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The industrial internet of things (IIoT) has significantly transformed the industrial sectors by connecting devices, machines, and systems to enhance automation, efficiency, and decision-making. However, the increased interconnectivity also poses significant security challenges because IIoT devices control critical infrastructures and processes. Our work presents an implementation of a robust industrial cybersecurity strategy with a segmented network architecture, collaborative efforts between information technology (IT) and operational technology (OT) teams for enhanced resilience and effectiveness, and vertical honeypots across all Industry 4.0 levels integrated with Wazuh for log transmission and proactive threat response, alongside Snort intrusion detection system (IDS) monitoring network traffic. Additionally, we reinforce our architecture by Wazuh with Elasticsearch and Kibana as a security information and event management solution, facilitating data analysis and compliance enforcement through custom rulesets and cybersecurity threat intelligence (CTI) integration, with automatic updates for continuous adaptation against emerging threats. [ABSTRACT FROM AUTHOR]

The investigation uses a state estimation fuzzy technique to address the problem of dynamic event-triggered secure control in Markov jump neural networks with time-varying delays subject to cyber-attacks. Two different deception attacks are given, each employing stochastic variables with a Bernoulli distribution. Specifically, this article presents a dynamic event-triggered strategy and energy restrictions to reduce network traffic and protect network resources during deception attacks. To prevent the system state from becoming unavailable, a state estimator is concurrently developed using the output signal. The application of Lyapunov stability theory yields sufficient criteria that ensure neural network stability. The study also accurately describes the controller gain's by solving Linear matrix inequality. Numerical examples are provided to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

With the proliferation of the Internet, the number of cyber-attacks has increased worldwide. To prevent cyber-attacks, network administrators need to use analytical tools to disclose six new critical statistics such as Internet Protocol (IP) authenticity and classification, IP ownership, frequency of attacks, time of day, day of the week, and country of attack. There are four SSH attack analysis tools available, but existing tools lack visualization and cannot provide the necessary attack statistics. This paper proposes SSHAA, the first open source PyPI analysis tool that meets the requirement. Text mining technology is used for extracting data from SSH log files, classifying IPs with identified country names and calculating a variety of statistics. PyPI allows SSHAA to run on Windows, MacOS, and Linux operating systems. This paper highlights how to debut a PyPI application using SSHAA which will be the world's first tutorial in the security journals for maximum software dissemination. According to PePy, SSHAA has been downloaded 7668 times worldwide. The substantial number of downloads signifies the efficacy of the proposed tool. The proposed system offers ten functional statistics for SSH attack analysis. The six new functional statistics to information security are significant. [ABSTRACT FROM AUTHOR]

As the prevalence of Internet of Things (IoT) devices increases, Cyber incidents are also increasing significantly. These Cyber incidents are mainly caused by various attacks, such as Distributed Denial of Service (DDoS), Denial of Service (DoS), intrusions, and web-based attacks. This type of attacks can severely impact valuable IoT system resources, compromise stored data, and lead to substantial financial losses if not adequately mitigated. Detecting these attacks within network traffic is complex and requires intelligent Intrusion Detection Systems (IDS). This paper proposes a Machine Learning (ML) based hybrid IDS model for edge computing networks. The feature selection process employs the 'Recursive Feature Elimination technique' (RFE) combined with 'Random Forest' (RF) to identify optimal features for attack detection. The Hybrid IDS model integrates 'Random Forest' (RF), 'Decision Tree' (DT), 'Extra Tree' (ET), and 'K-Nearest Neighbor' (KNN) algorithms. The Hybrid IDS model is evaluated on four datasets: 'CIC-IDS-2017', 'NSL-KDD', 'UNSW-NB15', and 'CSE-CIC-IDS-2018'. The results of the proposed model show maximum prediction accuracy of 99.92%, 99.89%, 99.50%, and 99.13%, and F1-score values obtained are 99.95%, 99.90%, 99.23%, and 99.13% on 'CIC-IDS-2017', 'NSL-KDD', 'UNSW-NB15', and 'CSE-CIC-IDS2018' datasets, respectively. The experimental results clearly demonstrate that the proposed model performs better than the models reported in the existing studies. [ABSTRACT FROM AUTHOR]

Providing security to Internet of Medical Things (IoMT) is significant worldwide problem for future generations its implementation to be successful. The traditional security methodologies developed for IoMT struggles with the specific issues of high false positives and lower detection rate. Therefore, the proposed work aims to develop a ground-breaking intrusion detection model, named as, Group Teaching Optimized Probabilistic Deep Auto-Encoder (GTPDA) for increasing the security of IoMT networks. Here, the data transformation and normalization processes are applied to balance the dataset's properties. Then, an Intriguing Group Teaching Optimization (IGTO) algorithm is applied to choose the most correlated and essential traits from the normalized dataset for effective intrusion detection. Consequently, a Conditional Probabilistic Deep Auto-Encoder (CPDAE) model is used to more accurately classify the type of intrusion with system complexity. This study uses the BoT-IoT, Kaggle invasion dataset, and ToN-IoT open benchmarking datasets for evaluation and performance assessments. Among all, the proposed GTPDA with its various performance metrics presented, achieves an impressive 98.8% precision, 99% recall, 98.8% F1-score, and 99% accuracy, showing its significant performance in ensuring IoMT network security. [ABSTRACT FROM AUTHOR]

This paper explores the problem of the security of unmanned aerial vehicles (UAV) by introducing the drone security scoring system (D3S). D3S is a security assessment method that analyzes the security of a UAV model by analyzing its components. Penetration tests were carried out to support D3S and identify potential vulnerabilities in UAVs. Specific cyber-attacks, such as deauthentication, flooding, and replay, were executed in an effort to take full control of the UAVs. Eight different UAV models were assessed using D3S, revealing notable variations in performance, both in control communications and video transmission. Security scores ranging from 0.9 to 4.5 out of 5 were obtained, showing significantly divergent security levels. [ABSTRACT FROM AUTHOR]

The increasing frequency, severity, and complexity of cyber-attacks at EU level highlights the significant importance of robust cyber security frameworks. This article examines the role of EU regulations, in particular the Directive on measures for a high common level of cybersecurity across the Union (NIS 2 Directive) and the Cyber Resilience Act (CRA), in strengthening cyber resilience in critical sectors. First, the review of EU regulations was carried out through a comparative analysis of the legislative documents to highlight objectives, targeted entities, security measures, and compliance and clarify how they are designed to improve cyber resilience in critical sectors. Furthermore, the methodology includes an applied case study designed to explore the practical implications of European regulations on cyber resilience through the examination of the hybrid threat - multiple cyber-attacks mixed with influence operations through the cyberspace targeting the civilian and defense infrastructures of an essential port. By analyzing the attacks through the lens of the NIS 2 Directive and CRA, this case study illustrates how EU regulations provide an efficient foundation for strengthening security and response measures. It also identifies operational challenges and demonstrates how adaptable frameworks aligned with EU regulations enable organizations to recover more effectively. These findings contribute to the broader discourse on securing critical infrastructures and advancing practical solutions to cyber resilience in an interconnected digital environment. To further improve understanding and preparedness, future research should focus on organizing tabletop exercises (TTX) that simulate scenarios similar to those analyzed in this case study. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the potential use of Neuro-Fuzzy Systems (NFS) in safeguarding autonomous vehicles (AVs) against cyber-attacks. As innovative technology continues to permeate various aspects of daily life, the integration of advanced technologies, such as Artificial Neural Networks (ANN) and fuzzy inference systems (FIS), holds promise for enhancing the security of intelligent transport systems. With the increasing prominence of autonomous vehicles and self-driving cars in smart city systems, it is imperative to address vulnerabilities that may compromise their security. Existing vulnerabilities, including insecure applications and data-gathering vulnerabilities, pose significant obstacles to the widespread adoption of this technology. The potential consequences of security breaches in autonomous vehicles, such as endangering the lives of individuals both inside and outside of the vehicle, underscore the critical need for comprehensive security measures. By leveraging NFS, this study explored the feasibility of mitigating cyber-attacks targeting AVs, thereby bolstering their security and resilience against malicious intrusions. [ABSTRACT FROM AUTHOR]

With advances in Cyber-Physical Systems (CPS), privacy-preserving and security issues have attracted substantial attention. A crucial function provided by CPS is anomaly detection on large-scale, complicated, and dynamic data. Physical and network information about the systems for safeguarding original data and identifying cyberattacks is needed in order to develop a reliable privacy-preserving anomaly detection approach. Conventional anomaly detection techniques cannot be directly used to solve these problems because they must deal with the expanding amount of data and need domain-specific expertise. By filtering and choosing key aspects from the original data for improved safety, this research presents a privacy preservation approach for secure anomaly detection. For selecting features, the Mutation Mayfly Algorithm (MMA) has been developed. The proposed program combines key benefits of swarm intelligence and evolutionary algorithms. The usage of MMA in feature selection results from its better accuracy and straightforward structure. Then, a strategy for identifying anomalies based on a Kalman Filter (KF) model and a Gaussian Mixture Model (GMM) has been created to find cyberattacks in CPS. Furthermore, the efficacy of privacy-preserving anomaly detection is being improved through the utilization of a Gaussian Mixture Model (GMM) to convert the noteworthy features into representative characteristics. The present study provides a description of the KF approach, which involves the analysis of the dynamics pertaining to both normal and attack events. The system employs a dynamic thresholding technique to detect anomalous behavior by calculating the lower and upper boundaries of normal activity. The architecture is assessed using two open datasets, UNSW-NB15 for network data and Power System for data on cyber power. [ABSTRACT FROM AUTHOR]

Featured Application: This paper applies process mining to improve IT vulnerability management by enhancing remediation processes and identifying inefficiencies. Using the CRISP data mining framework, it enables the continuous monitoring and improvement of security processes for better operational performance. With the number of cyber-attacks growing rapidly and the opportunities to attack companies widening, vulnerability management is gaining increased importance. It needs to reduce the variety of possibilities by remediating vulnerabilities found in IT infrastructures. Process mining is an established method used to discover, analyze, and manage data on (implemented) business processes. This paper proposes utilizing process mining for business-critical processes, including IT processes. This work explores how vulnerability management processes can be improved through process monitoring, targeting the operation time of processes, remediation, and the continuous management of the vulnerability process design. Thereby, process mining is applied for monitoring following the CRISP data mining approach. Process analysis includes process discovery, conformance checking, and process enhancement. Improving the vulnerability management process is based on the discovered vulnerability process, which is enhanced with throughput time data as well as priority, source, and assignment group data. Improvements concern changing the configuration of a source and better training for certain assignment groups. Process discovery can also be used to identify the cause for improper remediation of vulnerabilities. In this case, unwanted activity flows, which indicate that a major part of the vulnerability management process is being skipped, need to be identified. Finally, the continuous use of process mining allows for the monitoring of process modifications, including the impact of implemented improvements. [ABSTRACT FROM AUTHOR]

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Artificial intelligence (AI) has revolutionized many sectors with its development, but it is also likely to pose new threats when used maliciously. This study examines the implications of state and nonstate actors using AI to conduct more sophisticated cyber-attacks and their potential consequences for international relations and global security. Cyber-attack detection has become more automated, targeted, and challenging due to rapid advances in AI. Thanks to AI, adversaries can now impersonate humans, manipulate data, eavesdrop on conversations, and exploit system weaknesses on an unprecedented scale. Unchecked, AI-enabled cyber-attacks can undermine diplomatic relations, increase the likelihood of military conflict between governments, and destabilize the economy. The international community will need new legal frameworks and technological measures to mitigate these risks. International cooperation is necessary to limit the development of AI cyber weapons, develop robust systems, and establish guidelines for responsible state activity in cyberspace. Through cooperation and foresight, the potential of AI is more likely to be achieved while reducing the likelihood of intensified cyber warfare. This paper provides a broad literature perspective and offers recommendations for both legal and technological solutions to reduce the likelihood of the effects of AI-based cyber-attacks.

In modern automotive systems, introducing multiple connectivity protocols has transformed in-vehicle network communication, resulting in the widely recognized Controller Area Network (CAN) standard. Despite its ubiquitous use, the CAN protocol lacks critical security features, making vehicle communications vulnerable to message injection attacks. These assaults might confuse original electronic control units (ECUs) or cause system failures, emphasizing the need for strong cybersecurity solutions in automobile networks. This study addresses this need by developing a quick and efficient abnormal traffic detection system to protect vehicular communications from cyber attacks. The proposed system utilizes four machine learning techniques: Adaboost Trees (ABT), Coarse Decision Trees (CDT), Naive Bayes Classifier (NBC), and Support Vector Machine (SVM). These models were carefully assessed on the Car-Hacking-2018 dataset, which simulates real-time vehicular communication scenarios. Specifically, the system considers five balanced classes, including one normal traffic class and four classes for message injection attacks over the in-vehicle controller area network: fuzzy attack, DoS attack, RPM attack (spoofing), and gear attack (spoofing). Our best performance outcomes belong to the ABT model, which notched 99.8% classification accuracy and 6.67 µs of classification overhead. Such results have outweighed existing in-vehicle intrusion detection systems employing the same/similar dataset.

Data centres have grown drastically in size and in number as the digital economy has proliferated. For the advancement of society and the economy, data centres are becoming increasingly important. But even a little period of data centre downtime can be extremely harmful. Secure management of the physical infrastructure of data centres is essential to resolving this problem. A decentralized approach to healthcare systems is also made possible by blockchain technology, which gets rid of some of the drawbacks of centralized systems like single points of failure. Currently, a number of enhanced resilience security solutions using blockchain and ANP (Analytical Neural Processes) techniques have been presented to improve the security of transformation-based technologies. ANP finds false data and recognizes harmful data measured by medical sensors. For the Internet of Things (IoT) and Cyber Physical Systems (CPS), the development of defences against diverse cyber threats is advancing. Leveraging cloud environments to discover harmful code may not be a practical strategy in the future as malicious code grows in prevalence and there are no established techniques for identifying malicious code. Therefore, before the fog layer processes the data, transformation-based systems can identify and stop cyber-attacks. Additionally, it makes use of a blockchain network at the fog layer to guarantee data integrity and privacy by preventing data modification. Experimental findings demonstrate that the ANP and block chain models deliver what is promised. Additionally, the Transformer Neural Network (TNN) model's accuracy is 99.99% according to the F1 score accuracy indicator.

Abstract Due to the rising use of the Internet of Things (IoT), the connectivity of networks increases the risk of Distributed Denial of Service (DDoS) attacks. Decentralized systems commonly used in centralized security systems fail to adequately prevent potential cyber threats in IoT because of the issues of privacy and scaling. The method proposed in this study seeks to remedy these facts by employing Explainable Artificial Intelligence (XAI) together with Federated Deep Neural Networks (FDNNs) to detect and prevent DDoS attacks. Our approach is thus to use federated learning models that are to be trained on distributed and dissimilar sources of data without compromising on the privacy aspect. FDNNs were trained over three rounds with information from three client gadgets incorporating pre-processed datasets of various types of DDoS attacks. Additionally, for feature selection, we integrated XGBoost with SHapley Additive exPlanations (SHAP) to improve model interpretability. The proposed solution can be considered to be quite robust, privacy-preserving, and highly scalable for the detection of DDoS attacks on the IoT network. The results shown on the server side indicate that this approach accurately detects 99.78% of DDoS attacks with a precision rate as high as 99.80%, recall rate (detection rate) going up to 99.74% and F1 score reaching 99.76%. They emphasize that FL-based IDSs are strong enough to cope with cybersecurity challenges in IoT, thus offering hope for securing modern network infrastructures against ever-growing cyber threats.

ABSTRACT The main thrust of this study is to scrutinize the issues of fault estimation and asynchronous sampled‐data fault‐tolerant control for semi‐Markov jump cyber‐physical systems with external disturbances, faults and deception attacks. To do so, initially, an intermediate variable is framed and then using that variable as a foundation, a mode‐dependent intermediate estimator is constructed, which estimates the fault signals and system's state simultaneously. Due to the unavailability of mode information in the Markov chain for the observer/controller, a hidden Markov model is employed to represent the asynchronous scenario between the mode of the original system and that of the designed observer/controller. Subsequently, benefited by the estimated terms and sampled‐data approach, an asynchronous sampled fault‐tolerant control protocol is offered up that facilitates compensating for the faults occurring in the system. In the meantime, the extended passive performance is used to lessen the negative impact of external disturbances exerting on the system. Besides this, the deception attacks occurring in the system are presumed to have a stochastic nature that adheres to the Bernoulli distribution. Moreover, by constructing mode‐dependent Lyapunov–Krasovskii functional and blending it with integral inequalities, the sufficient condition confirming the intended outcomes is procured in the framework of linear matrix inequalities. Thereafter, on the platform of deduced adequate criteria, an explicit formulation for the requisite gain values can be obtained. Ultimately, simulation results are offered to verify the reliability of presented outcomes. [ABSTRACT FROM AUTHOR]

The maritime industry is constantly evolving and posing new challenges, especially with increasing digitalization, which has raised concerns about cyber-attacks on maritime supply chain agents. Although scholars have proposed various methods and classification models to counter these cyber threats, a comprehensive cyber-attack taxonomy for maritime supply chain actors based on a systematic literature review is still lacking. This review aims to provide a clear picture of common cyber-attacks and develop a taxonomy for their categorization. In addition, it outlines best practices derived from academic research in maritime cybersecurity using PRISMA principles for a systematic literature review, which identified 110 relevant journal papers. This study highlights that distributed denial of service (DDoS) attacks and malware are top concerns for all maritime supply chain stakeholders. In particular, shipping companies are urged to prioritize defenses against hijacking, spoofing, and jamming. The report identifies 18 practices to combat cyber-attacks, categorized into information security management solutions, information security policies, and cybersecurity awareness and training. Finally, this paper explores how emerging technologies can address cyber-attacks in the maritime supply chain network (MSCN). While Industry 4.0 technologies are highlighted as significant trends in the literature, this study aims to equip MSCN stakeholders with the knowledge to effectively leverage a broader range of emerging technologies. In doing so, it provides forward-looking solutions to prevent and mitigate cyber-attacks, emphasizing that Industry 4.0 is part of a larger landscape of technological innovation. [ABSTRACT FROM AUTHOR]

Modern battery systems exhibit a cyber-physical nature due to the extensive use of communication technologies in battery management. This makes modern cyber-physical battery systems vulnerable to cyber threats where an adversary can manipulate sensing and actuation signals to satisfy certain malicious objectives. In this work, we present a machine learning-based approach to enable resilience to adversarial attacks by detecting and estimating the attack and subsequently taking corrective action to mitigate the attack. In particular, we focus on false data injection type attacks on battery systems. The overall diagnostic algorithm consists of an adaptive boosting-based attack detector, a long short-term memory (LSTM) neural network-based attack estimator, and a corrective controller. The proposed algorithm is trained and tested by utilizing data from a complex battery electrochemical battery simulator. Simulation results are presented to verify the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive assessment of advanced cybersecurity attacks, their prevention, and future directions in the field. It aims to provide an overview of some of the most significant cyber-attacks on critical facilities that have occurred over the past 20 years. This paper aims to study various types of cyber-attacks, their effects, vulnerabilities, and the attackers and victims involved. The research identifies five major categories of attacks: web attacks, malware attacks, active attacks, passive attacks and cryptographic attacks. Each category is further divided into distinct types of cyber-attacks, which are described in terms of their proposals, evaluation parameters, characteristics and methodologies used. A summary of these attacks across various parameters is tabulated in this paper to facilitate detailed analysis. Additionally, the paper examines several cutting-edge solutions and preventive measures and reviews research gaps and limitations associated with various cyber-attacks. This study is significant as it provides researchers with a thorough review to better understand different cyber-attacks and recent developments in the field of cybersecurity. [ABSTRACT FROM AUTHOR]

Due to the rising use of the Internet of Things (IoT), the connectivity of networks increases the risk of Distributed Denial of Service (DDoS) attacks. Decentralized systems commonly used in centralized security systems fail to adequately prevent potential cyber threats in IoT because of the issues of privacy and scaling. The method proposed in this study seeks to remedy these facts by employing Explainable Artificial Intelligence (XAI) together with Federated Deep Neural Networks (FDNNs) to detect and prevent DDoS attacks. Our approach is thus to use federated learning models that are to be trained on distributed and dissimilar sources of data without compromising on the privacy aspect. FDNNs were trained over three rounds with information from three client gadgets incorporating pre-processed datasets of various types of DDoS attacks. Additionally, for feature selection, we integrated XGBoost with SHapley Additive exPlanations (SHAP) to improve model interpretability. The proposed solution can be considered to be quite robust, privacy-preserving, and highly scalable for the detection of DDoS attacks on the IoT network. The results shown on the server side indicate that this approach accurately detects 99.78% of DDoS attacks with a precision rate as high as 99.80%, recall rate (detection rate) going up to 99.74% and F1 score reaching 99.76%. They emphasize that FL-based IDSs are strong enough to cope with cybersecurity challenges in IoT, thus offering hope for securing modern network infrastructures against ever-growing cyber threats. [ABSTRACT FROM AUTHOR]

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system's security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators. [ABSTRACT FROM AUTHOR]

This article deals with the problem of event-triggered control of complex dynamical networks (CDNs) subject to stochastic cyber-attacks and random coupling delays. Hackers may abuse the system by injecting malicious activity into communication networks. Two types of non-linear functions for cyber-attacks, time-varying coupling delays, respectively, and a set of random variables satisfying the Bernoulli distribution are proposed. Against complex dynamical networks, an attacker has the capacity to change the transmitted data. So the traditional control method can be a major challenge, and it is essential to respond to cyber-attacks of this kind. An event-triggered mechanism (ETM) was implemented to avoid losing limited bandwidth. The input delay method is then used to build a time-varying sample under stochastic cyber-attacks. The stability criterion of the developed system model and the display expression of control parameters are then established using a new two-sided delay-dependent Lyapunov–Krasovskii functional (LKF) and linear matrix inequality (LMI) techniques. Lastly, two numerical examples, including the inverted pendulum application, are presented to illustrate the effectiveness and feasibility of the derived theoretical results. [ABSTRACT FROM AUTHOR]

Organizations of all types are prone to cybersecurity and information security attacks. Non-Profit Organizations (NPOs) and Non-Governmental Organizations (NGOs) are not exempt from using information technology solutions and, thus, have been the recipient victims of cyber attackers. There exist many areas and venues where data are collected to report back annually on the status and numbers of cybersecurity attacks against many sectors of our society. The Department of Homeland Security (DHS) Cybersecurity and Infrastructure Security Agency (CISA) catalogs sixteen critical sectors that are considered vital to the United States. However, finding where the NPO and NGO community should reside with regard to a categorized sector is challenging. The cybersecurity incident data collected by many agencies does place a focus on the sixteen sectors. This effort and write-up will focus on the NPO and NGO communities and provide the process followed to create the data repository, categorization of attacks taxonomy, fields captured, outlets, and areas where data that is relevant to historical cybersecurity incidents in these types of agencies is available. In addition, the beginning of a running log and dataset for the NPO and NGO community will emerge to determine if this activity is feasible and can continue. A desired outcome for this effort is to make available a dataset that can be referenced by researchers, students, and leaders investigating cybersecurity risk management and analysis of the NPO and NGO sectors. In addition, this effort was started with the purpose of providing students from varied academic disciplines the opportunity to engage in practical pedagogical cybersecurity and cybercrime activity. [ABSTRACT FROM AUTHOR]

The fusion of Internet of Things (IoT) technology into healthcare, known as the Internet of Medical Things (IoMT), has significantly enhanced medical treatment and operational efficiency. Real-time patient monitoring (RPM) and remote diagnostics enabled by IoMT allow doctors to treat more patients effectively and save lives. However, healthcare devices' interconnected nature makes them vulnerable to cyber-attacks, threatening patient privacy and security. Ensuring the security and accuracy of patient health data is paramount, as any tampering could have life-threatening consequences, especially in emergency situations. To address these challenges, this research focuses on developing robust security models to secure patient data in IoMT networks while meeting the growing demand for efficient healthcare services. Artificial intelligence (AI)-based technologies such as machine learning (ML) and deep learning (DL) have the potential to be employed as the methodology for intrusion detection. The goal of this research is threefold: firstly, the linear support vector machine (LinSVM) model; secondly, the convolutional support vector machine (ConvSVM) model; and finally, the categorical embedding (CatEmb) model, which have been proposed to overcome the issue of security in a network. This article offers the CatEmb model as the first effort to use a DL-based embedding approach to recognize intrusion in the IoMT environment, utilizing patient biometric and network traffic flow data. Our experimental results show the efficacy of the proposed DL models, with the LinSVM achieving a training accuracy of 99.78%, ConvSVM reaching 99.98%, and CatEmb achieving 99.84%. These models outperform existing methodologies by 2.61% in detecting network intrusions, as demonstrated through metrics such as detection rate and F1-score. Furthermore, the proposed approaches are thoroughly compared with the existing state-of-the-art studies. [ABSTRACT FROM AUTHOR]

This paper addresses the practical issue of load frequency control (LFC) in multi-area power systems with degraded actuators and sensors under cyber-attacks. A time-varying approximation model is developed to capture the variability in component degradation paths across different operational scenarios, and an optimal controller is constructed to manage stochastic degradation across subareas simultaneously. To assess the reliability of the proposed scheme, both Monte Carlo simulation and particle swarm optimization techniques are utilized. The methodology distinguishes itself by four principal attributes: (i) a time-varying degradation model that broadens the application from single-area to multi-area systems; (ii) the integration of physical constraints within the degradation model, which enhances the realism and practicality compared to existing methods; (iii) the sensor suffers from fault data injection attacks; and (iv) an optimal controller that leverages particle swarm optimization to effectively balance reliability and system performance, thereby improving both stability and reliability. This method has demonstrated its effectiveness and advantages in mitigating load disturbances, achieving its objectives in just one-third of the time required by established benchmarks. The case study validates the applicability of the proposed approach and demonstrates its efficacy in mitigating load disturbance amidst stochastic degradation in actuators and sensors under FDIA cyber-attacks. [ABSTRACT FROM AUTHOR]

Connected components in automobiles face cybersecurity threats if not adequately protected. The Controller Area Network (CAN), the primary communication protocol in vehicular systems, lacks essential security mechanisms, necessitating heightened cybersecurity awareness in the automotive industry. This paper presents a methodology to enhance cybersecurity within the CAN network, including a comparative study with benchmark results. Our research focuses on improving multiclass Intrusion Detection Systems (IDSs) for the CAN network. We introduce a hybrid deep learning model tailored for precise multiclass classification of attacks, an Artificial Neural Network (ANN)-based Light Gradient Boosting Machine (LightGBM) IDS that incorporates a comprehensive set of features. The evaluation on diverse cyber-attacks from the Car-Hacking dataset demonstrates the model's effectiveness. This study contributes to the field by offering a brief assessment of current state-of-the-art methods and presenting a novel approach to bolster cybersecurity. The proposed IDS serves as a valuable reference for ensuring the cybersecurity of automotive systems. [ABSTRACT FROM AUTHOR]

To improve the estimation accuracy of state of charge (SOC) and state of health (SOH) for lithium-ion batteries, in this paper, a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm (GA) combined with back propagation (BP) neural network is proposed, the research addresses the issue of data manipulation resulting from cyber-attacks. Firstly, anomalous data stemming from cyber-attacks are identified and eliminated using the isolated forest algorithm, followed by data restoration. Secondly, the incremental capacity (IC) curve is derived from the restored data using the Kalman filtering algorithm, with the peak of the IC curve (ICP) and its corresponding voltage serving as the health factor (HF). Thirdly, the GA-BP neural network is applied to map the relationship between HF, constant current charging time, and SOH, facilitating the estimation of SOH based on HF. Finally, SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time, and by updating the maximum available capacity. Experiments show that the root mean squared error of the joint estimation results does not exceed 1%, which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks. [ABSTRACT FROM AUTHOR]