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Most existing adaptive information security approaches focus on simplified behavioral patterns and work as isolated models. This limits their effectiveness against advanced and dynamic cyber threats. Therefore, there is an emergent requirement for a mathematically unified framework that can dynamically capture and forecast the aggregate behavior of both the attacker and the defender in a complex environment. The paper proposes a mathematical modeling approach that combines composite behavior models into adaptive information security strategies. The framework encapsulates heterogeneous behavioral patterns into a unified dynamic model that can adapt to an ever-changing threat landscape. This result in novel adaptation rules derived from system dynamics and game theory, with the aim of enabling proactive defense mechanisms that can adapt to real-time challenges posed by adversary actors. The outcomes presented in this paper demonstrate strong improvements in threat detection, mitigation speed, and resource optimization through systematic model implementation, comprehensive simulation, and positive statistical hypothesis testing. The comparison reveals that the proposed method is generally superior to existing methods in scalability and effectiveness. It presents a new class of adaptive cybersecurity models that have deeper behavioral insights and enhanced resilience in complicated threat environments.

This article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event‐triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources. In this context, a new memory stochastic state feedback controller is proposed to stabilize the closed‐loop networked control system. A new randomly occurring deception attacks model is employed to deal with the security problem of NCSs. Sufficient stability conditions are derived based on a suitable Lyapunov‐Krasovskii functional (LKF). The designed methodology is proposed in terms of linear matrix inequality to synthesize both event‐triggered parameters and controller gains, and to reduce the conservatism of the system some integral lemma are exploited to bind the time derivative of the LKF. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed method which provides a maximal upper bound value of the network‐induced delay and less transmitted packet regarding the maximal value delay obtained in other works, so less conservatism results are obtained, compared to previous ones in the literature. This article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event‐triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources.

As the worst-case interacting false data to the power system state estimation (SE), cyber data attacks can avoid being filtered out by most bad data detectors. In this study, coordinated attacks (unobservable attack and logic bomb attack) and coordinated defences (honeypot and weakening vision) are used to analyse attackers’ and defenders’ behaviours, respectively. To quantify the potential physical influences (attack-and-defence) benefits, the residual of the expected state is devised. Subsequently, a zero-sum stochastic game is utilised to model the interaction between the cyber-physical power system and the external attack-and-defence actions. This game is demonstrated to admit a Nash equilibrium and the minimax Q-learning algorithm is introduced to enable the two players to reach their equilibrium strategies while maximising their respective minimum rewards in a sequence of stages. Numerous simulations of the stochastic game model on the IEEE 14-bus system show that while resisting the isolated or coordinated attacks, the optimal coordinated defences are more effective than those of isolated attacks.

This paper addresses the inverse optimal security control problems for a class of stochastic non-strict feedback nonlinear cyber-physical systems under sensor and actuator attacks. The concerned system model includes both stochastic disturbances and more general nonlinearity. First, to make the control design feasible, a linear state transformation is applied to the attacked system. Furthermore, in the process of backstepping design, based on the Nussbaum gain function formula, fuzzy logic system approximation method, and inverse optimal control theory, combing the available output signal, an output feedback inverse optimal controller is proposed. Specifically, the designed controller not only ensures that the system is secure under network attacks but also optimal in terms of the cost function. Finally, two physical examples are given to verify the effectiveness of the proposed control scheme in various network attacks.

Federated cyber–physical systems (CPSs) present unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. Distributed ledger technology (DLT) offers a promising approach to implementing a resilient authentication and authorization mechanism and an immutable record of CPS identities and transactions in federated environments. However, using Distributed Ledger (DL) within a CPS raises some important questions regarding scalability, throughput, latency, and potential bottlenecks, which require effective modeling of DL performance. This paper proposes a novel approach to modeling distributed ledgers using Colored Timed Petri Nets (CPNs). We focus on the performance modeling of Hyperledger Fabric (HLF), a permissioned distributed ledger technology which provides a backbone for a Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI), a novel framework for secure communication between CPS devices. We implement our model using CPN Tools, a widely adopted CPN modeling software that provides advanced simulation, analysis, and performance monitoring features. Our model offers a robust framework for studying distributed ledger systems’ synchronization, throughput, and response time. It supports flexibility in modeling transaction validation and consensus algorithms, which provides an opportunity for adapting the model to future changes in HLF and modeling other DLs. We successfully validate our CPN model by comparing simulation results with experimental measurements obtained from a LAAFFI prototype.

While the number of things is growing accompanied with an explosive increase in wireless traffic, network providers are facing a set of challenges, especially that the massive number of devices are expected to communicate over the current cellular networks. In an attempt to relieve network congestion and increase throughput, we turn toward cell shrinking and offloading, a key technology in future 5G networks. Using this potential solution, we are mainly targeting two important issues: i) enabling cyber-physical systems (CPS) communications over cellular networks to provide CPS with several benefits such as ubiquitous coverage, global connectivity, reliability and security; and ii) offloading a proportion of CPS traffic to small cells, which in tun increases the throughput of macrocells, and frees more network resources to other users. Using stochastic geometry, we present an analysis on CPS offloading rate and achievable throughput when small cells base stations (SCBSs) are powered by solar energy. The solar energy harvesting allows SCBSs to offset the costs of serving CPS devices. Our results show the potential benefits for both macrocells and small cells in terms of minimum achievable throughput when the CPS offloading rate is high.

Given the deteriorative nature of industrial systems, implementation of advanced Preventive Maintenance (PM) strategies becomes of paramount importance to cope with the maintenance needs of ever-changing complex industrial and safety-critical systems. Conventionally, PM approaches are developed based on the perfect maintenance assumption, i.e., the underlying system is renewed to the as-good-as-new state after each preventive repair, or corrective maintenance action. Such an assumption, however, is not realistic in applications such as military machinery, power generation networks, and Cyber-Physical Systems (CPS), rendering conventional PM strategies impractical. In such application domains, it is not feasible to perform all the required maintenance actions during the available time leading to imperfect maintenance. Overlooking imperfect maintenance is critically problematic as it further deteriorates the reliability of the underlying system shortening its life span. Therefore, it is critical to perform optimal maintenance decisions under imperfect maintenance assumptions. While Geometric Process (GP) is broadly used for imperfect maintenance modeling and analysis of repairable systems, its utilization to describe the failure mechanism of production systems/processes is still in its infancy. Existing works, typically, consider restrictive assumptions to simplify the maintenance models, which limits their applicability. This paper addresses this gap and proposes a rigorous mathematical model without the incorporation of restrictive assumptions. More specifically, we consider a system for which the operational states are observable through inspections performed at specified time points and only the failure state is immediately observable. Upon the inspection, if the system is found to be in a partially-failed state, a Minor Repair (MIR) action is conducted. The effect of MIR is imperfect and MIR can only be conducted a maximum number of N times during a production cycle. After performing a MIR action, the failure mechanism of the system changes according to a stochastic decreasing GP. When the system enters the failure state, a Major Repair (MJR) action is conducted, which brings the system back to the as-good-as-new state. A comprehensive set of numerical examples, comparative studies, and sensitivity analyses are conducted to evaluate the efficacy of the proposed maintenance policy.

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods, which focus on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks, with an emphasis on fast data processing and model adaptation. AAD has been researched extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on current research in this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS from 2013 to November 2023. We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our findings show that most studies addressed either model adaptation or data processing, but rarely both simultaneously. This indicates a research gap in fully adaptive solutions. We also categorize algorithms, datasets, and attack characteristics, and summarize strengths and weaknesses across the literature. Our review provides a structured and accessible reference for researchers and practitioners, offering insights into key trends and highlighting limitations in current approaches. Finally, we outline several future research directions, including the need for integrated real-time processing and adaptive learning, explainability, and uncertainty quantification in AAD for CPS.

Stochastic population models are widely used to model phenomena in different areas such as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond. Quantitative analysis of stochastic population models easily becomes challenging due to the combinatorial number of possible states of the population. Moreover, while the modeller easily hypothesises the mechanistic aspects of the model, the quantitative parameters associated to these mechanistic transitions are difficult or impossible to measure directly. In this paper, we investigate how formal verification methods can aid parameter inference for population discrete-time Markov chains in a scenario where only a limited sample of population-level data measurements—sample distributions among terminal states—are available. We first discuss the parameter identifiability and uncertainty quantification in this setup, as well as how the existing techniques of formal parameter synthesis and Bayesian inference apply. Then, we propose and implement four different methods, three of which incorporate formal parameter synthesis as a pre-computation step. We empirically evaluate the performance of the proposed methods over four representative case studies. We find that our proposed methods incorporating formal parameter synthesis as a pre-computation step allow us to significantly enhance the accuracy, precision, and scalability of inference. Specifically, in the case of unidentifiable parameters, we accurately capture the subspace of parameters which is data-compliant at a desired confidence level.

Human Activity Recognition (HAR) is an active research topic as it finds use in many real-world applications such as health monitoring and biometric user identification. Smart wearables which form an integral part of the Internet of Medical Things (IoMT) and Cyber-Physical Systems can provide information about human activities on a daily basis, which may be used as soft biometrics for user identification. Over the last few years, one of the popular problem-solving approaches for HAR has been in the form of artificial intelligence methods. Since security is related to robustness, our primary aim is to solve the problem with better model capabilities. In this study, we consider machine learning algorithms like Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (k-NN)(and deep learning algorithms such as Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU)) for the purpose of HAR. In order to improvise the model performance, we introduce optimization techniques along with CNN, LSTM, and GRU. We rely on Stochastic Gradient Descent (SGD), and optimizers Adam and RMSProp, and evaluate the strength of the models using Accuracy and F-1 score. Moreover, the study has been carried out on three datasets that incorporate several human activities. Our study indicates that adding a component of optimization increases the model performance, and the highest accuracy achieved in the study is almost 98%.