Biblio

From an information-theoretic standpoint, the intrusion detection process can be examined. Given the IDS output(alarm data), we should have less uncertainty regarding the input (event data). We propose the Capability of Intrusion Detection (CID) measure, which is simply the ratio of mutual information between IDS input and output, and the input of entropy. CID has the desirable properties of (1) naturally accounting for all important aspects of detection capability, such as true positive rate, false positive rate, positive predictive value, negative predictive value, and base rate, (2) objectively providing an intrinsic measure of intrusion detection capability, and (3) being sensitive to IDS operation parameters. When finetuning an IDS, we believe that CID is the best performance metric to use. In terms of the IDS’ inherent ability to classify input data, the so obtained operation point is the best that it can achieve.

This article is about a criterion based on credibility complex fuzzy set (CCFS) to study the prevailing substitution boxes (S-box) and learn their properties to find out their suitability in image encryption applications. Also these criterion has its own properties which is discussed in detailed and on the basis of these properties we have to find the best optimal results and decide the suitability of an S-box to image encryption applications. S-box is the only components which produces the confusion in the every block cipher in the formation of image encryption. So, for this first we have to convert the matrix having color image using the nonlinear components and also using the proposed algebraic structure of credibility complex fuzzy set to find the best S-box for image encryption based on its criterion. The analyses show that the readings of GRAY S-box is very good for image data.

In this paper, the distributed security control problem of connected vehicle systems (CVSs) is investigated under denial of service (DoS) attacks and uncertain dynamics. DoS attacks usually block communication channels, resulting in the vehicle inability to receive data from the neighbors. In severe cases, it will affect the control performance of CVSs and even cause vehicle collision and life threats. In order to keep the vehicle platoon stable when the DoS attacks happen, we introduce a random characteristic to describe the impact of the packet loss behavior caused by them. Dependent on the length of the lost packets, we propose a security platoon control protocol to deal with it. Furthermore, the security platoon control problem of CVSs is transformed into a stable problem of Markov jump systems (MJSs) with uncertain parameters. Next, the Lyapunov function method and linear matrix inequations (LMI) are used to analyze the internal stability and design controller. Finally, several simulation results are presented to illustrate the effectiveness of the proposed method.

With the development of the information age, the process of global networking continues to deepen, and the cyberspace security has become an important support for today’s social functions and social activities. Web applications which have many security risks are the most direct interactive way in the process of the Internet activities. That is why the web applications face a large number of network attacks. Interpretive dynamic programming languages are easy to lean and convenient to use, they are widely used in the development of cross-platform web systems. As well as benefit from these advantages, the web system based on those languages is hard to detect errors and maintain the complex system logic, increasing the risk of system vulnerability and cyber threats. The attack defense of systems based on interpretive dynamic programming languages is widely concerned by researchers. Since the advance of endogenous security technologies, there are breakthroughs on the research of web system security. Compared with traditional security defense technologies, these technologies protect the system with their uncertainty, randomness and dynamism. Based on several common network attacks, the traditional system security defense technology and endogenous security technology of web application based on interpretive dynamic languages are surveyed and compared in this paper. Furthermore, the possible research directions of those technologies are discussed.

Cyber-Physical System (CPS) represents systems that join both hardware and software components to perform real-time services. Maintaining the system's reliability is critical to the continuous delivery of these services. However, the CPS running environment is full of uncertainties and can easily lead to performance degradation. As a result, the need for a recovery technique is highly needed to achieve resilience in the system, with keeping in mind that this technique should be as green as possible. This early doctorate proposal, suggests a game theory solution to achieve resilience and green in CPS. Game theory has been known for its fast performance in decision-making, helping the system to choose what maximizes its payoffs. The proposed game model is described over a real-life collaborative artificial intelligence system (CAIS), that involves robots with humans to achieve a common goal. It shows how the expected results of the system will achieve the resilience of CAIS with minimized CO2 footprint.

Since the cyber and physical layers in the distribution system are deeply integrated, the traditional distribution system has gradually developed into the cyber-physical distribution system (CPDS), and the failures of the cyber layer will affect the reliable and safe operation of the whole distribution system. Therefore, this paper proposes an CPDS planning method considering the reliability of the cyber-physical system. First, the reliability evaluation model of CPDS is proposed. Specifically, the functional reliability model of the cyber layer is introduced, based on which the physical equipment reliability model is further investigated. Second, an optimal planning model of CPDS considering cyber-physical random failures is developed, which is solved using the Monte Carlo Simulation technique. The proposed model is tested on the modified IEEE 33-node distribution system, and the results demonstrate the effectiveness of the proposed method.

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.

This paper presents a study on the "Dynamic Load Altering Attacks" (D-LAAs), their effects on the dynamics of a transmission network, and provides a robust control protection scheme, based on polytopic uncertainties, invariance theory, Lyapunov arguments and graph theory. The proposed algorithm returns an optimal Energy Storage Systems (ESSs) placement, that minimizes the number of ESSs placed in the network, together with the associated control law that can robustly stabilize against D-LAAs. The paper provides a contextualization of the problem and a modelling approach for power networks subject to D-LAAs, suitable for the designed robust control protection scheme. The paper also proposes a reference scenario for the study of the dynamics of the control actions and their effects in different cases. The approach is evaluated by numerical simulations on large networks.

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.

Artificial intelligence (AI) was engendered by the rapid development of high and new technologies, which altered the environment of business financial audits and caused problems in recent years. As the pioneers of enterprise financial monitoring, auditors must actively and proactively adapt to the new audit environment in the age of AI. However, the performances of the auditors during the adaptation process are not so favorable. In this paper, methods such as data analysis and field research are used to conduct investigations and surveys. In the process of applying AI to the financial auditing of a business, a number of issues are discovered, such as auditors' underappreciation, information security risks, and liability risk uncertainty. On the basis of the problems, related suggestions for improvement are provided, including the cultivation of compound talents, the emphasis on the value of auditors, and the development of a mechanism for accepting responsibility.

Vulnerability discovery is an important field of computer security research and development today. Because most of the current vulnerability discovery methods require large-scale manual auditing, and the code parsing process is cumbersome and time-consuming, the vulnerability discovery effect is reduced. Therefore, for the uncertainty of vulnerability discovery itself, it is the most basic tool design principle that auxiliary security analysts cannot completely replace them. The purpose of this paper is to study the source code vulnerability discovery method based on graph neural network. This paper analyzes the three processes of data preparation, source code vulnerability mining and security assurance of the source code vulnerability mining method, and also analyzes the suspiciousness and particularity of the experimental results. The empirical analysis results show that the types of traditional source code vulnerability mining methods become more concise and convenient after using graph neural network technology, and we conducted a survey and found that more than 82% of people felt that the design source code vulnerability mining method used When it comes to graph neural networks, it is found that the design efficiency has become higher.

Physical Unclonable Functions (PUFs) are the secured hardware primitives to authenticate Integrated Circuits (ICs) from various unauthorized attacks. The secured key generation mechanism through PUFs is based on random Process Variations (PVs) inherited by the CMOS transistors. In this paper, we proposed a chaotic-based challenge generation mechanism to feed the arbiter PUFs. The chaotic property is introduced to increase the non-linearity in the arbitration mechanism thereby the uncertainty of the keys is attained. The chaotic sequences are easy to generate, difficult to intercept, and have the additional advantage of being in a large number Challenge-Response Pair (CRP) generation. The proposed design has a significant advantage in key generation with improved uniqueness and diffuseness of 47.33%, and 50.02% respectively. Moreover, the enhancement in the reliability of 96.14% and 95.13% range from −40C to 125C with 10% fluctuations in supply voltage states that it has prominent security assistance to the Internet of Things (IoT) enabled devices against malicious attacks.

For designing the interaction with robots in healthcare scenarios, understanding how trust develops in such situations characterized by vulnerability and uncertainty is important. The goal of this study was to investigate how technology-related user dispositions, anxiety, and robot characteristics influence trust. A second goal was to substantiate the association between hospital patients' trust and their intention to use a transport robot. In an online study, patients, who were currently treated in hospitals, were introduced to the concept of a transport robot with both written and video-based material. Participants evaluated the robot several times. Technology-related user dispositions were found to be essentially associated with trust and the intention to use. Furthermore, hospital patients' anxiety was negatively associated with the intention to use. This relationship was mediated by trust. Moreover, no effects of the manipulated robot characteristics were found. In conclusion, for a successful implementation of robots in hospital settings patients' individual prior learning history - e.g., in terms of existing robot attitudes - and anxiety levels should be considered during the introduction and implementation phase.

The outages and power shortages are common occurrences in today's world and they have a significant economic impact. These failures can be minimized by making the power grid topologically robust. Therefore, the vulnerability assessment in power systems has become a major concern. This paper considers both pure and extended topological method to analyse the vulnerability of the power system to single line failures. The lines are ranked based on four spectral graph metrics: spectral radius, algebraic connectivity, natural connectivity, and effective graph resistance. A correlation is established between all the four metrics. The impact of load uncertainty on the component ranking has been investigated. The vulnerability assessment has been done on IEEE 9-bus system. It is observed that load variation has minor impact on the ranking.

Despite the fact that the power grid is typically regarded as a relatively stable system, outages and electricity shortages are common occurrences. Grid security is mainly dependent on accurate vulnerability assessment. The vulnerability can be assessed in terms of topology-based metrics and flow-based metrics. In this work, power flow analysis is used to calculate the metrics under single line contingency (N-1) conditions. The effect of load uncertainty on system vulnerability is checked. The IEEE 30 bus power network has been used for the case study. It has been found that the variation in load demand affects the system vulnerability.

This paper proposes a power network vulnerability analysis method based on topological approach considering of uncertainties from high-penetrated wind generations. In order to assess the influence of the impact of wind generation owing to its variable wind speed etc., the Quasi Monte Carlo based probabilistic load flow is adopted and performed. On the other hand, an extended stochastic topological vulnerability method involving Complex Network theory with probabilistic load flow is proposed. Corresponding metrics, namely stochastic electrical betweenness and stochastic net-ability are proposed respectively and applied to analyze the vulnerability of power network with wind generations. The case study of CIGRE medium voltage benchmark network is performed for illustration and evaluation. Furthermore, a cascading failures model considering the stochastic metrics is also developed to verify the effectiveness of proposed methodology.

An accurate state estimation (SE) considering increased uncertainty by the high penetration of renewable energy systems (RESs) is more and more important to enhance situational awareness, and the optimal and resilient operation of the renewable-rich power grids. However, it is anticipated that adversaries who plan to manipulate the target power grid will generate attacks that inject inaccurate data to the SE using the vulnerabilities of the devices and networks. Among potential attack types, false data injection attack (FDIA) is gaining popularity since this can bypass bad data detection (BDD) methods implemented in the SE systems. Although numerous FDIA detection methods have been recently proposed, the uncertainty of system configuration that arises by the continuously increasing penetration of RESs has been been given less consideration in the FDIA algorithms. To address this issue, this paper proposes a new FDIA detection scheme that is applicable to renewable energy-rich power grids. A deep learning framework is developed in particular by synergistically constructing a Bidirectional Long Short-Term Memory (Bi-LSTM) with modern smart grid characteristics. The developed framework is evaluated on the IEEE 14-bus system integrating several RESs by using several attack scenarios. A comparison of the numerical results shows that the proposed FDIA detection mechanism outperforms the existing deep learning-based approaches in a renewable energy-rich grid environment.

Real-time situational awareness (SA) plays an essential role in accurate and timely incident response. Maintaining SA is, however, extremely costly due to excessive false alerts generated by intrusion detection systems, which require prioritization and manual investigation by security analysts. In this paper, we propose a novel approach to prioritizing alerts so as to maximize SA, by formulating the problem as that of active learning in a hidden Markov model (HMM). We propose to use the entropy of the belief of the security state as a proxy for the mean squared error (MSE) of the belief, and we develop two computationally tractable policies for choosing alerts to investigate that minimize the entropy, taking into account the potential uncertainty of the investigations' results. We use simulations to compare our policies to a variety of baseline policies. We find that our policies reduce the MSE of the belief of the security state by up to 50% compared to static baseline policies, and they are robust to high false alert rates and to the investigation errors.

Active consumers have now been empowered thanks to the smart grid concept. To avoid fossil fuels, the demand side must provide flexibility through Demand Response events. However, selecting the proper participants for an event can be complex due to response uncertainty. The authors design a Contextual Consumer Rate to identify the trustworthy participants according to previous performances. In the present case study, the authors address the problem of new players with no information. In this way, two different methods were compared to predict their rate. Besides, the authors also refer to the consumer privacy testing of the dataset with and without information that could lead to the participant identification. The results found to prove that, for the proposed methodology, private information does not have a high impact to attribute a rate.

Over the past decade, smart grids have been widely implemented. Real-time pricing can better address demand-side management in smart grids. Real-time pricing requires managers to interact more with consumers at the data level, which raises many privacy threats. Thus, we introduce differential privacy into the Real-time pricing for privacy protection. However, differential privacy leaves more space for an adversary to compromise the robustness of the system, which has not been well addressed in the literature. In this paper, we propose a novel active attack detection scheme against stealthy attacks, and then give the proof of correctness and effectiveness of the proposed scheme. Further, we conduct extensive experiments with real datasets from CER to verify the detection performance of the proposed scheme.

In response to the vulnerabilities in traditional perimeter-based network security, the zero trust framework is a promising approach to secure modern network systems and address the challenges. The core of zero trust security is agent-centric trust evaluation and trust-based security decisions. The challenges, however, arise from the limited observations of the agent's footprint and asymmetric information in the decision-making. An effective trust policy needs to tradeoff between the security and usability of the network. The explainability of the policy facilitates the human understanding of the policy, the trust of the result, as well as the adoption of the technology. To this end, we formulate a zero-trust defense model using Partially Observable Markov Decision Processes (POMDP), which captures the uncertainties in the observations of the defender. The framework leads to an explainable trust-threshold policy that determines the defense policy based on the trust scores. This policy is shown to achieve optimal performance under mild conditions. The trust threshold enables an efficient algorithm to compute the defense policy while providing online learning capabilities. We use an enterprise network as a case study to corroborate the results. We discuss key factors on the trust threshold and illustrate how the trust threshold policy can adapt to different environments.

The parameters of transmission line vary with environmental and operating conditions, thus the paper proposes a digital twin-based transmission line model. Based on synchrophasor measurements from phasor measurement units, the proposed model can use the maximum likelihood estimation (MLE) to reduce uncertainty between the digital twin and its physical counterpart. A case study has been conducted in the paper to present the influence of the uncertainty in the measurements on the digital twin for the transmission line and analyze the effectiveness of the MLE method. The results show that the proposed digital twin-based model is effective in reducing the influence of the uncertainty in the measurements and improving the fault location accuracy.

This paper proposes an intelligent line congestion prognosis scheme based on wide-area measurements, which accurately identifies an impending congestion and the problem causing the congestion. Due to the increasing penetration of renewable energy resources and uncertainty of load/generation patterns in the Active Distribution Networks (ADNs), power line congestion is one of the issues that could happen during peak load conditions or high-power injection by renewable energy resources. Congestion would have devastating effects on both the economical and technical operation of the grid. Hence, it is crucial to accurately predict congestions to alleviate the problem in-time and command proper control actions; such as, power redispatch, incorporating ancillary services and energy storage systems, and load curtailment. We use neural network methods in this work due to their outstanding performance in predicting the nonlinear behavior of the power system. Bayesian Regularization, along with Levenberg-Marquardt algorithm, is used to train the proposed neural networks to predict an impending congestion and its cause. The proposed method is validated using the IEEE 13-bus test system. Utilizing the proposed method, extreme control actions (i.e., protection actions and load curtailment) can be avoided. This method will improve the distribution grid resiliency and ensure the continuous supply of power to the loads.

The accelerated penetration rate of renewable energy sources (RES) brings environmental benefits at the expense of increasing operation cost and undermining the satisfaction of the N-1 security criterion. To address the latter issue, this paper extends the state of the art, i.e. deterministic AC security-constrained optimal power flow (SCOPF), to capture two new dimensions: RES stochasticity and inter-temporal constraints of emerging sources of flexibility such as flexible loads (FL) and energy storage systems (ESS). Accordingly, the paper proposes and solves for the first time a new problem formulation in the form of stochastic multi-period AC SCOPF (S-MP-SCOPF). The S-MP-SCOPF is formulated as a non-linear programming (NLP). It computes optimal setpoints in day-ahead operation of flexibility resources and other conventional control means for congestion management and voltage control. Another salient feature of this paper is the comprehensive and accurate modelling: AC power flow model for both pre-contingency and post-contingency states, joint active/reactive power flows, inter-temporal resources such as FL and ESS in a 24-hours time horizon, and RES uncertainties. The applicability of the proposed model is tested on 5-bus (6 contingencies) and 60 bus Nordic32 (33 contingencies) systems.

Disinformation, fake news, and unverified rumors spread quickly in online social networks (OSNs) and manipulate people's opinions and decisions about life events. The solid mathematical solutions of the strategic decisions in OSNs have been provided under game theory models, including multiple roles and features. This work proposes a game-theoretic opinion framework to model subjective opinions and behavioral strategies of attackers, users, and a defender. The attackers use information deception models to disseminate disinformation. We investigate how different game-theoretic opinion models of updating people's subject opinions can influence a way for people to handle disinformation. We compare the opinion dynamics of the five different opinion models (i.e., uncertainty, homophily, assertion, herding, and encounter-based) where an opinion is formulated based on Subjective Logic that offers the capability to deal with uncertain opinions. Via our extensive experiments, we observe that the uncertainty-based opinion model shows the best performance in combating disinformation among all in that uncertainty-based decisions can significantly help users believe true information more than disinformation.