which chooses the most reliable basis (MRB) for re-encoding, the LC-OSD chooses an extended MRB on which local constraints are naturally imposed. A list of candidate codewords is then generated by performing a serial list Viterbi algorithm (SLVA) over the trellis specified with the local constraints. To terminate early the SLVA for complexity reduction, we present a simple criterion which monitors the ratio of the bound on the likelihood of the unexplored candidate codewords to the sum of the hard-decision vector’s likelihood and the up-to-date optimal candidate’s likelihood. Simulation results show that the LC-OSD can have a much less number of test patterns than that of the conventional OSD but cause negligible performance loss. Comparisons with other complexity-reduced OSDs are also conducted, showing the advantages of the LC-OSD in terms of complexity.

Cognitive Radio (CR) is an attractive solution in mobile communication for solving the spectrum scarcity problem. Moreover, security concerns are not yet fully satisfied. This article focuses on attacks such as the Primary user emulation attack (PUE) and the jammer attack. These attacks create anomalous spectrum access thereby disturbing the dynamic spectrum usage in the CR networks. A framework based on cross-layer has been designed effectively to determine these attacks in the CR networks. First, each secondary user will sense the spectrum in the physical layer and construct a feature space. Using the extracted features, the clusters are formed effectively for each user. In the network layer, multipath routing is employed to discover the routes for the secondary user. If the node in the path identifies any spectrum shortage, it will verify that location with the help of constructed cluster. If the node does not belong to any of the clusters, then it will be identified as the attacker node. Simulation results and security analysis are performed using the NS2 simulations, which show improvement in detection of the attacks, decrease in the detection delay, and less route dis-connectivity. The proposed cross-layer framework identifies the anomalous spectrum access attack effectively.

Cognitive Radio Network makes intelligent use of the spectrum resources. However, spectrum sensing is vulnerable to numerous harmful assaults. To lower the network's performance, hackers attempt to alter the sensed result. In the fusion centre, blockchain technology is used to make broad judgments on spectrum sensing in order to detect and thwart hostile activities. The sensed local results are hashed using the SHA 3 technique. This improves spectrum sensing precision and effectively thwarts harmful attacks. In comparison to other established techniques like equal gain combining, the simulation results demonstrate higher detection probability and sensing precision. Thus, employing Blockchain technology, cognitive radio network security can be significantly enhanced.

This paper investigates the physical layer security of a cognitive radio (CR) non-orthogonal multiple-access (NOMA) network supported by an intelligent reflecting surface (IRS). In a CR network, a secondary base station (BS) serves a couple of users, i.e., near and far users, via NOMA transmission under eavesdropping from a malicious attacker. It is assumed that the direct transmission link from the BS and far user is absent due to obstacles. Thus, an IRS is utilized to support far user communication, however, the communication links between the IRS and near/primary users are neglected because of heavy attenuation. The exact secrecy outage probability (SOP) for the near user and approximate SOP for the far user are then derived in closed-form by using the Gauss-Chebyshev approach. The accuracy of the derived analytical SOP is then verified through Monte Carlo simulations. The simulation results also provide useful insights on the impacts of the number of IRS reflecting elements and limited interference temperature on the system SOP.

Cognitive radio (CR) networks are an emerging and promising technology to improve the utilization of vacant bands. In CR networks, security is a very noteworthy domain. Two threatening attacks are primary user emulation (PUE) and spectrum sensing data falsification (SSDF). A PUE attacker mimics the primary user signals to deceive the legitimate secondary users. The SSDF attacker falsifies its observations to misguide the fusion center to make a wrong decision about the status of the primary user. In this paper, we propose a scheme based on clustering the secondary users to counter SSDF attacks. Our focus is on detecting and classifying each cluster as reliable or unreliable. We introduce two different methods using an artificial neural network (ANN) for both methods and five more classifiers such as support vector machine (SVM), random forest (RF), K-nearest neighbors (KNN), logistic regression (LR), and decision tree (DR) for the second one to achieve this goal. Moreover, we consider deterministic and stochastic scenarios with white Gaussian noise (WGN) for attack strategy. Results demonstrate that our method outperforms a recently suggested scheme.

CP-ABE (Ciphertext-policy attribute based encryption) is considered as a secure access control for data sharing. However, the SK(secret key) in most CP-ABE scheme is generated by Centralized authority(CA). It could lead to the high cost of building trust and single point of failure. Because of the characters of blockchain, some schemes based on blockchain have been proposed to prevent the disclosure and protect privacy of users' attribute. Thus, a new CP-ABE identity-attribute management(IAM) data sharing scheme is proposed based on blockchain, i.e. IAM-BDSS, to guarantee privacy through the hidden policy and attribute. Meanwhile, we define a transaction structure to ensure the auditability of parameter transmission on blockchain system. The experimental results and security analysis show that our IAM-BDSS is effective and feasible.