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This article investigates the finite-time decentralized event-triggered feedback control problem for generalized neural networks (GNNs) with mixed interval time-varying delays and cyber-attacks. A decentralized event-triggered method reduces the network transmission load and decides whether sensor measurements should be sent out. The cyber-attacks that occur at random are described employing Bernoulli distributed variables. By the Lyapunov-Krasovskii stability theory, we apply an integral inequality with an exponential function to estimate the derivative of the Lyapunov-Krasovskii functionals (LKFs). We present new sufficient conditions in the form of linear matrix inequalities. The main objective of this research is to investigate the stochastic finite-time boundedness of GNNs with mixed interval time-varying delays and cyber-attacks by providing a decentralized event-triggered method and feedback controller. Finally, a numerical example is constructed to demonstrate the effectiveness and advantages of the provided control scheme.

The document discusses the increasing prevalence of cyber-attacks in healthcare, posing risks to patient safety, data privacy, and healthcare system functionality. Clinical laboratories are particularly vulnerable to cyber-attacks due to high digitalization levels. A survey conducted by the EFLM Task Force Preparation of Labs for Emergencies (TF-PLE) in Europe revealed that many respondents lacked familiarity with cyber threats, highlighting the need for increased cybersecurity training and incident response planning in hospitals and laboratories. The survey results suggest a global need for improved cybersecurity measures in healthcare facilities. [Extracted from the article]

Local governments face critical challenges in the era of digital transformation, balancing the responsibility of safeguarding resident information and administrative documents while maintaining data integrity and public trust. These responsibilities become even more critical as they transition into smart cities adopting advanced technological innovations to revolutionize governance, enhance service delivery, and foster sustainable and resilient urban environments. Technological advancements like Internet-of-Things devices and artificial intelligence-driven approaches can provide better services to residents, but they also expose local governments to cyberthreats. There has been, nonetheless, very little study on cybersecurity issues from the local government perspective, and information on the multifaceted nature of cybersecurity in local government settings is scattered and fragmented, highlighting the need for a conceptual understanding and adequate action. Against this backdrop, this study aims to identify key components of cybersecurity in a local governmental context through a systematic literature review. This review further extends to the development of a conceptual framework providing a comprehensive understanding of the local government’s cybersecurity landscape. This study makes a significant contribution to the academic and professional domains of cybersecurity issues and policies within the local governmental context, offering valuable insights to local decision-makers, practitioners, and academics. This study also helps identify vulnerabilities, enabling stakeholders to recognize shortcomings in their cybersecurity and implement effective countermeasures to safeguard confidential information and documents. Thus, the findings inform local government policy to become more cybersecurity-aware and prepared.

The Internet of Things (IoT), considered an intriguing technology with substantial potential for tackling many societal concerns, has been developing into a significant component of the future. The foundation of IoT is the capacity to manipulate and track material objects over the Internet. The IoT network infrastructure is more vulnerable to attackers/hackers as additional features are accessible online. The complexity of cyberattacks has grown to pose a bigger threat to public and private sector organizations. They undermine Internet businesses, tarnish company branding, and restrict access to data and amenities. Enterprises and academics are contemplating using machine learning (ML) and deep learning (DL) for cyberattack avoidance because ML and DL show immense potential in several domains. Several DL teachings are implemented to extract various patterns from many annotated datasets. DL can be a helpful tool for detecting cyberattacks. Early network data segregation and detection thus become more essential than ever for mitigating cyberattacks. Numerous deep-learning model variants, including deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), are implemented in the study to detect cyberattacks on an assortment of network traffic streams. The Canadian Institute for Cybersecurity’s CICDIoT2023 dataset is utilized to test the efficacy of the proposed approach. The proposed method includes data preprocessing, robust scalar and label encoding techniques for categorical variables, and model prediction using deep learning models. The experimental results demonstrate that the RNN model achieved the highest accuracy of 96.56%. The test results indicate that the proposed approach is efficient compared to other methods for identifying cyberattacks in a realistic IoT environment.

Internet of Things (IoT) equipment is rapidly being used in a variety of businesses and for a variety of reasons (for example, sensing and collecting data from the environment in both public and military settings). Because of their expanding involvement in a wide range of applications and their rising computational and processing capabilities, they are a viable attack target for malware tailored to infect specific IoT devices. This study investigates the potential of detecting IoT malware using different deep learning techniques: the classic feedforward neural network (FNN), convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The proposed method analyses the execution operation codes of IOT app sequences using modern NLP (natural language processing) methods. The current work utilized an IoT application dataset with 500 malware (collected from the IOTPOT dataset) and 500 goodware samples to train the proposed algorithms. The trained model is tested against 2971 fresh IoT malware and goodware samples. The samples were input into deep learning models, and performance metrics were obtained. The results demonstrate that the RNN model had the best accuracy (99.19%) in detecting fresh malware samples. On the other hand, the results were compared by the time required for training; the CNN model shows that it could achieve high accuracy (98.05%) with less training time. A comparison with various deep learning classifiers demonstrates that the RNN and CNN techniques produce the best results.

A modified active disturbance rejection control (ADRC) strategy for the frequency regulation of sustainable microgrid against hybrid cyber-attacks is proposed in this paper. The proposed technique mitigates the effect of attacks which in turn regulates the frequency of microgrid. The sustainable microgrid consists of solar and wind as renewable energy sources, energy storage systems (ESSs) like battery ESS and flywheel ESS, and controllable sources of energy like fuel cell and diesel generator. The cyber-attacks such as false data injection, denial-of-service and time delay attack are considered for designing the proposed ADRC technique. The robustness of the microgrid system with the proposed controller is investigated by varying generation and load, stochastic nature of attacks, parametric uncertainties and generation rate constraint and governor dead band nonlinearities. Further, the stability analysis of the microgrid with proposed ADRC is investigated under the aforementioned cyber-attacks using the small gain theorem. [ABSTRACT FROM AUTHOR]

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain. [ABSTRACT FROM AUTHOR]

It has become possible to link anything and everything to the Internet in recent decades due to the expanding Internet of Things (IoT). As a result, our usage of technology has changed a lot, causing digital disruption in the real world. IoT allows drones, sensors, digital set‐top boxes, surveillance cameras, wearable technology, and medical equipment to be connected to the internet. Healthcare, manufacturing, utilities, transportation, and housing are among the various sectors that has become intelligent. Recently, we have seen a surge in cybersecurity challenges and opportunities for the improvement of various IoT applications. Although cybersecurity and the IoT are extensively researched, there is a dearth of studies that exclusively focus on the evolution of cybersecurity challenges in the area of AI and machine learning, blockchain and zero trust, lightweight security, integration of IoT with 5G networks, and many more in the IoT world. The availability of environment‐capturing sensors and internet‐connected tracking devices allows for private life surveillance and cloud data transmission. Therefore, a significant problem for researchers and developers is to ensure the CIA (Confidentiality, Integrity, and Availability) security triangle for people. This paper presents a comprehensive study of cybersecurity applications, challenges, and opportunities in the IoT world. The IoT architectural layer, attacks against the IoT layer, and related issues are highlighted. Furthermore, cybersecurity issues and challenges in IoT along with the strength and weaknesses of existing techniques are discussed in detail. Our study will provide insight into various current cybersecurity research trends in the IoT world. [ABSTRACT FROM AUTHOR]

The technological advancements of Connected and Automated Vehicles (CAVs) are outpacing the current regulatory regime, potentially resulting in a disconnect between legislators, technology, and CAV stakeholders. Although many studies explore the regulatory requirements of operations of CAVs, studies on regulatory challenges specific to the cybersecurity of CAVs are also emerging and receiving lots of attention among researchers and practitioners. However, studies providing an up-to-date synthesis and analysis on CAVs regulatory requirements specific to cyber-risk reduction or mitigation are almost non-existent in the literature. This study aims to overcome this limitation by presenting a comprehensive overview of the role of key Intelligent Transportation Systems (ITS) stakeholders in CAV's cybersecurity. These stakeholders include road operators, service providers, automakers, consumers, repairers, and the general public. The outcome of this review is an in-depth synthesis of CAV-based ITS stakeholders by visualising their scope in developing a Cybersecurity Regulatory Framework (CRF). The study demonstrated the compliance requirements for ITS communication service providers, regulatory standards for CAVs automakers, policy readiness for CAVs customers and the general public who interact with CAVs, and the role of the CAVs Network Operator Centre in regulating CAVs data flow. Moreover, the study illuminates several critical pathways necessary in future for synthesizing and forecasting the legal landscape of CAV-based transportation systems to integrate the regulatory framework for CAV stakeholders. The paper's findings and conclusions would assist policymakers in developing a comprehensive CRF. CAV Cybersecurity Regulation, Stakeholder Roles, and Conceptualization of CAVs (traffic laws) breaches. [Display omitted] • The study offers a comprehensive overview of CAV Cybersecurity's Regulatory Framework. • Considers road operators, service providers, automakers, consumers, repairers and the public. • Emphasizes automaker regulatory requisites for cyber-safe CAV operation. • Proposes CAVs Network Operation Centre to regulate data flow. • Advocates eSafety Traffic Unit for regulating CAV operations. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is rapidly growing and is projected to develop in future years. The IoT connects everything from Closed Circuit Television (CCTV) cameras to medical equipment to smart home appliances to smart automobiles and many more gadgets. Connecting these gadgets is revolutionizing our lives today by offering higher efficiency, better customer service, and more effective goods and services in a variety of industries and sectors. With this anticipated expansion, many challenges arise. Recent research ranked IP cameras as the 2nd highest target for IoT attacks. IoT security exhibits an inherent asymmetry where resource-constrained devices face attackers with greater resources and time, creating an imbalanced power dynamic. In cybersecurity, there is a symmetrical aspect where defenders implement security measures while attackers seek symmetrical weaknesses. The SILEX malware case highlights this asymmetry, demonstrating how IoT devices' limited security made them susceptible to a relatively simple yet destructive attack. These insights underscore the need for robust, proactive IoT security measures to address the asymmetrical risks posed by adversaries and safeguard IoT ecosystems effectively. In this paper, we present the IoT vulnerabilities, their causes, and how to detect them. We focus on SILEX, one of the famous malware that targets IoT, as a case study and present the lessons learned from this malware. [ABSTRACT FROM AUTHOR]

With the rising demand for E-commerce, Social Networking websites, it has become essential to develop security protocols over the World Wide Web that can provide security and privacy to Internet users all over the globe. Several traditional encryption techniques and attack detection protocols can secure the data transmitted over public networks. However, hackers can effortlessly exploit them to acquire access to the users' sensitive information such as user ID, session ID, cookies, passwords, bank account details, contact numbers, private PINs, database information, etc. Researchers have continuously innovated new techniques to build a secure and robust system that cannot be easily hacked and manipulated. Still, there is much scope for novelty to provide security against contemporary techniques used by intruders. The motivation of this survey is to observe the recent developments in Cross-Site Scripting attacks and techniques used by researchers to secure confidential information. Cross-Site Scripting (XSS) has been recognized as one of the top 10 online application security risks by the Open Web Application Security Project (OWASP) for decades. Therefore, dealing with this security flaw in web applications has become essential to avoid further personal and financial damage to Internet users and business organizations. There is a need for an extensive survey of recent XSS attack detection techniques that can provide the right direction to researchers and security professionals. We present a complete overview of recent machine learning and neural network-based XSS attack detection techniques in this paper, covering deep neural networks, decision trees, web-log-based detection models, and many more. This paper also highlights the research gaps that must be addressed while designing attack detection models. Further, challenges researchers face during the development of recent techniques are also discussed. Finally, future directions are provided to reflect on new concepts that can be used in forthcoming research works to improve XSS attack detection techniques. [ABSTRACT FROM AUTHOR]

In an industrial control system, a programmable logic controller (PLC) plays a vital role in maintaining the stable operation of the system. Cyber-attacks can affect the regular operation by tampering with the data stored in the PLC, thereby damaging to the system. Thus, it is particularly important to develop an efficient cyber-attacks recovery method. However, owing to the impact of unknown factors in theoretical methods, poor scalability of automaton theory, and a lack of constraints during the training process of deep learning network models, the restoration accuracy and stability are low. Therefore, it is a significant challenge to design an appropriate method to improve the accuracy and stability of cyber-attacks recovery. In this study, the generative adversarial networks were applied to the problem of cyber-attacks recovery; furthermore, a multi-stage generative adversarial networks was designed. The model consisted of a Variational Autoencoder and two conditional energy-based generative adversarial networks (CEBGANs). Then the second CEBGAN uses the fitted random noise appending with the data generated by the previous stage and the historical data as additional information to obtain the restoration results. Moreover, a self-adaptive decision policy was established to enhance the restoration accuracy and stability. Experimental results demonstrated that the proposed method in this manuscript could effectively improve the accuracy of cyber-attacks data recovery and reduce the possibility of outliers in data recovery. [ABSTRACT FROM AUTHOR]

Resilient control and cyber security are of great importance for multiagent systems (MASs) due to the vulnerability during their coordination through information interaction among agents. Unexpected cyber-attacks on a single agent can spread quickly and can affect critically the safety and the performance of the system. This paper proposes a distributed adaptive resilient neural network (NN)-based control procedure to guarantee its stability and to make the agents to follow the leader's profile when there exist cyber-attacks and external disturbances in the system. Firstly, an adaptive neural network is designed to estimate the nonlinear part of the MAS. Then, a variable structure super twisting approach is proposed in which the adaptive weights of the NN, and a virtual disturbance are designed adaptively via updating laws. Moreover, stability criteria and control objectives are investigated through Lyapunov theorem, which leads to a novel scheme that can handle nonlinearity, cyber-attacks, and external disturbances without requirement of designing different controllers in an extra algorithm and considering any limiting predefined condition such as Lipschitz on the nonlinear function. As an application, an adaptive cruise control of a platoon of connected automated vehicles is considered to scrutinize the proposed procedure in the absence and the presence of cyber-attacks, which verify the theoretical results. [ABSTRACT FROM AUTHOR]

This paper argues that the primary threat posed by cyber-attacks is not cataclysmic physical destruction - but rather more insidious societal risks such as reduced trust in government. To test this claim, we collect and analyze survey data in the immediate aftermath of a ransomware attack against a Düsseldorf hospital (n = 707). We find that exposure to cyber-attacks significantly diminishes public confidence among segments of the population who are exposed to the attack. Cyber-attacks exploit particular qualities of cyberspace that are directly tied to matters of public confidence, causing a precipitous drop in public trust. Second, we identify the psychological mechanism underpinning this effect, with anger and dread intervening in countervailing directions. Feelings of anger triggered by exposure to cyber-attacks amplify public confidence, while the more potent feeling of dread reduces confidence. Our findings verify that governments cannot rely on a unifying social-cohesion effect following cyber-attacks since the public is liable to perceive the authorities as incapable of defending against future threats. We conclude by discussing why escalating cyber-threats can cause severe social upheaval and reduce trust in democratic institutions, and discuss what constitutes exposure to the new generation of attacks in cyberspace. [ABSTRACT FROM AUTHOR]

Currently, the Internet of Thing has become an integral part of the world's Internet infrastructure. One of the important matters in this field is the security of the network. Some networks like Software-defined Networking are proposed to improve it. In this paper, cyber-attacks have been simulated and various parameters such as the average rate of receiving traffic, the average response time and network delay have been analyzed. In the following, a new plan for switch buffers and their effect on the state of CPU performance, the controller bandwidth and the amount of packet loss have been discussed. The new buffer structure is proposed using a hash table, which uses a DRAM combination structure. To implement the proposed technique, AS5710-54X-EC switch and floodlight controller were used, and also Eclipse Neon 3.1 was used for writing modules. The results imply that this method had a great impact on detecting attacks in SDN-based IoT frameworks. [ABSTRACT FROM AUTHOR]

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The complex and interconnected infrastructure of smart cities offers several opportunities for attackers to exploit vulnerabilities and carry out cyberattacks that can have serious consequences for the functioning of cities' critical infrastructures. This study aims to address this phenomenon and characterize the dimensions of security risks in smart cities and present mitigation proposals to address these risks. The study adopts a qualitative methodology through the identification of 62 European research projects in the field of cybersecurity in smart cities, which are underway during the period from 2022 to 2027. Compared to previous studies, this work provides a comprehensive view of security risks from the perspective of multiple universities, research centers, and companies participating in European projects. The findings of this study offer relevant scientific contributions by identifying 7 dimensions and 31 sub-dimensions of cybersecurity risks in smart cities and proposing 24 mitigation strategies to face these security challenges. Furthermore, this study explores emerging cybersecurity issues to which smart cities are exposed by the increasing proliferation of new technologies and standards. [ABSTRACT FROM AUTHOR]

This article investigates the finite-time decentralized event-triggered feedback control problem for generalized neural networks (GNNs) with mixed interval time-varying delays and cyberattacks. A decentralized event-triggered method reduces the network transmission load and decides whether sensor measurements should be sent out. The cyber-attacks that occur at random are described employing Bernoulli distributed variables. By the Lyapunov-Krasovskii stability theory, we apply an integral inequality with an exponential function to estimate the derivative of the Lyapunov-Krasovskii functionals (LKFs). We present new sufficient conditions in the form of linear matrix inequalities. The main objective of this research is to investigate the stochastic finite-time boundedness of GNNs with mixed interval time-varying delays and cyber-attacks by providing a decentralized event-triggered method and feedback controller. Finally, a numerical example is constructed to demonstrate the effectiveness and advantages of the provided control scheme. [ABSTRACT FROM AUTHOR]

Advancements in digital technologies have significantly enhanced the functional capabilities of consumers and businesses alike, yet have concurrently amplified the complexities associated with cybersecurity, including theft and cyber-attacks. Consequently, auditing of information systems has emerged as a crucial security apparatus for organizations aiming to safeguard their data assets, specifically with respect to customer information. This study aims to design an information systems security and audit model that emphasizes the fortification of an organization's crucial assets via IT infrastructure security and information security management systems, in alignment with ISO 27001 standards. The proposed model seeks to assure information confidentiality, integrity, availability, and compliance with legal mandates. The study adopted the OCTAVE v2.0 method, executed in three distinct phases. In the first phase, profiles of asset-based threats were constructed. The second phase involved the identification of infrastructure vulnerabilities, whereas the final phase focused on the development of a security strategy and plans. The implementation of the proposed model yielded a marked impact, with a positive shift from 46% to 94% following the establishment of IT infrastructure security policies. The study underscores the importance of conducting a comparative analysis prior to implementation and asserts that well-defined and identified security models and information systems auditing can effectively counteract potential data leaks and cyber-attacks such as malware, phishing, spam, and ransomware. The findings suggest that a meticulous and preemptive approach to auditing and security planning can significantly bolster the resilience of an organization's digital infrastructure. [ABSTRACT FROM AUTHOR]

Abstract The Internet of Things (IoT) has revolutionized our world today by providing greater levels of accessibility, connectivity and ease to our everyday lives. It enables massive amounts of data to be traversed across multiple heterogeneous devices that are all interconnected. This phenomenon makes IoT networks vulnerable to various network attacks and intrusions. Building an Intrusion Detection System (IDS) for IoT networks is challenging as they enable a massive amount of data to be aggregated, which is difficult to handle and analyze in real time mainly because of the heterogeneous nature of IoT devices. This inefficient, traditional IDS approach accentuates the need to develop advanced IDS techniques by employing Machine or Deep Learning. This paper presents a deep ensemble-based IDS using Lambda architecture by following a multi-pronged classification approach. Binary classification uses Long Short Term Memory (LSTM) to differentiate between malicious and benign traffic, while the multi-class classifier uses an ensemble of LSTM, Convolutional Neural Network and Artificial Neural Network classifiers to detect the type of attacks. The model training is performed in the batch layer, while real-time evaluation is carried out through model inferences in the speed layer of the Lambda architecture. The proposed approach gives high accuracy of over 99.93% and saves useful processing time due to the multi-pronged classification strategy and using the lambda architecture.

A year after the start of the Russia-Ukraine war, the threat landscape influenced by cybercrime groups has seen further changes, and while some groups have declared allegiance to the Russian government, others have split over ideological differences or remained apolitical, opting to capitalize on geopolitical instability for financial gain. Affiliates of these cybercrime groups are actively involved in operations targeting entities and critical infrastructures of Ukraine, as well as countries that have declared their support for Ukraine, posing a threat to supporting states. This paper aims to highlight how financially motivated cybercrime actors capitalize on geopolitical instability and how they aid and abet Russian state interests, either by accident or on purpose. The objectives of the paper are to identify those cybercrime groups that use the ransomware attack or advanced persistent threat methods to carry out major cyber-attacks and how they changed their attack method after the outbreak of the conflict. The research methods used are qualitative, through document analysis and netnography, and the interpretation of the results is a justification of the involvement of cybercrime groups in this war. Netnography is used to analyse how these cybercrime groups discuss on public forums and groups, such as on Telegram, where they share all the information between members. In the NIS Directive are mentioned 7 sectors of economic activity that should be insured a common high level of security of networks and IT systems. In the main results of this research, we identify that cybercriminals groups attack all these main sectors, such as energy, transport, banking, infrastructures, health, and digital infrastructures. There were identified changes in malware-as-a-service and ransomware-as-a-service attacks, as well as changes in cybercriminal tactics and methods to orchestrate an attack. Also, when we refer to ransomware, LockBit, and CL0P groups are currently the most important cybercrime groups that carry out major cyber-attacks on countries from Europe. The information used in the research comes from open sources, mainly oriented toward those originating from the Russian language and those found in the public groups of the affiliates of these groups.

Self-driving cars are going to be the main future mode of transportation. However, such systems like, any other cyber-physical system, are vulnerable to attack vectors and uncertainties. As a response, resilience-based approaches are being developed. However, the approaches lack a sound attack model that recognizes the attack vectors and vulnerabilities such a system would have and that does a proper severity analysis of such attacks. Moreover, the existing attack models are too generic. Currently, the domain lacks such specific work pertaining to self-driving cars. Given the technology and architecture of self-driving cars, the field requires a domain-specific attack model. This paper gives a review of the attack models and proposes a domain-specific attack model for self-driving cars. The proposed attack model, severity-based analytical attack model for resilience (SAAMR), provides attack analysis based on existing models. Also, a domain-based severity score for attacks is calculated. Further, the attacks are classified using the decision-tree method and predictions of the type of attacks are given using long short-term memory network.

This study presents a novel adaptation of the Hoeffding Adaptive Tree (HAT) classifier with an instance selection algorithm that detects and identifies cyber and non-cyber contingencies in real time to enhance the situational awareness of cyber-physical power systems (CPPS). Wide-area monitoring, protection, and control (WAMPAC) systems allow system operators to operate CPPS more efficiently and reliably. WAMPAC systems use intelligent devices such as phasor measurement units (PMUs) to monitor the CPPS state. However, such devices produce continuous and unbounded data streams, posing challenges for data handling and storage. Moreover, WAMPAC devices and the communication links connecting them are vulnerable to cybersecurity risks. In this study, we consider several cyber and non-cyber contingencies affecting the physics and monitoring infrastructure of CPPS. Our proposed classifier distinguishes disturbances from cyberattacks using a novel instance selection algorithm with three algorithmic stages to ease data management. A cost and complexity analysis of the algorithm is discussed. With reduced computational effort, the classifier can handle high-velocity, high-volume, and evolving data streams from the PMUs. Six case studies with extensive simulation results corroborate the merits of the proposed classifier, which outperforms state-of-the-art classifiers. Moreover, the classifier demonstrated a high performance using a dataset outside the contingency detection domain. Finally, the real-time applicability of the proposed methodology is assessed, and its limitations are discussed.

Protection of computer systems and networks against malicious attacks is particularly important in industrial networked control systems. A successful cyber-attack may cause significant economic losses or even destruction of controlled processes. Therefore, it is necessary to test the vulnerability of process control industrial networks against possible cyber-attacks. Three approaches employing Generative Adversarial Networks (GANs) to generate fake Modbus frames have been proposed in this work, tested for an industrial process control network and compared with the classical approach known from the literature. In the first approach, one GAN generates one byte of a message frame. In the next two approaches, expert knowledge about frame structure is used to generate a part of a message frame, while the remaining parts are generated using single or multiple GANs. The classical single-GAN approach is the worst one. The proposed one-GAN-per-byte approach generates significantly more correct message frames than the classical method. Moreover, all the generated fake frames have been correct in two of the proposed approaches, i.e., single GAN for selected bytes and multiple GANs for selected bytes methods. Finally, we describe the effect of cyber-attacks on the operation of the controlled process.

In this article, we address the cyber-security problem of industrial control systems (ICSs) when their sensor measurements may be compromised due to an attacker who has intercepted those measurements via a network. We introduce a general-purpose method “Dynamic Watermarking (DW)” to detect potential cyber-intrusions on speed sensor measurements within industrial control systems, which deploy an adjustable speed drive (ASD) to control a critical process. The DW method is injecting a random private low-amplitude signal with a zero mean Gaussian distribution, “watermark”, into one of the input phase voltages powering the ASD system. The watermark signal propagates through the system including pulse width modulation (PWM) power conversion stage and motor, then ultimately appears in the speed sensor measurements. By deploying two statistical DW tests with two proper thresholds, the system can detect potential cyber-intrusions or unobservable cyber-attacks such as replay attacks and false data injection attacks (FDIA). The DW method tested on a laboratory-scale ASD system experimentally to protect the system against cyber-intrusions. This system, powered by a commercial PWM drive operating at 208 V, 3-phase, and 3.7 kW, served as our experimental platform.

Deep learning techniques have been widely adopted for cyber defence applications such as malware detection and anomaly detection. The ever-changing nature of cyber threats has made cyber defence a constantly evolving field. Smart manufacturing is critical to the broader thrust towards Industry 4.0 and 5.0. Developing advanced technologies in smart manufacturing requires enabling a paradigm shift in manufacturing, while cyber-attacks significantly threaten smart manufacturing. For example, a cyber attack (e.g., backdoor) occurs during the model’s training process. Cyber attack affects the models and impacts the resultant output to be misled. Therefore, this paper proposes a novel and comprehensive framework for smart cyber defence in deep learning security. The framework collectively incorporates a threat model, data, and model security. The proposed framework encompasses multiple layers, including privacy and protection of data and models. In addition to statistical and intelligent model techniques for maintaining data privacy and confidentiality, the proposed framework covers the structural perspective, i.e., policies and procedures for securing data. The study then offers different methods to make the models robust against attacks coupled with a threat model. Along with the model security, the threat model helps defend the smart systems against attacks by identifying potential or actual vulnerabilities and putting countermeasures and control in place. Moreover, based on our analysis, the study provides a taxonomy of the backdoor attacks and defences. In addition, the study provides a qualitative comparison of the existing backdoor attacks and defences. Finally, the study highlights the future directions for backdoor defences and provides a possible way for further research.

As the world evolves, becoming increasingly interconnected through digital technologies, there is a growing need for global collaboration in addressing the challenges of cyberspace. Cyber diplomacy, the use of diplomatic means to manage international relations in cyberspace, is emerging as a new field of international relations. With the advancement of cybercrime, cyberspace actors – governments, organizations, corporations, the private sector, and civil society need to collaborate, negotiate and develop cyber capabilities to ensure a safe digital space through cyber diplomacy. The article outlines the current state of cyberspace and critical threats to global security and stability, examining cybercrime, state-sponsored cyberattacks, cyberespionage, cyberterrorism, and trends in cybercrime. It focuses on the concept of cyber diplomacy and its expansion as a field of international relations, noting key developments that have contributed to this aspect. At the same time, the role of cyber diplomacy in shaping global norms, standards, and regulations for cyberspace is mentioned, and the potential advantages of better international cooperation in this field are explored.

Supervisory Control and Data Acquisition (SCADA) systems are widely used for remote monitoring and control of industrial processes, such as oil and gas production, power generation, transmission and distribution, and water treatment. Despite the enhanced accessibility, control, and data availability afforded by recent advances in communication technologies, the utilization of these technologies exposes critical infrastructures such as power systems to potential cyber threats. A Machine Learning (ML)-based Intrusion Detection System (IDS) seems promising; however, the development of ML models often requires custom methodologies for data preprocessing and training. This strategic approach is necessary for creating high-performance models that can be robustly evaluated and seamlessly integrated into real-time systems. As a result, we propose an ML-based IDS design framework for a SCADA-based power system incorporating effective modeling aspects, such as dataset preprocessing to ensure accurate representation, data augmentation for achieving a balanced dataset, automated feature selection to reduce dimensionality, and rigorous model training and testing procedures. To substantiate our proposed design framework, we conducted a series of experiments using a publicly available ORNL (Oak Ridge National Laboratory) dataset for a SCADA-based power system. The evaluation process encompasses efficient validation techniques with unseen data. Furthermore, the augmented dataset emerged through the aggregation of readings from four Phasor Measurement Units (PMUs) collected over a specific time span into a unified dataset. Among the assessed classifiers, the Random Forest (RF) model, trained on an augmented and balanced dataset, outperformed others, yielding an F1 score of 94.09% during testing with unseen data.

Autonomous driving (AD) has developed tremendously in parallel with the ongoing development and improvement of deep learning (DL) technology. However, the uptake of artificial intelligence (AI) in AD as the core enabling technology raises serious cybersecurity issues. An enhanced attack surface has been spurred on by the rising digitization of vehicles and the integration of AI features. The performance of the autonomous vehicle (AV)-based applications is constrained by the DL models' susceptibility to adversarial attacks despite their great potential. Hence, AI-enabled AVs face numerous security threats, which prevent the large-scale adoption of AVs. Therefore, it becomes crucial to evolve existing cybersecurity practices to deal with risks associated with the increased uptake of AI. Furthermore, putting defense models into practice against adversarial attacks has grown in importance as a field of study amongst researchers. Therefore, this study seeks to provide an overview of the most recent adversarial defensive and attack models developed in the domain of AD.

In the 21st century, there are many threats and challenges in the world. All this poses a significant threat to states and humanity. Also, new standards need to be developed in light of the growing threats from cyberspace. Worldwide studies and existing facts show that today’s defense mechanisms cannot adequately deal with the created situation – aggressor states very quickly adopt new technologies and use them to the detriment of other countries. That is, they pose a threat to other states. In this case, the main topic of our research is cyber-attacks on critical infrastructure, along with hard power and asymmetric threats. In many cases, it is the main cornerstone of starting a cyber war. There are facts when it is impossible to determine the origin of the cyber-attack, and the issue remains unclear. But there are also numerous examples of almost open cyber-attacks on critical infrastructure. There are confirmed facts from where the cyber-attack was carried out, which is internationally assessed as a cyber war.

This article is devoted to the state estimation issue under probability constraint for a class of time‐varying stochastic complex networks with uncertain inner couplings and cyber‐attacks. To save the communication energy, the dynamic event‐triggered strategy is deployed in each individual sensor‐to‐estimator channel to govern the data transmissions. A multi‐attack model incorporating two different deception signals is considered to describe the phenomenon of randomly occurring cyber‐attacks. The saturation‐type function is involved to reduce the possible innovation outliers resulting from the hostile deception signals. A sufficient condition is first developed to guarantee the probability H∞$$ {H}_{\infty } $$ performance constraint on estimation error dynamics over certain finite horizon, and then the suitable state estimator gains are derived by means of a set of matrix inequalities. Subsequently, a recursive algorithm is put forward for the addressed finite‐horizon state estimator design problem. Finally, the validity of the proposed state estimation scheme is testified by a numerical example. [ABSTRACT FROM AUTHOR]

This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes. This means that the attacker may not know the physical nature of the process; an LSTM network is sufficient to mislead the process operator. Our experimental studies were conducted in an industrial control network containing a magnetic levitation process. The model training, evaluation, and structure selection are described. The chosen LSTM network very well mimicked the considered process. Finally, based on the obtained results, we formulated possible protection methods against the considered types of cyber-attack. [ABSTRACT FROM AUTHOR]

Nowadays, with the increase in cyber-attacks, hacking, and data theft, maintaining data security and confidentiality is of paramount importance. Several techniques are used in cryptography and steganography to ensure their safety during the transfer of information between the two parties without interference from an unauthorized third party. This paper proposes a modern approach to cryptography and steganography based on exploiting a new environment: bases and protein chains used to encrypt and hide sensitive data. The protein bases are used to form a cipher key whose length is twice the length of the data to be encrypted. During the encryption process, the plain data and the cipher key are represented in several forms, including hexadecimal and binary representation, and several arithmetic operations are performed on them, in addition to the use of logic gates in the encryption process to increase encrypted data randomness. As for the protein chains, they are used as a cover to hide the encrypted data. The process of hiding inside the protein bases will be performed in a sophisticated manner that is undetectable by statistical analysis methods, where each byte will be fragmented into three groups of bits in a special order, and each group will be included in one specific protein base that will be allocated to this group only, depending on the classifications of bits that have been previously stored in special databases. Each byte of the encrypted data will be hidden in three protein bases, and these protein bases will be distributed randomly over the protein chain, depending on an equation designed for this purpose. The advantages of these proposed algorithms are that they are fast in encrypting and hiding data, scalable, i.e., insensitive to the size of plain data, and lossless algorithms. The experiments showed that the proposed cryptography algorithm outperforms the most recent algorithms in terms of entropy and correlation values that reach −0.6778 and 7.99941, and the proposed steganography algorithm has the highest payload of 2.666 among five well-known hiding algorithms that used DNA sequences as the cover of the data. [ABSTRACT FROM AUTHOR]

Under connected vehicles environment, we proposed a new coupled map car-following model integrating cyber-attacks on the continuous delay effect involving the relative velocity historical integral. Also, the stable conditions are obtained through control theory, which is related to cyber-attacks on the continuous delay effect. Furthermore, numerical simulations are executed to corroborate theoretical analysis for the new coupled map car-following model. From simulation results, the continuous delay effect during normal transmission plays a positive role on the traffic stability and decreases pollutants emissions in coupled map car-following model under connected vehicles environment. However, traffic stability would be damaged and a lot of pollutants emissions would be produced if the transmission of the continuous delay effect is subjected to cyber-attacks between connected vehicles. Moreover, a compensation algorithm is designed to resist the potential harm caused by cyber-attacks in the new coupled map car-following model under connected vehicles environment. More importantly, the stability of the traffic flow can be maintained and the pollutants emissions falls down by adopting the compensation algorithm to boycott the negative impact of cyber-attacks in the new coupled map car-following model. [ABSTRACT FROM AUTHOR]

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The digital age has fundamentally transformed the nature of diplomacy. As countries embrace the opportunities of the digital realm to foster global relationships and promote their interests, they simultaneously confront an array of cyber threats and challenges. The inherent dynamism of this digital transformation challenges the age-old tenets of diplomacy, prompting a re-evaluation of traditional methodologies and strategies. Countries today are presented with a dual-edged sword. On one hand, digital tools serve as potent enablers, facilitating the forging of global relationships, enhancing soft power projection, and promoting nuanced national interests with unparalleled efficiency. Yet, the same arena also poses substantial challenges: cyber threats, misinformation campaigns, and intricate webs of state-backed digital espionage represent just the tip of a vast iceberg of challenges in the cyber domain. This research paper seeks to elucidate the evolving landscape of digital diplomacy, assessing its implications for traditional statecraft and international relations. By examining the dual nature of digitalisation -- it's potential for both collaboration and conflict -- we aim to provide a comprehensive understanding of the strategies and tactics employed by state actors. This exploration offers insights into the future of diplomacy, advocating for adaptive, resilient, and innovative approaches to navigate the challenges and harness the opportunities of the cyber era. [ABSTRACT FROM AUTHOR]

Healthcare Industry plays a pivotal role in every one's life and with rapid advancements in cyber-attack vectors, threat actors and their strategies it has in-need created a necessity and a challenge to the numerous organizations and to the Governments as well, to stand guard and secure the institutes and the data stored with them. To design a secure healthcare system involves several considerations to protect sensitive patient data and ensure the confidentiality, integrity, and availability of information. Lot of work has been published on cyber security along with importance of protecting the Personally Identifiable Information (PII) and patient health records stored in hospitals, and also comparisons were made between paid or licensed tools and open source; however, implementation of the tools in real time was not in place where the financial limitations are a real concern and security is a need. This paper has given insight into important parameters such as risk assessment and security policies etc. [ABSTRACT FROM AUTHOR]

The growing prevalence and impact of cyber-attacks have led many countries to rank cybersecurity failure as a top risk. Honeypots offer a means to detect attacks and enhance security measures by enticing attackers to compromised devices and collecting data during their interactions. Although Artificial Intelligence (AI) has the potential to strengthen cybersecurity by detecting attacks more quickly and accurately, its adoption in practice remains limited. This project was developed to address the increasing number of cyber-attacks in the era of cloud computing and remote work. The study employed a unique methodology of using AI and Machine Learning to identify patterns in data and improve security measures. The research focused on SSH attacks, which involved mass scanning, brute force attacks, reconnaissance commands, and file uploads. The data extracted from the Cowrie log files was heterogeneous, making it challenging to analyze and utilize for training a machine learning model. To address this, feature engineering was performed to create new features and transform existing ones. The study shifted from a binary classification of traffic to analyzing the behaviour of attackers and predicting their next moves. The machine learning algorithm used was LSTM, which achieved an accuracy of 98% after tuning the parameters. The study concluded that AI could ease the burden on SOC analysts and allow them to be more productive by learning adaptively and finding new patterns that could speed up the process of identifying, containing, and responding to attacks. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy of network attack prediction and long response time of attack detection, bidirectional long short-term memory (BiLSTM) was used to predict network attacks. However, BiLSTM has the problems of difficulty in parameter setting and low accuracy of the prediction model. This paper first proposes the Improved Grey Wolf algorithm (IGWO) to optimize the BiLSTM (IGWO-BiLSTM). First, IGWO uses Dimension Learning Hunting (DLH) strategy to construct the wolf neighborhood. In the established wolf neighborhood, the BiLSTM parameters are iteratively optimized to obtain a prediction model with fast convergence speed and small reconstruction error. Secondly, the dataset is preprocessed, and the IP packet statistical signature (IPDCF) is defined according to the characteristics of denial of service (DOS) and distributed denial of service (DDOS) attacks. IPDCF was used to establish the time series model and network traffic time series data were input into IGWO-BiLSTM to get the prediction results. Finally, the DOS and DDOS network packets were input into the trained prediction model to obtain the prediction results of attack data. By comparing the predicted values of IGWO-BiLSTM normal network packets and attack packets, a reasonable threshold is set to provide the basis for the subsequent attack prediction. Experiments show that the IGWO-BiLSTM can reach 99.05% of the fitting degree and accurately distinguish network attacks from normal network demand increases. [ABSTRACT FROM AUTHOR]

The healthcare industry has recently shown much interest in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a component of the IoTs in which medical appliances transmit information to communicate critical information. The growth of the IoMT has been facilitated by the inclusion of medical equipment in the IoT. These developments enable the healthcare sector to interact with and care for its patients effectively. Every technology that relies on the IoT can have a serious security challenge. Critical IoT connectivity data may be exposed, changed, or even made unavailable to authenticated users in the case of such attacks. Consequently, protecting IoT/IoMT systems from cyber-attacks has become essential. Thus, this paper proposes a machine-learning- and a deep-learning-based approach to creating an effective model in the IoMT system to classify and predict unforeseen cyber-attacks/threats. First, the dataset is preprocessed efficiently, and the Harris Hawk Optimization (HHO) algorithm is employed to select the optimized feature. Finally, machine learning and deep learning algorithms are applied to detect cyber-attack in IoMT. Results reveal that the proposed approach achieved an accuracy of 99.85%, outperforming other techniques and existing studies. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is rapidly expanding and becoming an integral part of daily life, increasing the potential for security threats such as malware or cyberattacks. Many embedded systems (ESs), responsible for handling sensitive data or facilitating secure online activities, must adhere to stringent security standards. For instance, payment processors employ security-critical components as distinct chips, maintaining physical separation from other network components to prevent the leakage of sensitive information such as cryptographic keys. Establishing a trusted environment in IoT and ESs, where interactions are based on the trust model of communication nodes, is a viable approach to enhance security in IoT and ESs. Although trust management (TM) has been extensively studied in distributed networks, IoT, and ESs, significant challenges remain for real-world implementation. In response, we propose a hybrid fuzzy rule algorithm (FRA) and trust planning mechanism (TPM), denoted FRA + TPM, for effective trust management and to bolster IoT and ESs reliability. The proposed system was evaluated against several conventional methods, yielding promising results: trust prediction accuracy (99%), energy consumption (53%), malicious node detection (98%), computation time (61 s), latency (1.7 ms), and throughput (9 Mbps). [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems. [ABSTRACT FROM AUTHOR]

Wide-Area Power System Stabilizers (WAPSSs) are damping controllers used in power systems that employ data from Phasor Measurement Units (PMUs). WAPSSs are capable of providing high damping rates for the low-frequency oscillation modes, especially the inter-area modes. Oscillation modes can destabilize power systems if they are not correctly identified and adequately damped. However, WAPSS communication channels may be subject to failures or cyber-attacks that affect their proper operation and may even cause system instability. This research proposes a method based on an optimization model for the design of a WAPSS robust to multiple permanent communication failures. The results of applications of the proposed method in the IEEE 68-bus system show the ability of the WAPSS design to be robust to a possible number of permanent communication failures. Above this value, the combinations of failures and processing time are high and they make it difficult to obtain high damping rates for the closed-loop control system. The application and comparison of different optimization techniques are valid and showed a superior performance of the Grey Wolf Optimizer in solving the optimization problem. [ABSTRACT FROM AUTHOR]

Wearable technology, sensor networks, and home utilities are just a few of the businesses where the Internet of Things (IoT) is spreading quickly. With the development of the IoT, billions of gadgets are now connected to the internet and exchanging data. The proliferation of IoT devices has increased the number of IoT-based cyberattacks. In 2016 a massive denial of service (DDOS) cyber-attack was lunched utilizing infected internet of things devices a major website including Netflix and CNN was shutdown. Therefore, new ways for recognizing threats posed by hacked IoT nodes must be developed to overcome this concern. In that same context, ML and DL approaches are the best appropriate investigative control solution against IoT device-based intrusions. The point of the study is to offer a complete grasp of the IoT system-relevant technologies, standards, architecture, and the increasing dangers from corrupted IoT gadgets and an introduction to intrusion detection systems. Additionally, this research focuses on deep learning-based solutions for identifying IoT devices susceptible to cyber-attacks. The detection rate provided by deep learning algorithms shows promising results which reached 99% detection accuracy in some cases. [ABSTRACT FROM AUTHOR]