Biblio

The Internet of Things (IoT) is an evolving network of billions of interconnected physical objects, such as, numerous sensors, smartphones, wearables, and embedded devices. These physical objects, generally referred to as the smart objects, when deployed in real-world aggregates useful information from their surrounding environment. As-of-late, this notion of IoT has been extended to incorporate the social networking facets which have led to the promising paradigm of the `Social Internet of Things' (SIoT). In SIoT, the devices operate as an autonomous agent and provide an exchange of information and services discovery in an intelligent manner by establishing social relationships among them with respect to their owners. Trust plays an important role in establishing trustworthy relationships among the physical objects and reduces probable risks in the decision making process. In this paper, a trust computational model is proposed to extract individual trust features in a SIoT environment. Furthermore, a machine learning-based heuristic is used to aggregate all the trust features in order to ascertain an aggregate trust score. Simulation results illustrate that the proposed trust-based model isolates the trustworthy and untrustworthy nodes within the network in an efficient manner.

We present a privacy-preserving distributed reinforcement learning-based control scheme to address the problem of frequency control and economic dispatch in power generation systems. The proposed control approach requires neither a priori system model knowledge nor the mathematical formulation of the generation cost functions. Due to not requiring the generation cost models, the control scheme is capable of dealing with scenarios in which the cost functions are hard to formulate and/or non-convex. Furthermore, it is privacy-preserving, i.e. none of the units in the network needs to communicate its cost function and/or control policy to its neighbors. To realize this, we propose an actor-critic algorithm with function approximation in which the actor step is performed individually by each unit with no need to infer the policies of others. Moreover, in the critic step each generation unit shares its estimate of the local measurements and the estimate of its cost function with the neighbors, and via performing a consensus algorithm, a consensual estimate is achieved. The performance of our proposed control scheme, in terms of minimizing the overall cost while persistently fulfilling the demand and fast reaction and convergence of our distributed algorithm, is demonstrated on a benchmark case study.

Most of today's infrastructure systems can be efficiently operated thanks to the intelligent power supply of the smart grids. However, smart grids are highly vulnerable to malicious attacks, that is, because of the interplay between the components in the smart grids, the failure of some critical components may result in the cascading failure and breakdown of the whole system. Therefore, the question of how to identify the most critical components to protect the smart grid system is the first challenge to operators. To enable the system's robustness, there has been a lot of effort aimed at the system analysis, designing new architectures, and proposing new algorithms. However, these works mainly introduce different ranking methods for link (transmission line) or node (station) identification and directly select most the highest degree nodes or common links as the critical ones. These methods fail to address the problem of interdependencies between components nor consider the role of users that is one of critical factors impacting on the smart grid vulnerability assessment. This motivates us to study a more general and practical problem in terms of smart grid vulnerability assessment, namely the Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. The objective of this research is to provide an efficient method to identify critical components in the system by considering a realistic attack scenario.

When modeling information security systems (ISS), the vast majority of works offer various models of threats to the object of protection (threat trees, Petri nets, etc.). However, ISS is not only a mean to prevent threats or reduce damage from their implementation, but also other components - the qualifications of employees responsible for IS, the internal climate in the team, the company's position on the market, and many others. The article considers the cognitive model of the state of the information security system of an average organization. The model is a weighted oriented graph, its' vertices are standard elements of the organization's information security system. The most significant factors affecting the condition of information security of the organization are identified based on the model. Influencing these factors is providing the most effect if IS level.

Increased penetration of distributed energy resources (DERs) creates challenges in formulating the security constrained optimal power flow (SCOPF) problem as the number of models for these resources proliferate. Specifically, the number of devices with different mathematical models is large and their integration into the SCOPF becomes tedious. Henceforth, a process that seamlessly models and integrates such new devices into the SCOPF problem is needed. We propose an object-oriented modeling approach that leads to the autonomous formation of the SCOPF problem. All device models in the system are cast into a universal syntax. We have also introduced a quadratization method which makes the models consisting of linear and quadratic equations, if nonlinear. We refer to this model as the State and Control Quadratized Device Model (SCQDM). The SCQDM includes a number of equations and a number of inequalities expressing the operating limits of the device. The SCOPF problem is then formed in a seamless manner by operating only on the SCQDM device objects. The SCOPF problem, formed this way, is also quadratic (i.e. consists of linear and quadratic equations), and of the same form and syntax as the SCQDM for an individual device. For this reason, we named it security constrained quadratic optimal power flow (SCQOPF). We solve the SCQOPF problem using a sequential linear programming (SLP) algorithm and compare the results with those obtained from the commercial solver Knitro on the IEEE 57 bus system.

The paper is devoted to the development and research of the redundancy cyber resiliency technique based on fast rerouting under security metric with the implementation of the basic schemes for network elements protection, namely node, link, path, and bandwidth. Within the model, the secure fast rerouting task is formulated as an optimization problem of nonlinear programming. The model is configured in order to calculate primary and backup paths that contain links with the minimum values of the probability of compromise that is achieved by using the appropriate weights in the objective function, the value of which is minimized. Numerical research has been conducted, results of which proved the proposed model efficiency and adequacy for the practical application.

This paper compares the known method of Analytical Design of Aggregated Regulators (ADAR) with the method of Analytical Design of Optimal Regulators (ADOR). Both equivalence of these methods and the significant difference in the approaches to the analytical synthesis of control laws are shown. It is shown that the ADAR method has significant advantages associated with a simpler and analytical procedure of design of nonlinear laws for optimal control, clear physical representation of weighting factors of optimality criteria, validity and unambiguity of selecting regulator setting parameters, more simple approach to the analysis of the closed-loop system asymptotic stability. These advantages are illustrated by the examples of synthesis.

This paper describes the development of a module for calculating security zones in the cloud service of APCS modeling. A mathematical model based on graph theory is used. This allows you to describe access relationships between objects and security policy subjects. A comparative analysis of algorithms for traversing graph vertices is performed in order to select a suitable method for allocating security zones. The implemented algorithm for calculating security zones was added to the cloud service omole.ws.

Security preservation is considered as one of the major concerns in this digital world, mainly for performing any online transactions. As the time progress, it witnesses an enormous amount of security threats and stealing different kind of digital information over the online network. In this regard, lots of cryptographic algorithms based on secret key generation techniques have been implemented to boost up the security aspect of network systems that preserve the confidentiality of digital information. Despite this, intelligent intruders are still able to crack the key generation technique, thus stealing the data. In this research article, we propose an innovative approach for generating a pseudo-pseudo-random key sequence that serves as a base for the encryption/decryption process. The key generation process is carried out by extracting the essential features from a facial image and based on the extracted features; a pseudo-random key sequence that acts as a primary entity for the efficient encryption/decryption process is generated. Experimental findings related to the pseudo-random key is validated through chi-square, runs up-down and performs a period of subsequence test. Outcomes of these have subsequently passed in achieving an ideal key.

The following topics are dealt with: authorisation; data privacy; mobile computing; security of data; cryptography; Internet of Things; message authentication; invasive software; Android (operating system); vectors.

Two-factor authentication (2FA) systems implement by verifying at least two factors. A factor is something a user knows (password, or phrase), something a user possesses (smart card, or smartphone), something a user is (fingerprint, or iris), something a user does (keystroke), or somewhere a user is (location). In the existing 2FA system, a user is required to act in order to implement the second layer of authentication which is not very user-friendly. Smart devices (phones, laptops, tablets, etc.) can receive signals from different radio frequency technologies within range. As these devices move among networks (Wi-Fi access points, cellphone towers, etc.), they receive broadcast messages, some of which can be used to collect information. This information can be utilized in a variety of ways, such as establishing a connection, sharing information, locating devices, and, most appropriately, identifying users in range. The principal benefit of broadcast messages is that the devices can read and process the embedded information without being connected to the broadcaster. Moreover, the broadcast messages can be received only within range of the wireless access point sending the broadcast, thus inherently limiting access to those devices in close physical proximity and facilitating many applications dependent on that proximity. In the proposed research, a new factor is used - something that is in the user's environment with minimal user involvement. Data from these broadcast messages is utilized to implement a 2FA scheme by determining whether two devices are proximate or not to ensure that they belong to the same user.

With the concept of “wireless city”, 3G, WIFI and other wireless network coverages have become more extensive. Data transmission rate has achieved a qualitative leap, providing feasibility for the implementation of mobile video surveillance solutions. The mobile video monitoring system based on the bee population optimization algorithm proposed in this paper makes up for the defects of traditional network video surveillance, and according to the video surveillance system monitoring command, the optimal visual effect of the current state of the observed object can be rendered quickly and steadily through the optimization of the camera linkage model and simulation analysis.

Connected vehicles as a promising concept of Intelligent Transportation System (ITS), are a potential solution to address some of the existing challenges of emission, traffic congestion as well as fuel consumption. To achieve these goals, connectivity among vehicles through the wireless communication network is essential. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with purposes of disrupting the performance of the connected vehicles, lead to catastrophic collision and traffic congestion. In this study, we consider a platoon of connected vehicles equipped with Cooperative Adaptive Cruise Control (CACC) which are subjected to a specific type of cyber-attack namely "False Data Injection" attack. We developed a novel method to model the attack with ghost vehicles injected into the connected vehicles network to disrupt the performance of the whole system. To aid the analysis, we use a Partial Differential Equation (PDE) model. Furthermore, we present a PDE model-based diagnostics scheme capable of detecting the false data injection attack and isolating the injection point of the attack in the platoon system. The proposed scheme is designed based on a PDE observer with measured velocity and acceleration feedback. Lyapunov stability theory has been utilized to verify the analytically convergence of the observer under no attack scenario. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation study.

The advent of Internet and recent technological developments have paved the way for IoT devices in different sectors. The demand for real-time response led to the development of fog computing which is now a popular computing technique. It provides processing, computing and storage at the network edge for latency-sensitive applications such as banking transactions, healthcare etc. This has further led to the pool of user's sensitive data across the web that needs to be secured. In order to find an efficient security solution, it is mandatory to prioritize amongst different fog-level security factors. The authors have therefore, adopted a fuzzy-based Analytical Hierarchy Approach (AHP) for ranking the security attributes in fog-driven IoT environment. The results have also been compared to the ones obtained from classical-AHP and are found to be correlated.

Battery energy storage systems are facing risks of unreliable battery sensor data which might be caused by sensor faults in an embedded battery management system, communication failures, and even cyber-attacks. It is crucial to evaluate the trustworthiness of battery sensor data since inaccurate sensor data could lead to not only serious damages to battery energy storage systems, but also threaten the overall reliability of their applications (e.g., electric vehicles or power grids). This paper introduces a battery sensor data trust framework enabling detecting unreliable data using a deep learning algorithm. The proposed sensor data trust mechanism could potentially improve safety and reliability of the battery energy storage systems. The proposed deep learning-based battery sensor fault detection algorithm is validated by simulation studies using a convolutional neural network.

As a key technology in cognitive radio, cooperative spectrum sensing has been paid more and more attention. In cooperative spectrum sensing, multi-user cooperative spectrum sensing can effectively alleviate the performance degradation caused by multipath effect and shadow fading, and improve the spectrum utilization. However, as there may be malicious users in the cooperative sensing users, sending forged false messages to the fusion center or neighbor nodes to mislead them to make wrong judgments, which will greatly reduce the spectrum utilization. To solve this problem, this paper proposes an intelligent anti spectrum sensing data falsification (SSDF) attack algorithm using trust-based non consensus message passing algorithm. In this scheme, only one perception is needed, and the historical propagation path of each message is taken as the basis to calculate the reputation of each cognitive user. Every time a node receives different messages from the same cognitive user, there must be malicious users in its propagation path. We reward the nodes that appear more times in different paths with reputation value, and punish the nodes that appear less. Finally, the real value of the tampered message is restored according to the calculated reputation value. The MATLAB results show that the proposed scheme has a high recovery rate for messages and can identify malicious users in the network at the same time.

The blockchain is a storage technology and transmission of information, transparent, secure, and operating without central control. In this paper, we propose a new decentralized trust management and cooperation model where data is shared via blockchain and we explore the revenue distribution under different consensus schemes. To reduce the power calculation with respect to the control mechanism, our proposal adopts the possibility of Proof on Trust (PoT) and Proof of proof-of-stake based trust to replace the proof of work (PoW) scheme, to carry out the mining and storage of new data blocks. To detect nodes with malicious behavior to provide false system information, the trust updating algorithm is proposed..

The paper discusses the features of the automated process control system, defines the algorithm for installing critical updates. The main problems in the administration of a critical system have been identified. The paper presents a model for recognizing anomalies in the network traffic of an industrial information system using machine learning methods. The article considers the network intrusion dataset (raw TCP / IP dump data was collected, where the network was subjected to multiple attacks). The main parameters that affect the recognition of abnormal behavior in the system are determined. The basic mathematical models of classification are analyzed, their basic parameters are reviewed and tuned. The mathematical model was trained on the considered (randomly mixed) sample using cross-validation and the response was predicted on the control (test) sample, where the model should determine the anomalous behavior of the system or normal as the output. The main criteria for choosing a mathematical model for the problem to be solved were the number of correctly recognized (accuracy) anomalies, precision and recall of the answers. Based on the study, the optimal algorithm for recognizing anomalies was selected, as well as signs by which this anomaly can be recognized.

It has been recognized that artificial intelligence (AI) will play an important role in future societies. AI has already been incorporated in many industries to improve business processes and automation. Although the aviation industry has successfully implemented flight management systems or autopilot to automate flight operations, it is expected that full embracement of AI remains a challenge. Given the rigorous validation process and the requirements for the highest level of safety standards and risk management, AI needs to prove itself being safe to operate. This paper addresses the safety issues of AI deployment in an aviation network compatible with the Future Communication Infrastructure that utilizes heterogeneous wireless access technologies for communications between the aircraft and the ground networks. It further considers the exploitation of software defined networking (SDN) technologies in the ground network while the adoption of SDN in the airborne network can be optional. Due to the nature of centralized management in SDN-based network, the SDN controller can become a single point of failure or a target for cyber attacks. To countermeasure such attacks, an intrusion detection system utilises AI techniques, more specifically deep neural network (DNN), is considered. However, an adversary can target the AI-based intrusion detection system. This paper examines the impact of AI security attacks on the performance of the DNN algorithm. Poisoning attacks targeting the DSL-KDD datasets which were used to train the DNN algorithm were launched at the intrusion detection system. Results showed that the performance of the DNN algorithm has been significantly degraded in terms of the mean square error, accuracy rate, precision rate and the recall rate.

The paper deals with the modelling and simulation of a Fast Discharge Unit (FDU) for quench protection of the Toroidal Field (TF) magnets of the Divertor Tokamak Test, an experimental facility under design and construction in Frascati (Italy). The FDU is a safety key component that protects the superconducting magnets when a quench is detected through the fast extraction of the energy stored in superconducting magnets by adding in the TF magnets a dump (or discharge) resistor. In the paper, two different configurations of dump resistors (fixed and variable respectively) have been analysed and discussed. As a first result, it is possible to underline that the configuration with variable dump resistor is more efficient than the one with a fixed dump resistor.

Cyber-Physical Systems (CPS) use computational resources to control physical processes and provide critical services. For this reason, an attack in these systems may have dangerous consequences in the physical world. Hence, cyber- resilience is a fundamental property to ensure the safety of the people, the environment and the controlled physical processes. In this paper, we present metrics to quantify the cyber-resilience level based on the design, structure, stability, and performance under the attack of a given CPS. The metrics provide reference points to evaluate whether the system is better prepared or not to face the adversaries. This way, it is possible to quantify the ability to recover from an adversary using its mathematical model based on actuators saturation. Finally, we validate our approach using a numeric simulation on the Tennessee Eastman control challenge problem.

The increasing development of smart grid facilitates that modern power grids inter-connect with each other and form a large power system, making it possible and advantageous to conduct coordinated power flow among several grids. The communication burden and privacy issue are the prominent challenges in the application of synchronous iteration power flow method. In this paper, a synchronous iterative method of power flow in inter-connected power grid considering privacy preservation is proposed. By establishing the masked model of power flow for each sub-grid, the synchronous iteration is conducted by gathering the masked model of sub-grids in the coordination center and solving the masked correction equation in a concentration manner at each step. Generally, the proposed method can concentrate the major calculation of power flow on the coordination center, reduce the communication burden and guarantee the privacy preservation of sub-grids. A case study on IEEE 118-bus test system demonstrate the feasibility and effectiveness of the proposed methodology.

The increased power capacity and networking requirements in Extremely Fast Charging (XFC) systems for battery electric vehicles (BEVs) and the resulting increase in the adversarial attack surface call for security measures to be taken in the involved cyber-physical system (CPS). Within this system, the security of the BEV's battery management system (BMS) is of critical importance as the BMS is the first line of defense between the vehicle and the charge station. This study proposes an optimal control and moving-target defense (MTD) based novel approach for the security of the vehicle BMS) focusing on the charging process, during which a compromised vehicle may contaminate the XFC station and the whole grid. This paper is part of our ongoing research, which is one of the few, if not the first, reported studies in the literature on security-hardened BMS, aiming to increase the security and performance of operations between the charging station, the BMS and the battery system of electric vehicles. The developed MTD based switching strategy makes use of redundancies in the controller and feedback design. The performed simulations demonstrate an increased unpredictability and acceptable charging performance under adversarial attacks.

The DES is a symmetric cryptosystem which encrypts data in blocks of 64 bits using 48 bit keys in 16 rounds. It comprises a key schedule, encryption and decryption components. The key schedule, in particular, uses three static component units, the PC-1, PC-2 and rotation tables. However, can these three static components of the key schedule be altered? The DES development team never explained most of these component units. Understanding the DES key schedule is, thus, hard. In addition, reproducing the DES model with unknown component units is challenging, making it hard to adapt and bring implementation of the DES model closer to novice developers' context. We propose an alternative approach for re-implementing the DES key schedule using, rather, dynamic instead of static tables. We investigate the design features of the DES key schedule and implement the same. We then propose a re-engineering view towards a more white-box design. Precisely, generation of the PC-1, rotation and PC-2 tables is revisited to random dynamic tables created at run time. In our views, randomly generated component units eliminate the feared concerns regarding perpetrators' possible knowledge of the internal structures of the static component units. Comparison of the performances of the hybrid DES key schedule to that of the original DES key schedule shows closely related outcomes, connoting the hybrid version as a good alternative to the original model. Memory usage and CPU time were measured. The hybrid insignificantly out-performs the original DES key schedule. This outcome may inspire further researches on possible alterations to other DES component units as well, bringing about completely white-box designs to the DES model.

False data injection attack (FDIA) destroys the automatic generation control (AGC) system and leads to unstable operation of the power system. Fast and accurate detection can help prevent and disrupt malicious attacks. This paper proposes an improved detection method, which is combined with fuzzy theory and support vector machine (SVM) to identify various types of attacks. The impacts of different types of FDIAs on the AGC system are analyzed, and the reliability of the method is proved by a large number of experimental data. This experiment is simulated on a single-area LFC system and the effects of adding a wind storage system were compared in a dynamic model. Simulation studies also show a higher accuracy of fuzzy support vector machine (FSVM) than traditional SVM and fuzzy pattern trees (FPTs).