Biblio

A new framework is presented in this paper for proving coding theorems for linear codes, where the systematic bits and the corresponding parity-check bits play different roles. Precisely, the noisy systematic bits are used to limit the list size of typical codewords, while the noisy parity-check bits are used to select from the list the maximum likelihood codeword. This new framework for linear codes allows that the systematic bits and the parity-check bits are transmitted in different ways and over different channels. In particular, this new framework unifies the source coding theorems and the channel coding theorems. With this framework, we prove that the Bernoulli generator matrix codes (BGMCs) are capacity-achieving over binary-input output symmetric (BIOS) channels and also entropy-achieving for Bernoulli sources.

This paper investigates the event-triggered control (ETC) problem for stochastic networked control systems (NCSs) with exogenous disturbances and Denial-of-Service (DoS) attacks. The ETC strategy is proposed to reduce the utilization of network resource while defending the DoS attacks. Based on the introduced ETC strategy, sufficient conditions, which rely on the frequency and duration properties of DoS attacks, are obtained to achieve the stochastic input-to-state stability and Zeno-freeness of the ETC stochastic NCSs. An example of air vehicle system is given to explain the effectiveness of proposed ETC strategy.

With the advancement of communication technology, the interoperability of the power grid operation has improved significantly, but due to its dependence on the communication system, it is extremely vulnerable to network attacks. Among them, the false data injection attack utilizes the loophole of bad data detection in the system and attacks the state estimation system, resulting in frequent occurrence of abnormal data in the system, which brings great harm to the power grid. In view of the fact that false data injection attacks are easy to avoid traditional bad data detection methods, this paper analyzes the different situations of false data injection attacks based on the characteristics of the power grid. Firstly, it proposes to apply the distortion index method to false data injection attack detection. Experiments prove that the detection results are good and can be complementary to traditional detection methods. Then, combined with the traditional normalized residual method, this paper proposes the improved distortion index method based on the distortion index, which is good at detecting abnormal data. The use of improved distortion index method to detect false data injection attacks can make up for the defect of the lack of universality of traditional detection methods, and meet the requirements of anomaly detection efficiency. Finally, based on the MATLAB power simulation test system, experimental simulation is carried out to verify the effectiveness and universality of the proposed method for false data injection attack detection.

Statistical structure learning (SSL)-based approaches have been employed in the recent years to detect different types of anomalies in a variety of cyber-physical systems (CPS). Although these approaches outperform conventional methods in the literature, their computational complexity, need for large number of measurements and centralized computations have limited their applicability to large-scale networks. In this work, we propose a distributed, multi-agent maximum likelihood (ML) approach to detect anomalies in smart grid applications aiming at reducing computational complexity, as well as preserving data privacy among different players in the network. The proposed multi-agent detector breaks the original ML problem into several local (smaller) ML optimization problems coupled by the alternating direction method of multipliers (ADMM). Then, these local ML problems are solved by their corresponding agents, eventually resulting in the construction of the global solution (network's information matrix). The numerical results obtained from two IEEE test (power transmission) systems confirm the accuracy and efficiency of the proposed approach for anomaly detection.

Sybils are users created for carrying out nefarious actions in online social networks (OSNs) and threaten the security of OSNs. Therefore, Sybil detection is an urgent security task, and various detection methods have been proposed. Existing Sybil detection methods are based on the relationship (i.e., graph structure) of users in OSNs. Structure-based methods can be classified into two categories: Random Walk (RW)-based and Belief Propagation (BP)-based. However, although almost all methods have been experimentally evaluated in terms of their performance and robustness to noise, the theoretical understanding of them is insufficient. In this paper, we interpret the Sybil detection problem from the viewpoint of graph signal processing and provide a framework to formulate RW- and BPbased methods as low-pass filtering. This framework enables us to theoretically compare RW- and BP-based methods and explain why BP-based methods perform well for scale-free graphs, unlike RW-based methods. Furthermore, by this framework, we relate RW- and BP-based methods and Graph Neural Networks (GNNs) and discuss the difference among these methods. Finally, we evaluate the validity of this framework through numerical experiments.

The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).

Fully homomorphic encryption (FHE) was one of the most prominent research topics of the last ten years. And it is considered as a major cryptographic tool in a secure and reliable cloud computing environment. The reason behind that because it allows computations over encrypted data, without decrypting the original message. This paper developed a new symmetric (FHE) algorithm based on Enhanced Matrix Operation for Randomization and Encryption (EMORE) algorithm using a chaotic system. The proposed algorithm was considered a noise-free algorithm. It generates the ciphertext in a floating-point number's format, overcomes the problem of plaintext ring and modular arithmetic operation in EMORE by the hardness of a chaotic system, and provides another level of security in terms of randomness properties, sensitivity to the initial condition, and large key size (\textbackslashtextgreater2100) of a chaotic system. Besides that, the proposed algorithm provides the confidentiality and privacy of outsourced data computing through homomorphism property of it. By using both numerical and statistical tests, these tests proved that the proposed algorithm has positive randomness properties and provide secure and reliable encryption (through encryption-decryption time, key sensitivity, keyspace, and correlation). Finally, the simulation results show that the execution time of the proposed algorithm is faster about 7.85 times than the EMORE algorithm.

In this paper, we propose a compositional scheme for the construction of abstractions for networks of control systems by using the interconnection matrix and joint dissipativity-type properties of subsystems and their abstractions. In the proposed framework, the abstraction, itself a control system (possibly with a lower dimension), can be used as a substitution of the original system in the controller design process. Moreover, we provide a procedure for constructing abstractions of a class of nonlinear control systems by using the bounds on the slope of system nonlinearities. We illustrate the proposed results on a network of linear control systems by constructing its abstraction in a compositional way without requiring any condition on the number or gains of the subsystems. We use the abstraction as a substitute to synthesize a controller enforcing a certain linear temporal logic specification. This example particularly elucidates the effectiveness of dissipativity-type compositional reasoning for large-scale systems.

This paper studies the sensor placement problem in a networked control system for improving its security against cyber-physical attacks. The problem is formulated as a zero-sum game between an attacker and a detector. The attacker's decision is to select f nodes of the network to attack whereas the detector's decision is to place f sensors to detect the presence of the attack signals. In our formulation, the attacker minimizes its visibility, defined as the system L2 gain from the attack signals to the deployed sensors' outputs, and the detector maximizes the visibility of the attack signals. The equilibrium strategy of the game determines the optimal locations of the sensors. The existence of Nash equilibrium for the attacker-detector game is studied when the underlying connectivity graph is a directed or an undirected tree. When the game does not admit a Nash equilibrium, it is shown that the Stackelberg equilibrium of the game, with the detector as the game leader, can be computed efficiently. Our results show that, under the optimal sensor placement strategy, an undirected topology provides a higher security level for a networked control system compared with its corresponding directed topology.

This paper focuses on the issue of designing L2 state feedback controller for networked control systems subject to unknown periodic denial-of-service (DoS) jamming attacks. Primarily, a resilient event-triggering mechanism is introduced to counteract the influence of DoS jamming attacks. Secondly, a switching system model of NCSs is set up. Then, the criteria of the exponential stability analysis is obtained by the piecewise Lyapunov functional approach based on the model. Thirdly, a co-design approach of the trigger parameters and L2 controller is developed. Lastly, a practical system is used for proving the efficiency of the proposed approach.

Internet of Things (IoT) is an advanced automation technology and analytics systems which connected physical objects that have access through the Internet and have their unique flexibility and an ability to be suitable for any environment. There are some critical applications like smart health care system, in which the data collection, sharing and routing through IoT has to be handled in sensitive way. The IPv6 Routing Protocol for LL(Low-power and Lossy) networks (RPL) is the routing protocols to ensure reliable data transfer in 6LOWPAN networks. However, RPL is vulnerable to number of security attacks which creates a major impact on energy consumption and memory requirements which is not suitable for energy constraint networks like IoT. This requires secured RPL protocol to be used for critical data transfer. This paper introduces a novel approach of combining a lightweight LBS (Location Based Service) authentication and Attribute Based Trust Evaluation (ABTE). The algorithm has been implemented for smart health care system and analyzed how its perform in the RPL protocol for IoT constrained environments.

Self-assembled semiconductor quantum dots possess an intrinsic geometric symmetry due to the crystal periodic structure. In order to systematically analyze the symmetric properties of quantum dots' bound states resulting only from geometric confinement, we apply group representation theory. We label each bound state for two kinds of popular quantum dot shapes: pyramid and half ellipsoid with the irreducible representation of the corresponding symmetric groups, i.e., C4v and C2v, respectively. Our study completes all the possible irreducible representation cases of groups C4v and C2v. Using the character theory of point groups, we predict the selection rule for electric dipole induced transitions. We also investigate the impact of quantum dot aspect ratio on the symmetric properties of the state wavefunction. This research provides a solid foundation to continue exploring quantum dot symmetry reduction or broken phenomena because of strain, band-mixing and shape irregularity. The results will benefit the researchers who are interested in quantum dot symmetry related effects such as absorption or emission spectra, or those who are studying quantum dots using analytical or numerical simulation approaches.

In Vehicular networks, privacy, especially the vehicles' location privacy is highly concerned. Several pseudonymous based privacy protection mechanisms have been established and standardized in the past few years by IEEE and ETSI. However, vehicular networks are still vulnerable to Sybil attack. In this paper, a Sybil attack detection method based on k-Nearest Neighbours (kNN) classification algorithm is proposed. In this method, vehicles are classified based on the similarity in their driving patterns. Furthermore, the kNN methods' high runtime complexity issue is also optimized. The simulation results show that our detection method can reach a high detection rate while keeping error rate low.

This paper deals with the robust H∞ cyber-attacks estimation problem for control systems under stochastic cyber-attacks and disturbances. The focus is on designing a H∞ filter which maximize the attack sensitivity and minimize the effect of disturbances. The design requires not only the disturbance attenuation, but also the residual to remain the attack sensitivity as much as possible while the effect of disturbance is minimized. A stochastic model of control system with stochastic cyber-attacks which satisfy the Markovian stochastic process is constructed. And we also present the stochastic attack models that a control system is possibly exposed to. Furthermore, applying H∞ filtering technique-based on linear matrix inequalities (LMIs), the paper obtains sufficient conditions that ensure the filtering error dynamic is asymptotically stable and satisfies a prescribed ratio between cyber-attack sensitivity and disturbance sensitivity. Finally, the results are applied to the control of a Quadruple-tank process (QTP) under a stochastic cyber-attack and a stochastic disturbance. The simulation results underline that the designed filters is effective and feasible in practical application.