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Nowadays, the need for cloud computing has increased due to the exponential growth in information transmission. Cybercriminals are persistent in their efforts to breach cloud environments, even with security measures in place to protect data stored in the cloud. To address this challenge, an enhanced authentication approach is needed for enhanced security. In order to protect user privacy and anonymity in cloud environments, the study presents a novel technique called Hyperelliptic Curve-based Anonymous Ring Signature (HCARS). Moreover, Blockchain technology is utilized to securely record timestamps and cryptographic keys. The hashing functions in the Blockchain system employ SHA 256 and SHA 512 algorithms. Furthermore, utilizing Ring Learning with Error (RLWE) problems, an Nth degree Truncated Polynomial Ring Units (NTRU)-Based Fully Homomorphic Encryption (NTRU-FHE) Scheme encrypts sensitive data and ensures its integrity. A comparative study between the proposed method and current approaches is done through experimental verification utilizing Java. The results demonstrate that the proposed approach outperforms existing techniques, achieving an encryption time of 6.75 s for an input size of 75 and a decryption time of 5.128 s for the same input size. Similarly, the signature generation time is 125 ms for 100 received messages, block generation time of 10.8 s for 450 blocks, throughput of 98 MB/sec for a record size of 16,384, and total computational time of 403 ms for 20 messages. The results demonstrate the superior performance of the HCARS approach, with significantly reduced encryption, decryption, and signature generation times, as well as improved throughput and computational efficiency. Securing the security and privacy of cloud-based systems in the face of changing cyber threats has been made much easier with the help of the HCARS approach. [ABSTRACT FROM AUTHOR]

The article is devoted to a comprehensive and holistic analysis of the legislative framework of Ukraine in the field of cybersecurity. On the basis of a systematic review of normative legal acts, the main trends in the development of legislation have been identified, their compliance with modern challenges and international standards has been analyzed. In addition, it should be noted that attention is paid to the impact of Russian aggression, which continues from the annexation of the Crimea and the occupation of Luhansk and part of Donetsk regions, as well as from the day of the full-scale invasion of the Russian Federation into the territory of our sovereign state to changes in the legislation. The study allows to assess the effectiveness of the current legislation and formulate proposals for its improvement and improvement. The results of the study can be used by state authorities, enterprises and organizations to increase the level of cyber defense and ensure the national security of Ukraine as a whole. The process of formation and formation of the legal framework for ensuring cybersecurity in Ukraine does not stop, but continues to this day, as it is a slow process. Despite the fact that the main and main vectors of state policy in this area have already been defined and outlined, the legislation still needs further improvement to adequately and fully respond to modern cyber threats. The challenges of the digital world require constant updating of the regulatory framework. We believe that in order to further develop and improve legislation in the field of cybersecurity, it is necessary: to improve the regulatory framework taking into account the constant dynamics of cyberspace, also to introduce effective mechanisms of legal protection against cyber threats that constantly exist, especially due to aggression by the Russian Federation, in addition, it is necessary to increase the level of cyber defense of critical infrastructure. Also, in our opinion, it is important and important to ensure the harmonization of national legislation with international standards. We also consider it appropriate to develop innovative, modern legal tools to protect information resources that exist in our country as a whole. That is, a deep analysis of these issues contributes to a comprehensive understanding of cybersecurity problems and allows us to develop effective measures to protect the information space of Ukraine. [ABSTRACT FROM AUTHOR]

The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users' behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules. [ABSTRACT FROM AUTHOR]

This article presents an asynchronous event‐triggered scheme for switched Takagi–Sugeno (T–S) fuzzy systems against dual‐channel hybrid cyber attacks. Different from existing results, both sensor and controller channels are subjected to aperiodic denial‐of‐service attacks and random false data injection attacks. To efficiently utilize dual‐channel network communication resources while resisting hybrid attacks, two resilient event‐triggered mechanisms (ETMs) are constructed. Considering asynchronous ETMs and hybrid cyber attacks, the time‐delay switched T–S fuzzy system is derived by utilizing model transformation methods. Thereby, the stability conditions are derived by utilizing multiple Lyapunov functions technique, and slack matrices are introduced to further relax the conditions. Finally, two examples are given to demonstrate the effectiveness of the developed event‐based security control strategy. [ABSTRACT FROM AUTHOR]

This study introduces a Deep Reinforcement Learning approach (DRL-MD) aimed at optimizing the deployment of mitigations to minimize redundancy while ensuring effective defense against cyberattacks. DRL-MD initially enhances ATT &CK (Adversarial Tactics, Techniques, and Common Knowledge) to underscore the formal relationships between attacks and defenses. Over the enhanced ATT &CK, DRL-MD then operates in two phases: (1) Estimating Node Importance: DRL-MD proposes a model to estimate the importance of deployed nodes in the network, prioritizing mitigation deployment locations for better evaluation of mitigation effectiveness; and (2) Optimizing Mitigation Deployment: A Soft Actor-Critic algorithm finds the optimal mitigation deployment policy through multi-objective optimization of the importance of deployed nodes, the effectiveness of mitigations in preventing cyberattacks, vulnerability repair, and deployment cost. A case study with DRL-MD against the state-of-the-art counterparts has been performed considering the WannaCry threat, and results indicate that: (1) DRL-MD performs the best with 6.4–11% decrease in deployment cost; and (2) DRL-MD can significantly reduce redundancy in mitigation deployments, which partially benefits from the enhanced ATT &CK model. Overall, a comprehensive solution of mitigation deployment has been fostered to significantly lower the redundancy with more effective defenses against cyberattacks sustained. [ABSTRACT FROM AUTHOR]

Deep learning plays a significant role in developing a robust and constructive framework for tackling complex learning tasks. Consequently, it is widely utilized in many security-critical contexts, such as Self-Driving and Biometric Systems. Due to their complex structure, Deep Neural Networks (DNN) are vulnerable to adversarial attacks. Adversaries can deploy attacks at training or testing time and can cause significant security risks in safety–critical applications. Therefore, it is essential to comprehend adversarial attacks, their crafting methods, and different defending strategies. Moreover, finding effective defenses to malicious attacks that can promote robustness and provide additional security in deep learning models is critical. Therefore, there is a need to analyze the different challenges concerning deep learning models' robustness. The proposed work aims to present a systematic review of primary studies that focuses on providing an efficient and robust framework against adversarial attacks. This work used a standard SLR (Systematic Literature Review) method to review the studies from different digital libraries. In the next step, this work designed and answered several research questions thoroughly. The study classified several defensive strategies and discussed the major conflicting factors that can enhance robustness and efficiency. Moreover, the impact of adversarial attacks and their perturbation metrics are also analyzed for different defensive approaches. The findings of this study assist researchers and practitioners in choosing an appropriate defensive strategy by incorporating the considerations of varying research issues and recommendations. Finally, relying upon reviewed studies, this work found future directions for researchers to design robust and innovative solutions against adversarial attacks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) nodes consist of sensors that collect environmental data and then perform data exchange with surrounding nodes and gateways. Cybersecurity attacks pose a threat to the data security that is being transmitted in any IoT network. Cryptographic primitives are widely adopted to address these threats; however, the substantial computation demands limit their applicability in the IoT ecosystem. In addition, each IoT node varies with respect to the area and throughput (TP) requirements, thus demanding flexible implementation for encryption/decryption processes. To solve these issues, this work implements the NIST lightweight cryptography standard, Ascon, on a SAED 32 nm process design kit (PDK) library by employing loop folded, loop unrolled and fully unrolled architectures. The fully unrolled architecture can achieve the highest TP but at the cost of higher area utilisation. Unrolling by a lower factor results in lower area implementations, enabling the exploration of design space to tackle the trade-off between area and TP performance of the design. The implementation results show that, for loop folded architecture, Ascon-128 and Ascon-128a require 36.7k μ m 2 and 38.5k μ m 2 chip area, respectively compared to 277.1k μ m 2 and 306.6k μ m 2 required by their fully unrolled implementations. The proposed implementation strategies can adjust the number of rounds to accommodate the varied requirements of IoT ecosystems. An implementation with an open-source 45 nm PDK library is also undertaken for enhanced generalization and reproducibility of the results. [ABSTRACT FROM AUTHOR]

In an era dominated by network connectivity, the reliance on robust and secure networks has become paramount. With the advent of 5G and the Internet of Things, networks are expanding in both scale and complexity, rendering them susceptible to a myriad of cyber threats. This escalating risk encompasses potential breaches of user privacy, unauthorized access to transmitted data, and targeted attacks on the underlying network infrastructure. To safeguard the integrity and security of modern networked societies, the deployment of Network Intrusion Detection Systems is imperative. This paper presents a novel lightweight detection model that seamlessly integrates Spiking Neural Networks and Convolutional Neural Networks with advanced algorithmic frameworks. Leveraging this hybrid approach, the proposed model achieves superior detection accuracy while maintaining efficiency in terms of power consumption and computational resources. This paper presents a new style recognition model that seamlessly integrates spiking neural networks and convolutional neural networks with advanced algorithmic frameworks. We call this combined method Spiking-HCCN. Using this hybrid approach, Spiking-HCCN achieves superior detection accuracy while maintaining efficiency in terms of power consumption and computational resources. Comparative evaluations against state-of-the-art models, including Spiking GCN and Spike-DHS, demonstrate significant performance advantages. Spiking-HCCN outperforms these benchmarks by 24% in detection accuracy, 21% in delay, and 29% in energy efficiency, underscoring its efficacy in fortifying network security in the face of evolving cyber threats. [ABSTRACT FROM AUTHOR]

In the age characterized by relentless cyber threats, the need for innovative and proactive security measures has never been more important. Deception is defined as the deliberate structure of tricks, traps, and false information to mislead and discourage threats, while providing timely warning signals and useful information to defenders. The two-tier deception-driven security model's implementation focuses on applying deception security techniques to deceive potential attackers and protect network resources, with an emphasis on a proactive defense approach. The study emphasized the deployment and deep testing of the model, which aims to assess its efficacy and feasibility in real-time practice. The study shows that the two-layered approach effectively defends the network within the multiple layers using a combination of decoys, honeypots, and deceptive network segments. The deception security model effectively prevents and confuses potential threats, improving the network's overall resilience and threat defense capabilities. The findings suggest that integrating deception techniques into cybersecurity frameworks can provide a robust layer of protection against evolving cyber threats. Furthermore, this research contributes to the ongoing discourse on proactive cybersecurity strategies and offers practical insights for improving network defense mechanisms. [ABSTRACT FROM AUTHOR]

Purpose: Despite the proliferation of cyberthreats upon the supply chain (SC) at large, knowledge on SC cybersecurity is scarce and predominantly conceptual or descriptive. Addressing this gap, this research examines the effect of SC cyber risk management strategies on integration decisions for cybersecurity (with suppliers, customers, and internally) to enhance the SC's cyber resilience and robustness. Design/methodology/approach: A research model grounded in the supply chain risk management (SCRM) literature, with roots in the Dynamic Capabilities View and the Relational View, was developed. Survey responses of 388 SC managers at US manufacturers were obtained to test the model. Findings: An impact of SC cyber risk management strategies on internal cyber integration was detected, which in turn impacted external cyber integration with both suppliers and customers. Further, a positive effect of internal and customer cyber integration on both cyber resilience and robustness was found, while cyber integration with suppliers impacted neither. Practical implications: Industry practitioners may adapt certain risk management and integration strategies to enhance the cybersecurity posture of their SCs. Originality/value: This research bridges between the established domain of SCRM and the emergent field of SC cybersecurity by forming and testing novel relationships between SCRM-rooted constructs tailored to an SC cyber risks context. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) has revolutionized various fields by enabling seamless connectivity and data exchange among numerous devices. However, this interconnectivity introduces significant security challenges, particularly in ensuring data confidentiality, integrity, and authenticity. This study proposes a novel secure open standard framework for IoT applications, addressing these challenges through the integration of elliptic curve cryptography (ECC) and fog computing. The framework consists of three core components: secure device registration, data encryption within the fog gateway, and a robust mechanism for detecting man-in-the-middle (MITM) attacks. The unique aspect of the proposed method lies in its comprehensive approach to IoT security. Utilizing ECC, the framework ensures secure communication among resource constrained IoT devices, balancing encryption strength and efficiency. The integration of fog computing reduces latency and enhances processing efficiency by offloading intensive tasks from IoT devices to the fog layer. The MITM attack detection mechanism continuously monitors cryptographic keys and communication patterns, providing an additional layer of security against advanced cyber threats. The system was implemented and evaluated using the NS-3.26 network simulator and Python for data visualization. The experimental setup included 100 IoT devices, 25 users, a fog gateway, a datacenter, and a cloud server. Results demonstrate the framework's scalability and efficiency, with consistent throughput increases and balanced power consumption across varying IoT device numbers. [ABSTRACT FROM AUTHOR]

Cyber-physical system (CPS) is an integral part of an internet of things (IoT) with established wide spread applications. An increasing concern towards being highly vulnerable to various forms of dynamic cyber-attacks has been increasingly evolving. A review of existing research methodology showcases complex solutions that can offer sub-optimal security strength when exposed to dynamic cyber-attack forms while increasing the computational burden. Therefore, this manuscript presents a novel yet simplified computational framework capable of determining and resisting critical anomalies within internet-of-cyber physical systems (IoCPS). The presented scheme contributes towards preprocessing following a distinct oversampling method targeting balancing the data. An ensemble machine learning model using a discrete variant of AdaBoost and neural decision tree (NDT) has been implemented to optimize the learning process and improve the threat detection efficiency. The comparative outcome of the proposed study showcases that it offers approximately 7.2% increased threat detection accuracy and approximately 68% reduced response time compared to frequently adopted learning mechanisms towards threat detection over an IoT environment. [ABSTRACT FROM AUTHOR]

This study explored the integration of blockchain and artificial intelligence technologies to enhance the security framework of electronic voting (e-voting) systems. Amid increasing vulnerabilities and cyber threats to electoral integrity, these technologies provided robust solutions by ensuring the immutability of voting records and enabling real-time anomaly detection. Blockchain technology secured votes in a decentralized, tamper-proof ledger, preventing unauthorized modifications, and enhancing transparency. Concurrently, artificial intelligence leveraged predictive analytics to dynamically monitor and respond to potential security threats, thereby ensuring the reliability and integrity of the voting process. This paper presented a dual-technology approach where blockchain's transparency complemented artificial intelligence's (AI) threat detection capabilities, providing a comprehensive security solution for e-voting systems. Through theoretical models and empirical data, we demonstrated significant improvements in transaction throughput, threat detection accuracy, and system scalability. The findings suggested that the strategic application of these technologies could substantially mitigate current e-voting vulnerabilities, offering a pathway to more secure, transparent, and efficient electoral processes globally. [ABSTRACT FROM AUTHOR]

Adversarial attacks pose a significant threat to real-world applications based on deep neural networks (DNNs), especially in security-critical applications. Research has shown that adversarial examples (AEs) generated on a surrogate model can also succeed on a target model, which is known as transferability. Feature-level transfer-based attacks improve the transferability of AEs by disrupting intermediate features. They target the intermediate layer of the model and use feature importance metrics to find these features. However, current methods overfit feature importance metrics to surrogate models, which results in poor sharing of the importance metrics across models and insufficient destruction of deep features. This work demonstrates the trade-off between feature importance metrics and feature corruption generalization, and categorizes feature destructive causes of misclassification. This work proposes a generative framework named PTNAA to guide the destruction of deep features across models, thus improving the transferability of AEs. Specifically, the method introduces path methods into integrated gradients. It selects path functions using only a priori knowledge and approximates neuron attribution using nonuniform sampling. In addition, it measures neurons based on the attribution results and performs feature-level attacks to remove inherent features of the image. Extensive experiments demonstrate the effectiveness of the proposed method. The code is available at https://github.com/lounwb/PTNAA. [ABSTRACT FROM AUTHOR]

Advanced Persistent Threat (APT) attack is one of the most dangerous cyber-attack techniques nowadays. Therefore, the issue of detecting and predicting the spread of APT malware in the network is a very urgent issue to help the process of preventing this attack effectively. In this paper, we propose a new approach that is capable of predicting the spread of APT malware in the network based on the APT's own behaviors. Accordingly, to predict the spread of APT malicious code in the system, we propose to use a combination of two single Susceptible‐Infected‐Recovered (SIR) models. Specifically, the first SIR model was built to predict the spread of APT malicious code to devices and computers within the organization. These devices and computers are often used by APT malicious code as a basis to escalate privileges to devices or computers containing important and sensitive information of the organization. The second SIR model has the function of predicting the spread of APT malware to a group of computers containing sensitive information or potentially causing high risks to the organization. The two SIR models will provide information about infections between computer groups in the system to help accurately predict the spread of APT malware in the system. The proposal to combine two SIR models in the article is a new proposal based on the behavior of APT malware in practice. By combining two SIR models, the proposal in this article has opened up a new approach for a number of problems predicting the spread in the internet such as malicious code in wireless sensor networks or malicious information on the social network. [ABSTRACT FROM AUTHOR]

Despite stringent regulatory scrutiny and increased cybersecurity spending, data breaches and cyberattacks have persisted, resulting in dire socioeconomic consequences. Particularly affected is the automotive industry, where original equipment manufacturers (OEMs) have implemented effective strategies against cyberattacks. Limiting the vulnerability of connected and autonomous vehicles to cyberattacks requires an expanded suite of strategies over and above technological safeguards. This study, guided by the National Institute of Standards and Technology cybersecurity framework, aimed to answer two questions: First, how do leaders of automotive OEMs describe and interpret the use of cybersecurity frameworks to prevent cyberattacks? Second, how does the perceived impact of potential cyberattacks influence the cybersecurity framework used by automotive OEM leaders? Using purposeful sampling, 20 automotive OEM leaders in the cybersecurity field responded to questionnaires and interviews to reveal three insights: (a) The automotive OEMs must reinforce bundled risk management frameworks because attackers will continue to exploit human vulnerabilities to gain access to secured systems, and this calls for reducing human vulnerabilities by understanding employee behaviors; (b) The leaders have to embrace open-threat intelligence through information sharing. Instructive for policy-making is the continued advocacy for threat intelligence-sharing platforms that are transparent and timely; (c) Uniquely insightful is leveraging blockchain technology to manage and securely track CAVs and their components enhances the automotive OEMs' ability to maintain cyber-defensible CAV assets. [ABSTRACT FROM AUTHOR]

Purpose: Disruptive events caused by cyber incidents, such as supply chain (SC) cyber incidents, can affect firms' SC operations on a large scale, causing disruptions in material, information and financial flows and impacting the availability, integrity and confidentiality of SC assets. While SC resilience (SCRES) research has received much attention in recent years, the purpose of this study is to investigate specific capabilities for building SCRES to cyber risks. Based on a nuanced understanding of SC cyber risk characteristics, this study explores how to build SC cyber resilience (SCCR) using the perspective of dynamic capability (DC) theory. Design/methodology/approach: Based on 79 in-depth interviews, this qualitative study examines 28 firms representing 4 SCs in Central Europe. The researchers interpret data from semistructured interviews and secondary data using the DC perspective, which covers sensing, seizing and transforming. Findings: The authors identify SCRES capabilities, in general, and SCCR-specific capabilities that form the basis for the realignment of DCs for addressing cyber risks in SCs. The authors argue that SCRES capabilities should, in general, be combined with specific capabilities for SCCR to deal with SC cyber risks. Based on these findings, 10 propositions for future research are provided. Practical implications: Practitioners should collaborate specifically to address cyber threats and risks in SCs, integrate new SC partners and use new approaches. Furthermore, this study shows that cyber risks need to be treated differently from traditional SC risks. Originality/value: This empirical study enriches the SC management literature by examining SCRES to cyber risks through the insightful lens of DCs. It identifies DCs for building SCCR, makes several managerial contributions and is among the few that apply the DC approach to address specific SC risks. [ABSTRACT FROM AUTHOR]

Every modern socially active person in Ukraine uses mobile devices and has access to the Internet, state institutions have switched to electronic document management, the stable operation of the banking sector, railways, air transport and large companies depends on the stability of the cyberspace in which they work and rely on communication using electronic means. In the conditions of martial law, the activities of state authorities and local self-government bodies are reduced to assisting the governing bodies created in individual regions in the direct performance of national defense tasks. In the system of state authorities operating under martial law, internal affairs bodies occupy a special place due to the nature of the tasks they perform under these operational conditions. Effectiveness of internal affairs bodies is vital for the direct provision of internal security. It is for this purpose that it is necessary to clearly define the competence of internal affairs bodies. After all, only the presence of certain powers ensures compliance with legality in the performance of functions by services and divisions of internal affairs bodies. Of course, for the successful implementation of the tasks assigned to the Ministry of Internal Affairs under martial law, the duties and rights of the Ministry of Internal Affairs must correspond to the basic principle that new duties correspond to new rights. It is very important to include in the system of coercive measures the expansion of the powers of internal affairs bodies during the introduction of martial law. Internal security officers cannot go beyond the permitted methods and means of carrying out security operations. In our opinion, the scope of powers of the employee of internal affairs bodies in such cases should be expanded. The complexity of the powers of the Ministry of Internal Affairs is reflected in the internal management of various instit utions. The legislation of Ukraine on criminal responsibility, which was developed for the needs of peacetime, turned out to be insufficiently effective in the field of combating crime in the situation of armed aggression of the Russian Federation. Such a situation necessitated the urgent adaptation of the legislation of Ukraine to the conditions of martial law. [ABSTRACT FROM AUTHOR]

The article is devoted to analyzing threats to Ukraine's critical infrastructure. The relevance of the study is driven by the dangerous dynamics of threat levels that affect national security and the socioeconomic stability of the state under martial law. A review of the scientific literature revealed that there is currently no unified approach to the classification and assessment of such threats. Therefore, the article aims to analyze existing classifications of threats to critical infrastructure and propose an original classification of their manifestations on critical infrastructure objects under martial law, considering both international and national experiences. During the research, several tasks were accomplished: the essence of the term «threat to critical infrastructure» was defined; an analysis of legal documents related to the protection of critical infrastructure was conducted; domestic and international experiences in threat classification were studied; principles underlying both domestic and foreign classifications were reviewed and their effectiveness was assessed; and an original classification of threats to critical infrastructure for Ukraine was developed. The results of the study revealed that there are various classifications of threats to critical infrastructure today. However, in our opinion, the most effective classification that meets modern challenges is one that considers all forms of their manifestations: physical assaults, cyberattacks, economic sabotage, terrorist actions, and assaults using climate weapons. This classification of threats to Ukraine's critical infrastructure is developed based on international and domestic experience and is of great importance for enhancing national security, as its value lies in the systematization of theoretical knowledge and a deep analysis of modern threats facing the state. The main scientific value of the proposed classification lies in its ability to integrate different aspects of threats into a single classification that takes into account both internal and external factors. This allows for a more accurate risk assessment and the implementation of appropriate measures to protect national critical infrastructur e objects. [ABSTRACT FROM AUTHOR]

In today's digital world, where the amount of information that is stored and processed in cyberspace is constantly growing, the issue of personal data protection is becoming more and more relevant. Personal data, such as financial information, medical records, and social media data, is a valuable resource that can be used for both legitimate and malicious purposes. Despite the continuous improvement of security technologies, the growing complexity of cyber threats and the evolution of cyber attack methods create new challenges for the protection of personal information. In this connection, there is a need to analyze the modern problems of personal data protection, in particular the legal, technical and ethical aspects of this issue. Ensuring the security of personal data remains an urgent problem today, because more and more people use it. On the Internet, they pass authorization on various resources, using their personal data for this purpose, and more and more different institutions create peculiar databases, not always taking care of their safety. The insufficient measure of ensuring the security of personal data in cyberspace leads to excessive activity of cyberattacks and the commission of various manipulations with personal data. The relevance of the topic of personal data protection in cyberspace is due to several important factors that have a significant impact on modern society, economy and security. The main aspects that emphasize the relevance of this issue: the growth of digitalization; cyber threats; legal requirements; social responsibility and trust; economic consequences; technological challenges; international relations and geopolitics. Thus, the relevance of the topic of personal data protection in cyberspace is due to the complexity and global nature of this issue, as well as its importance for preserving privacy, security and trust in the digital world. This article is aimed at researching key challenges in the field of personal data protection in cyberspace, as well as providing recommendations for increasing the level of security and minimizing the risks of information leakage. [ABSTRACT FROM AUTHOR]

This article comprehensively analyzes Israel's experience in the legal regulation of cybersecurity and explores its potential adaptation for Ukraine. Israel is a leading cybersecurity nation due to its integrated approach to cyber protection. The paper examines key legislative acts and organizational measures implemented in Israel, such as establishing specialized cyber units like TEHILA and the Israel National Cyber Directorate (INCD), and coordination centers that facilitate cohesive efforts between governmental and private organizations. The article highlights the significance of incorporating cybersecurity mechanisms into Israel's national defense strategy and the active involvement of the private sector, including numerous tech companies and startups, in safeguarding information infrastructure. The crucial role of international cooperation, particularly with the EU and the USA, in developing an effective cyber strategy is also discussed, emphasizing its relevance for Ukraine amidst the ongoing conflict with Russia. Additionally, the article delves into the continuous improvement of cybersecurity technologies and the professional development of specialists in this field. It highlights the importance of a holistic approach encompassing technological advancements and strategic planning. The paper also emphasizes the need for interagency coordination and the collaboration with international partners to elevate the overall cybersecurity posture. Key aspects of adapting Israeli approaches to Ukrainian realities are outlined, including the necessity of establishing specialized bodies, developing a national cybersecurity strategy, and engaging the private sector in cybersecurity efforts. By integrating Israel's best practices, Ukraine can significantly bolster its cyber defense capabilities, enhancing its ability to respond to cyber threats more effectively and protecting its critical infrastructures. Furthermore, the study addresses legal regulation issues, including both the regulatory framework and practical aspects of implementing cybersecurity measures at national and local levels. Continuous monitoring and evaluation of the effectiveness of these measures are essential to identify and ne utralize potential threats timely. [ABSTRACT FROM AUTHOR]

Attacks on IoT devices are increasing day by day. Since IoT devices nowadays have become an integral part of our daily lives, the data gathered from IoT devices benefits intruders in many ways. Financial and Healthcare institutions also allow their customers to use their services by using handheld IoT devices. Smart homes and autonomous vehicles use many IoT devices to gather data through the built-in sensors and send it to the Edge server for further processing. The computation result on the Edge server determines the decision to fulfill the user-specific needs. As these data are vital in the future cycle of execution of an intelligent algorithm of IoT device software program, hence the data are not just of temporary use, but it is transferred to a Cloud server for permanent storage. The data flows from IoT sensors to the Edge server, then from the Edge server to the Cloud server. Here the riskiest part for data to stay is on the Edge server. To counter such a security risk, we proposed and implemented the Adaptive Epsilon Greedy Reinforcement Learning (AEGRL) method which is the extension of the traditional Epsilon (ℇ) greedy reinforcement learning method. The proposed method works efficiently for both static and dynamic environments. Experimental results show that our proposed security method outperforms the recent similar security approaches in terms of scalability, robustness, and accuracy. Article Highlights: The research work proposed in this paper is to prevent malicious attacks on the IoT edge server since the edge server continuously gathers data from the surrounding IoT devices. The following are the main highlights of the paper, which bring novelty to our research work. We have considered the dynamic nature of traffic data concerning volume and the pattern of malicious data. The metadata of the malicious packets keeps changing by the smart hackers who also use intelligent tools to cross the common security barriers. Considering the dynamic environment over the Edge server we extended the traditional reinforcement learning approach to include Adaptive ability on the dynamic constraints. We experimented with both simulation-based and real-time environments. [ABSTRACT FROM AUTHOR]

The purpose of the article is to carry out a multi-faceted analysis of the dynamics of the development of parliamentarism and digital constitutionalism, to identify possible challenges and threats, as well as prospects for parliaments and parliamentary activity in the conditions of the spread of modern digital (electronic) technologies. Approaches to understanding and defining the phenomena of «parliamentarism», «constitutionalism», «digital constitutionalism», some of their modifications and features are highlighted, their multifaceted nature, transformation in the conditions of modern progressive digital development, electronic democracy is emphasized. Crisis and conflict events have largely become challenges for parliaments and parliamentary activity, as well as digitalization and digitization processes and tools. It is important that parliamentary institutions of various levels (national, supranational, inter-parliamentary associations, their auxiliary bodies and services) contribute to the development and regulation of these processes, using advanced and effective digital technologies tested in practice. The «Digital Compass» for the EU Digital Decade (2030), the concept of digital sovereignty, the adopted charters of digital rights (Catalan, Nice), the Law on Digital Markets, the Law on Digital Services, the Law on Artificial Intelligence, the Declaration on European Digital Rights and Principles should be singled out. (2022), «World e-Parliament Report» of the IPU, the concept of e-democracy of the European Parliament, the resolution on e-democracy of the PACE, recommendations on open government and electronic participation of citizens OECD. Thanks to this, new forms and tools for the implementation of existing human rights are introduced, as well as new digital rights are constituted (to access the Internet, to identity in the digital environment, to protection in the digital environment, non-discrimination, etc.). In the same way, the forms and methods of activity of parliaments are being modernized (expansion of platforms for online broadcasts of plenary and committee meetings, electronic (remote) voting, improvement of interactive websites with access to draft laws and other documents, and systems of electronic petitions and consultations with voters, electronic participation of citizens and public organizations in the legislative process). However, the development of digital parliamentarism and digital democracy is accompanied by numerous challenges (digital inequality, low digital literacy of members of parliaments and parliamentary services, cyber threats, material and financial costs etc.). This requires a thorough study of the issues raised, improvement of digital culture, improvement of parliamentary and non-parliamentary procedures, along with further development of technological infrastructure, cyber security protection of the components of digital parliamentarism. [ABSTRACT FROM AUTHOR]

Significant attempts have been made to make the electrical grid more intelligent and responsive to better meet customers' requirements while boosting the stability and efficiency of current power systems. Smart grid technologies, which have just recently emerged, facilitated the incorporation of demand response (DR) by introducing an information and communication backbone to the current system. The Internet of Things (IoT) has emerged as a key technology for smart energy grids. Security concerns have emerged as a major obstacle to the widespread adoption of IoT‐enabled devices because of the inherent Internet connectivity of these smart gadgets. Therefore, security is a crucial factor to address before the widespread implementation of IoT‐based devices in power grids. In this study, the framework and architecture of smart grids that are enabled by the IoT are first examined. Then, the role of IoT for DR in smart grids and different approaches adopted worldwide to make DR schemes more effective, have been discussed in detail. Finally, the authors discuss how IoT‐enabled smart grids can benefit from cutting‐edge solutions and technologies that make them more secure and resistant to cyber and physical attacks. [ABSTRACT FROM AUTHOR]

Steganography, adopted to hide secret information into multiple digital carriers, has been widely used by lawbreakers for spiteful purposes, posing a threat to cyber security. Recently, VoIP speech (compressed speech) has been determined as a suitable carrier for steganography. Among the existing VoIP steganography algorithms, QIM-based VoIP steganography has great stealthiness, making it hard to detect. Although some existing studies have been conducted on detecting QIM-based VoIP steganography, some major challenges still exist that need to be addressed. First, no previous study attempts to estimate the payload locations of QIM-based VoIP steganography, a potential research direction, and a practical way of reducing the search space to recover hidden information. Second, the detection performance of existing models on QIM-based VoIP steganography still has a huge room for improvement. To address the above issues, this paper proposes a novel method called the Steganography Payload-localization Model (SPM) as a countermeasure for QIM-based VoIP steganography. Experimental results show that SPM can estimate the payload locations and perform real-time steganalysis regarding QIM-based VoIP steganography with superb performance. [ABSTRACT FROM AUTHOR]

In the contemporary landscape of smart cities, ensuring the security and confidentiality of transmitted data has become paramount. The proliferation of Internet of Things (IoT) devices, mobile communication platforms, and wireless sensor networks has magnified the need for robust cryptographic solutions to safeguard sensitive information from malicious adversaries. In response to these challenges, we introduce ChessCrypt—a novel approach to enhancing wireless communication security through the development of a new S-Box algorithm inspired by the nonlinear movement patterns of chess pieces. ChessCrypt leverages the dynamic and unpredictable nature of chess piece movements, namely knights, kings, and bishops, to introduce a high degree of nonlinearity and confusion into the encryption process. By incorporating elements of randomness and unpredictability, our proposed S-Box algorithm offers robust protection against a wide range of cyber threats, including eavesdropping, data interception, and cryptographic attacks. We demonstrate the effectiveness and resilience of ChessCrypt through rigorous testing and analysis, showcasing its superiority over traditional cryptographic methods in enhancing the security of wireless communication networks. Our results underscore the significance of ChessCrypt as a promising solution for fortifying wireless communication security in an increasingly interconnected world of ours. [ABSTRACT FROM AUTHOR]

In recent years, the rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management. While these technological innovations hold immense promise, they concurrently raise critical security concerns, particularly in safeguarding medical data against potential cyber threats. The sensitive nature of health-related information requires robust measures to ensure patient data's confidentiality, integrity, and availability within IoT-enabled medical environments. Addressing the imperative need for enhanced security in IoT-based healthcare systems, we propose a comprehensive method encompassing three distinct phases. In the first phase, we implement blockchain-enabled request and transaction encryption to fortify the security of data transactions, providing an immutable and transparent framework. Subsequently, in the second phase, we introduce request pattern recognition check, leveraging diverse data sources to identify and thwart potential unauthorized access attempts. Finally, the third phase incorporates feature selection and the BiLSTM network to enhance the accuracy and efficiency of intrusion detection through advanced machine-learning techniques. We compared the simulation results of the proposed method with three recent related methods, namely AIBPSF-IoMT, OMLIDS-PBIoT, and AIMMFIDS. The evaluation criteria encompass detection rates, false alarm rates, precision, recall, and accuracy, crucial benchmarks in assessing the overall performance of intrusion detection systems. Notably, our findings reveal that the proposed method outperforms these existing methods across all evaluated criteria, underscoring its superiority in enhancing the security posture of IoT-based healthcare systems. [ABSTRACT FROM AUTHOR]

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques. [ABSTRACT FROM AUTHOR]

While the proliferation of the Internet of Things (IoT) has revolutionized several industries, it has also created severe data security concerns. The security of these network devices and the dependability of IoT networks depend on efficient threat detection. Device heterogeneity, computing resource constraints, and the ever-changing nature of cyber threats are a few of the obstacles that make detecting cyber threats in IoT systems difficult. Complex threats often go undetected by conventional security measures, requiring more sophisticated, adaptive detection methods. Therefore, this study presents the Hybrid approach based on the Support Vector Machines Rule-Based Detection (HSVMR-D) method for an all-encompassing approach to identifying cyber threats to the IoT. The HSVMR-D employs SVM to categorize known and unknown threats using attributes acquired from IoT data. Identifying known attack signatures and patterns using rule-based approaches improves detection efficiency without retraining by adapting pre-trained models to new IoT contexts. Moreover, protecting vital infrastructure and sensitive data, HSVMR-D provides a thorough and adaptable solution to improve the security posture of IoT deployments. Comprehensive experiment analysis and simulation results compared to the baseline study have confirmed the efficiency of the proposed HSVMR-D. Furthermore, increased resilience to completely novel changing threats, fewer false positives, and improved accuracy in threat detection are all outcomes that show the proposed work outperforms others. The HSVMR-D approach is helpful where the primary objective is a secure environment in the Internet of Things (IoT) when resources are limited. [ABSTRACT FROM AUTHOR]

Peculiarities of legal regulation of population protection during radiation emergency situations at nuclear power plants under martial law conditions have been studied. The decisive link is the prevention of emergency situations at NPPs, the provision of deep-echeloned five-level protection of NPPs in accordance with NP 3062.141-2008 “General provisions for the safety of nuclear plants”. In wartime conditions, additional threats to the NPP as a critical infrastructure facility should be taken into account, including cyber terrorism, cyberespionage, threats of terrorist acts, threats of missile attacks, information threats, as well as the threat of loss of power supply for the NPP’s own needs. The need to create a regulatory body in Ukraine for the implementation of state policy on nuclear and radiation safety issues, as a state collegial body of executive power with a special status, through the adoption of the relevant Law, with the determination of the features of the state regulation of nuclear and radiation safety during the period of martial law, in particular to determine by law, is substantiated conditions for introducing maximum autonomy in NPP management, maximum simplification and minimal intervention in NPP operation while preserving all actions related to ensuring nuclear and radiation safety; proper organization of interaction with the public. According to the results of the study, taking into account the experience of the EU, it is proposed to introduce a system of independent advisory and advisory bodies based on the National Commission for Radiation Protection of the Population of Ukraine (NKRZU), which was formed in accordance with the Declaration on the State Sovereignty of Ukraine of July 16, 1990, as a permanently active higher independent collegial scientific and expert advisory body on anti-radiation protection and radiation safety of the population of Ukraine, with the aim of identifying problematic issues and providing recommendations on the specifics of developing plans for the protection of personnel and the population in the event of radiation accidents at nuclear plants. The research uses the general dialectical method, the method of scientific knowledge, the method of interpreting legal norms, the formal legal method, the comparative legal method, and the method of systemic analysis. [ABSTRACT FROM AUTHOR]

Why do cyberattacks elevate perceptions of threat even as they fail to generate truly destructive effects? We argue that people employ motivated reasoning to align cyberattacks with their preconceptions. Fielding a two-wave panel survey immediately before and after a cyberattack on an Israeli hospital, we confirm that motivated reasoning plays a crucial role in how people interpret cyber events. Results show that people already predisposed to perceive cyberattacks as threatening experience a significant increase in perceptions of threat, while those initially viewing cyberattacks as harmless also have their beliefs reinforced. The data reveals how even minor incidents bolster cyber doom convictions. [ABSTRACT FROM AUTHOR]

Safeguarding digital information against unauthorized access is critical in the industry context. Techniques for cracking passwords are essential tools for both attackers and defenders. This study explores the utilization of Bloom filters, a probabilistic data structure known for its space and time efficiency, to refine the password-cracking process. We demonstrate that by employing Bloom filters, it is possible to significantly enhance the performance of password-cracking techniques in terms of speed and memory consumption. By conducting a comparative analysis with prevalent techniques such as hash tables and binary search, we demonstrate the superior performance of Bloom filters. The experimentation, utilizing a publicly available dataset of leaked password hashes, indicates a significant improvement in cracking efficiency. The findings contribute to the broader cybersecurity goal of developing resilient systems against password-related breaches, underscoring the importance of integrating cutting-edge research and practical applications to fortify digital defenses. [ABSTRACT FROM AUTHOR]

Wireless networking provides many advantages, but it also coupled with new security threats and alters the organization's overall information security risk profile. Meanwhile, researchers are also actively working to enhance the security of embedded devices. However, previous studies have overlooked the insecurity of a particular device category, known as End-of-Life (EoL) devices. When a product reaches its EoL phase, vendors discontinue its maintenance, including the provision of bug fixes and security patches. However, many EoL devices remain on the internet with several critical vulnerabilities, thereby creating a fertile ground for botnets and cyber-attacks. Due to the lack of security updates, hardening the potentially vulnerable firmware in IoT devices is the most direct and promising defense method, but it has not been fully explored. In this paper, we propose a systematic active defense approach to harden EoL IoT devices, utilizing a hybrid binary rewriting method to monitor high-risk APIs and filter attack vectors. The proposed system, called Harden-IoT, consists of three tightly coupled modules: suspicious code snippets location, attack vector interception, and heuristic firmware repackaging. It can reinforce different architecture (MIPS, ARM) Linux-Based IoT devices without source code. We evaluate the effectiveness, adaptability, and overhead of the method using 23 firmware images sourced from various vendors. The results show that Harden-IoT can effectively defend against multiple types of vulnerabilities under low overhead conditions while also being able to adapt to different heterogeneous devices. [ABSTRACT FROM AUTHOR]

Ransomware is a menace to the vibrant digital ecosystem. The exponential growth in ransomware attacks, its detrimental impacts, and the ever‐changing methods adopted by threat actor groups demands a focused understanding of the evolution of ransomware. This would help the organizations devise novel defensive frameworks and security controls against the modern ransomware. In this work, the impacts and evolution of ransomware through different phases up to its current form are detailed. Further, based on the study and analysis of the most prevalent modern ransomware variants, their most used tactics, techniques and procedures (TTPs) are identified as per the MITRE ATT&CK model. This acts as a platform to propose a generic attack model for "modern ransomware." Building on the existing MITRE mitigation, D3FEND‐based approaches and considering the resource and budget constraints of organizations, a simplified three‐tier defensive model that is cost‐effective and implementable is put forward. Thus, this work aims to open avenues for understanding the TTPs, and attack methodology of "modern ransomware," thereby developing feasible and implementable defensive security controls. [ABSTRACT FROM AUTHOR]

Managing identities in metaverse environment is challenging due to the conflicting requirements of ensuring user privacy while enforcing accountability. Users may participate in the metaverse through multiple virtual identities or avatars for different applications/scenarios. Thus, virtual identities must be continuously generated and mapped with real identities. Further, user privacy needs to be protected while not ensuring blanket anonymity to indulge in objectionable or unlawful conduct. Ensuring privacy for the metaverse while still being able to pinpoint users for their actions in the metaverse is a significant research issue. Solving the aforementioned issue, we propose a third‐party identity management solution for the metaverse based on creating a decentralized oracle network (DON) providing identity authentication and mapping service. The proposed identity management framework is metaverse platform agnostic, ensuring users' privacy on the platform while still being able to uncover their real identities if warranted. The proposed framework is viable as evidenced by experimental results of DON and blockchain implementation. The DON based framework leverage the benefits of blockchain technology and introduces immutability, transparency, and traceability into the the metaverse environment. This ensures data integrity and minimizes the frequency of cyber attacks in the metaverse ecosystem. [ABSTRACT FROM AUTHOR]

Purpose: Adverse cyber events, like death and taxes, have become inevitable. They are an increasingly common feature of organisational life. Their aftermaths are a critical and under-examined context and dynamic space within which to examine trust. In this paper, we address this deficit. Design/methodology/approach: Drawing on pertinent theory and reports of empirical studies, we outline the basis of two alternative subsequent trajectories, drawing out the relationships between trust, vulnerability and emotion, both positive and negative, in the aftermath of an adverse cyber event. Findings: We combine stage theory and social information processing theories to delineate the dynamics of trust processes and their multilevel trajectories during adverse cyber event aftermaths. We consider two response trajectories to chart the way vulnerability arises at different levels within these social systems to create self-reinforcing trust and distrust spirals. These ripple out to impact multiple levels of the organisation by either amplifying or relieving vulnerability. Research limitations/implications: The way adverse cyber events aftermaths are managed has immediate and long-term consequences for organisational stakeholders. Actions impact resilience and the ability to preserve the social fabric of the organisations. Subsequent trajectories can be "negative" or "positive". The "negative" trajectory is characterised by efforts to identify and punish the employee whose actions facilitated the adverse events, i.e. the "who". Public scapegoating might follow thereby amplifying perceived vulnerability and reducing trust across the board. By contrast, the "positive" trajectory relieves perceived vulnerability by focusing on, and correcting, situational causatives. Here, the focus is on the "what" and "why" of the event. Practical implications: We raise the importance of responding in a constructive way to adverse cyber events. Originality/value: The aftermaths of cyber attacks in organisations are a critical, neglected context. We explore the interplay between trust and vulnerability and its implications for management "best practice". [ABSTRACT FROM AUTHOR]

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out. [ABSTRACT FROM AUTHOR]

The digitalisation of the economy increases the vulnerability of both economies in the EU and the US, as does its transborder dimension. Cyber policy has evolved over time on both sides of the Atlantic. The EU began initially to emphasise cybercrime regulation but its focus upon cybersecurity now dominates, similar to the US. The internal market has evolved as a rationale for regulation in the EU and to similar effect a market-led approach dominates in the US. While in the EU a comprehensive cybersecurity law has been adopted, the US lacks a uniform federal cybersecurity law. Despite many domestic divergences, there is considerable similarity between the US and the EU. Substantively, these divergences have not inhibited convergence. Geopolitical considerations as to cyber have accelerated an ongoing process, driven by the transborder nature of cyber security and the global leadership of the EU and the US. [ABSTRACT FROM AUTHOR]

The rapidly growing number of Internet of Things (IoT) devices has led to a rise in data transfers, which has raised security concerns. Due to the devices' limited processing capabilities and vulnerability to many cyber attacks, securing IoT communications is challenging. Security threats, especially Distributed Denial of Service (DDoS) attacks, take a toll on the network in the form of increased communication overhead. Hence, a centralized unit is required to detect DDoS attacks in IoT networks at the earliest. Software-Defined Networking (SDN) promises a potential solution for better network traffic management and data flow. This paper presents a machine learning-based ensemble model for the detection of DDoS attacks in IoT networks using SDN. The proposed model employs a multi-step approach utilizing various Machine Learning (ML) algorithms. The proposed Ensemble Model (EM) combines Logistic Regression (LR), k-Nearest Neighbors (KNN), Gradient Boosting (GB), Extra-tree (ET), AdaBoost, and XGBoost, with XGBoost as the final estimator classifier. Various metrics, including sensitivity, specificity, precision, accuracy, and others, derived from the confusion matrix, evaluate the proposed model's performance. The EM demonstrates superior performance during comparative analysis with state-of-the-art schemes, with a classification accuracy of 99.8%. Furthermore, the paper evaluates the model based on Receiver Operator Characteristic (ROC) curves, showing its superiority in True Positive Rates (TPR) compared to False Positive Rates (FPR). The AUC analysis supports the EM's effectiveness. Cross-validation results further validate the model's robustness, with a mean accuracy of 97.92%. [ABSTRACT FROM AUTHOR]

As industrial and everyday devices become increasingly interconnected, the data volume within the Internet of Things (IoT) has experienced a substantial surge. This surge in data presents a heightened risk of IoT environments being vulnerable to cyber attacks, which poses a significant threat to the seamless functioning of both industrial and daily activities. Therefore, the implementation of Network Intrusion Detection System (IDS) is vital for safeguarding the security of IoT network environments. This paper introduces a network intrusion detection model based on deep learning (DL). The model aims to enhance detection accuracy by extracting features from both the spatial and temporal dimensions of network traffic data. To tackle the challenge of low detection accuracy arising from data imbalance, in this study, a Conditional Tabular Generative Adversarial Network (CTGAN) is utilized to generate synthetic data for the minority class. The objective is to enhance the volume of minority class samples, address data imbalance, and subsequently enhance the accuracy of network intrusion detection. The classification performance of the proposed model is validated on UNSW-NB15, CIC-IDS2018, and CIC-IOT2023 datasets. The experimental findings demonstrate that the suggested model attains elevated levels of classification accuracy across all three datasets. The model presented in this article is particularly well suited to handle multi-class intrusion detection tasks. The model demonstrates superior performance compared to other models used for comparison. [ABSTRACT FROM AUTHOR]

The problem of asynchronous fault detection filtering for nonhomogeneous Markov jumping systems with dynamic quantization and hybrid cyber attacks is addressed in this paper. The introduction of polytopic-structure-based transition probabilities is employed to describe the nonhomogeneous Markov process. An asynchronous fault detection filter is proposed, which utilizes the hidden Markov model to achieve comprehensive access to the plant mode information. Prior to transmission to the filter, the measurement output of the system undergoes quantization using a dynamic quantizer. The novel hybrid cyber attacks model being discussed involves four types of attacks: deception attacks, denial-of-service attacks, no attack, and hybrid attacks with both deception and denial-of-service attacks. By constructing Lyapunov functional, sufficient conditions are presented for achieving the stochastic stability with H ∞ performance. Under the complex network environment, the industrial application of the presented asynchronous fault detection filtering model is demonstrated on a non-isothermal continuous stirred tank reactor. The simulation results confirm the practicality of the proposed design method. • The dynamic quantizer is adopted to quantify measurement output signals to reduce information exchange frequency and communication burden. • A convex polyhedron is used to describe the time-varying transition probability matrix. • Due to the mode information between the filter and the plant cannot be fully obtained in the networked control system, a HMM-based asynchronous mechanism is proposed. • In order to improve the system performance, a novel hybrid network attack model is proposed, which includes four attack modes. [ABSTRACT FROM AUTHOR]

Sensors and wearable technology are used to accumulate health-related data from the visually impaired (VI) and other special persons. This health-related data is disseminated via an open network channel, which poses numerous threats and requires rigorous attention for its protection. An appropriate security framework must be implemented on both the sender and the recipient sides to ensure the privacy and security of sensitive medical information for VI and disabled persons. The telehealth system will then benefit the VI and disabled people by improving their quality of life, telemonitoring, saving time and money, providing online support, and increasing the degree of trust and reliance on the system and the treatment of doctors. These practical benefits are not just theoretical but potentially life-changing for the VI and disabled community. Strong and flawless mutual authentication is necessary for the telehealth system to secure and preserve the privacy of VI and disabled people. Therefore, this article proposes a privacy-preserving asymmetric-based, lightweight, and robust security framework for the telehealth system operationalized for VI and disabled people. The proposed security framework alleviates all threats to sensitive information and preserves the privacy and security of the whole telehealth system. The security of the proposed framework has been verified through a well-known Real-Or-Random (RoR) model and simulated through a ProVerif software verification toolkit, while the performance analysis has been tackled by measuring computation, storage, and communication costs. The comparative analysis result revealed that the proposed framework outperforms its competitors, further reinforcing the hope about its potential impact. [Display omitted] • To design a fast and secure protocol for securely authenticating visually impaired (VI) and disabled people of the telehealth system in the digital world. • To present a secure mechanism that not only protects the privacy of VI and disabled people when transmitting personal sensitive information remotely but also opens up new possibilities for their participation in telehealth. • To confirm the security of the proposed authentication protocol formally through the Real-Or-Random (RoR) model and ProVerif and informally through discussions of known attacks. • To conduct a comprehensive performance analysis of the proposed protocol, considering storage, communication and computation costs in providing a robust and reliable solution. • To comparatively analyze the proposed security mechanism with state-of-the-art mechanisms by scrutinizing its efficacy and robustness based on a set of predefined criteria. This will provide a clear understanding of how our protocol stands against existing solutions. [ABSTRACT FROM AUTHOR]

Cyberattack classification involves applying deep learning (DL) and machine learning (ML) models to categorize digital threats based on their features and behaviors. These models examine system logs, network traffic, or other associated data patterns to discriminate between standard activities and malicious actions. Efficient cyberattack classification is vital for on-time threat detection and response, permitting cybersecurity specialists to categorize and reduce potential risks to a system. Handling class-imbalanced data in cyberattack classification using DL is critical for achieving exact and robust models. In cybersecurity databases, instances of normal behavior frequently significantly outnumber instances of cyberattacks, foremost due to biased methods that may complete poorly on minority classes. To address this issue approaches such as oversampling the lesser class, undersampling the popular class, or using more advanced systems can be used. These plans defend that the DL technique is more complex when determining cyberattacks, so it increases complete performance and adapts the effect of the imbalance class on the classification results. This study presents a novel Hybrid Salp Swarm Algorithm with a DL Approach for Cyberattack Classification (HSSADL-CAC) technique. The HSSADL-CAC method intends to resolve class imbalance data handling with an optimum DL model for the recognition of cyberattacks. At first, the HSSADL-CAC method experiences data normalization as a pre-processing stage. The HSSADL-CAC technique uses the ADASYN approach to handle class imbalance problems. In addition, the HSSADL-CAC technique applies an HSSA-based feature selection approach. The HSSADL-CAC technique detects cyberattacks using a deep extreme learning machine (DELM) model. Finally, the hyperparameter tuning of the ELM model takes place by utilizing the beluga whale optimization (BWO) model. The performance analysis of the HSSADL-CAC technique employs a benchmark database. The comprehensive comparison research indicates the superior performance of the HSSADL-CAC technique in the cyberattack detection procedure. [ABSTRACT FROM AUTHOR]