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The growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent selection at the network layer of IoT causes energy drain in the nodes, particularly near the sink node. As a result, nodes die earlier, causing network holes that further increase the control message overhead as well as the energy consumption of the network, compromising network security. This research introduces an AI‐based approach to parent selection of the Routing Protocol for Low Power and Lossy networks (RPL) at the network layer of IoT to enhance security and energy efficiency. A novel objective function, named Energy and Parent Load Objective Function (EA‐EPL), is also proposed that considers the composite metrics, including energy and parent load. Extensive experiments are conducted to assess EA‐EPL against OF0 and MRHOF algorithms. Experimental results show that EA‐EPL outperformed these algorithms in improving energy efficiency, network stability, and packet delivery ratio. The results also demonstrate a significant enhancement in the overall efficiency of IoT networks and increased security in smart home environments. [ABSTRACT FROM AUTHOR]

The issue of violent extremism has given rise to new policy debates in Norway. A key limitation of these debates, often grounded in naïve assumptions about the peacebuilding effect of education, is the downplay of emotions and dissent in democratic engagement. This article analyses how selected educators in Norway describe encountering and engaging with extremist students for educational interventional purposes. Previous research suggests that educational efforts to counter violent extremism can be exclusionary from the perspective of target audiences. In contrast, this study draws on agonistic pluralism to provide an alternative to understand educational approaches that may help students disengage from violent extremism. The findings show that the selected educators argue the importance of trust, support and tolerance when engaging with extremist students. For these professionals, education should not downplay radical or extreme emotions but rather place them at the centre of educational engagement. Furthermore, it is suggested that educators' empathic engagement may open the path for young people to disengage from violent extremism. [ABSTRACT FROM AUTHOR]

The Smart Grid AMI is at the forefront of modernizing energy management for efficient energy distribution and maintaining grid reliability. However, the seamless flow of data within AMI introduces critical security challenges, necessitating innovative approaches to ensure secure data transmission. This paper addresses these challenges by proposing a Quantum Key Distribution (QKD) based on Multi-Objective Optimization (MOO) and Reinforcement Learning (RL) framework for secure data transmission within the Smart Grid AMI. Primarily, we formulate the routing problem as a MOO challenge with four primary objectives namely, energy efficiency, latency minimization, reliability, and security to capture the essence of efficient and secure data transmission. Further, we introduce an RL agent based on the Proximal Policy Optimization (PPO) algorithm for robust policy learning. The RL agent by exploring diverse routing actions and receiving rewards optimizes the routing decisions within the network environment based on the objectives specified in the multi-objective function (MOF). A novel MOF quantifies trade-offs between security and performance metrics, integrating QKD-based security metrics with traditional optimization objectives. This function guides the RL agent in making informed routing decisions. Through extensive simulations using the NS-2 simulator in a realistic Smart Grid AMI environment, the proposed approach obtained energy consumption of 750 J, latency of 55 ms, and security level of 96% and also revealed significant improvements compared to conventional techniques widely employed for secured communication in AMI networks. Overall, the simulation results exhibited that the proposed method showed outstanding performance by achieving energy efficiency of 0.95, latency reduction of 0.92, reliability improvement of 0.94, and security enhancement of 0.96. Overall, this integration offers a pioneering solution to address the evolving security challenges in the Smart Grid landscape and contributes to the advancement of secure data transmission in Smart Grid AMI networks, fostering a resilient and secure energy ecosystem. [ABSTRACT FROM AUTHOR]

The Ethereum consensus layer provides the Proof of Stake (PoS) consensus algorithm with the beacon chain for the Ethereum blockchain network. However, the beacon chain is proved vulnerable to consensus-targeted attacks, which are difficult to detect. To address this issue, blockchain developers require an interactive tool to identify and mitigate potential security threats. Currently, most blockchain visualization solutions only display client logs or transaction records, making responding quickly to security threats challenging. This paper introduces the first visual analytics solution for security threat awareness on the Ethereum consensus layer. We cooperate with blockchain experts and investigate a top-down exploration approach, providing an overview of the general security level, as well as detailed consensus achievements in each slot. Our visual system lets users discover specific outcomes of the consensus execution and identify anomalies in the beacon chain historical data. Furthermore, the system includes two case studies of actual attacks to help developers better understand and mitigate potential security threats. [ABSTRACT FROM AUTHOR]

The war on terror as a discourse assumes that terrorism is an essential threat of global proportions, is mostly perpetuated by Islamist networks, and requires a strong international response. This discourse had tremendous impacts on both domestic and international politics. Consequentially, a large number of studies analyse the assumptions underlying and the policies legitimised by the war on terror discourse. However, existing work mostly focusses on one or a few cases, predominantly in the global north. This article introduces a novel dataset containing information on the war on terror discourse in the school textbooks of 36 countries, representing around 64% of the world's population, for the period 2003–2014. Based on this dataset, I present the first comprehensive analysis of the global diffusion of the war on terror discourse. The study finds that the discourse has by no means globalised but is mostly limited to wealthy countries in Europe and North America. There are hence clear limits to the USA's soft power and the hyper-globalisation of terrorism discourses. Factors like terrorism intensity, armed conflict and authoritarian regime have little predictive power. This is despite clear incentives for challenged (authoritarian) regimes to adapt the war on terror discourse. Contrary to common assumptions in critical security and terrorism studies, the war on terror discourse is hardly associated with an emphasis on terrorists' irrationality and hatred or with the marginalisation of socio-political grievances. [ABSTRACT FROM AUTHOR]

European policymakers have identified the need to understand influence operations on social media, cyber-attacks or hidden economic investments – activities that can be characterised as hybrid threats or hybrid warfare. Yet, the difference between hybrid threats and hybrid warfare is unclear. In 2019, Mikael Wigell therefore coined the term "hybrid interference" to clarify the distinction between "hybrid warfare" and "hybrid threats". However, less attention has been given to the activities hybrid interference may consist of. To address this gap we have used a morphological analysis, which is a structured tool for analysis that addresses all aspects of a concept. Through this method, we propose categories that make more sense of the complex phenomenon of hybrid interference. These five categories are international politics, coercive diplomacy, priming, covert coercion, and sabotage and assassinations. This article also identifies problems with referring to activities in the category of international politics as hybrid interference activities. The result is novel because we place all combinations of tools and methods within the concept of hybrid interferences in one of the five above-mentioned categories, and as such have provided a detailed operationalising of the concept in a transparent manner. [ABSTRACT FROM AUTHOR]

This paper examines the form, function and impact of previous Olympic security arrangements and their intersection with planning practice. Drawing from prior and ongoing empirical research investigating the security practices at summer Olympic Games, the paper argues that wider shifts towards 'total' security models comprising continually reproduced security motifs can be observed that are increasingly standardized, mobile, globalized and planned-in. For most Olympic organizers, preparations now necessarily include attempts to equate spectacle with safety and to 'design-out' terrorism by relying on highly militarized tactics and expensive and detailed contingency planning. Such securitizing practices have intensified in form and scale since 9/11, with such intensification set to continue at the XXXIII Olympiad in Paris, where a vast security infrastructure is being embedded into the large-scale and long-term master-plans for the central city. This represents a high point in spatial planning practice through embracing principles of security-by-design where Games-time security infrastructure, whilst providing effective protection, becomes a less visible but permanent, physical legacy that can also contribute to local programmes of regeneration, climate resilience and crime prevention. The paper concludes by reflecting upon what the continual evolution of security infrastructure means for the balancing of planned-in security and spectacle at future Olympiads. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI)-based IDS systems are susceptible to adversarial attacks and face challenges such as complex evaluation methods, elevated false positive rates, absence of effective validation, and time-intensive processes. This study proposes a WCSAN-PSO framework to detect adversarial attacks in IDS based on a weighted conditional stepwise adversarial network (WCSAN) with a particle swarm optimization (PSO) algorithm and SVC (support vector classifier) for classification. The Principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) are used for feature selection and extraction. The PSO algorithm optimizes the parameters of the generator and discriminator in WCSAN to improve the adversarial training of IDS. The study presented three distinct scenarios with quantitative evaluation, and the proposed framework is evaluated with adversarial training in balanced and imbalanced data. Compared with existing studies, the proposed framework accomplished an accuracy of 99.36% in normal and 98.55% in malicious traffic in adversarial attacks. This study presents a comprehensive overview for researchers interested in adversarial attacks and their significance in computer security. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) plays a fundamental role in contemporary society, necessitating an in-depth comprehension of its application layer protocols, intertwined technologies, security issues, and effective countermeasures. This survey delivers an exhaustive analysis of these protocols, emphasizing the escalating significance of explainable artificial intelligence in IoT (XAIoT). To elucidate its practical implications, we conducted a case study examining a real-world scenario where XAIoT significantly bolstered IoT security. This case study demonstrated XAIoT's potential to enhance transparency and trustworthiness in IoT systems. Furthermore, the survey critically evaluates existing literature, pinpointing specific opportunities and gaps in the present state of IoT application layer security. For instance, our analysis revealed a pressing need for more robust security protocols and the integration of advanced machine-learning techniques for anomaly detection in IoT applications. This survey, designed to provide a comprehensive perspective, seeks to stimulate additional innovation and research in the realm of secure and intelligent IoT applications. In doing so, it contributes to the ongoing dialogue on improving IoT security, offering valuable insights for researchers and practitioners alike. [ABSTRACT FROM AUTHOR]

Artificial intelligence-based algorithms are widely adopted in critical applications such as healthcare and autonomous vehicles. Mitigating the security and privacy issues of AI models, and enhancing their trustworthiness have become of paramount importance. We present a detailed investigation of existing security, privacy, and defense techniques and strategies to make machine learning more secure and trustworthy. We focus on the new paradigm of machine learning called federated learning, where one aims to develop machine learning models involving different partners (data sources) that do not need to share data and information with each other. In particular, we discuss how federated learning bridges security and privacy, how it guarantees privacy requirements of AI applications, and then highlight challenges that need to be addressed in the future. Finally, after having surveyed the high-level concepts of trustworthy AI and its different components and identifying present research trends addressing security, privacy, and trustworthiness separately, we discuss possible interconnections and dependencies between these three fields. All in all, we provide some insight to explain how AI researchers should focus on building a unified solution combining security, privacy, and trustworthy AI in the future. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is based on the idea that smart things can connect to the Internet and exchange the collected data in a peer-to-peer paradigm. Due to its inherent features, IoT can be utilized in real-world scenarios and its expansion can improve human well-being. Internet of things is applied quite closely to humans and transmits serious information such as healthcare information, financial data, and private information through an insecure communication platform. Since almost all tasks are performed with minimal human intervention, and adversary may deploy its nodes among other legitimate elements of IoT, providing an effective mutual authentication is vital. In this Systematic Literature Review, authentication of IoT and its literature are reviewed systematically. In particular, it has endeavored that the collected literature covers the papers conducted from 2018 to 2022. Moreover, this study seeks to provide a comprehensive answer to six important Research Questions in the context of authentication of IoT that often engage the minds of scholars. It is hoped that this survey will be an effective guide for future research by addressing the relevant challenges, analyzing open issues, and providing future research directions. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and distributed hyper ledger technology are both at the height of their hype cycles, which means they have the potential to be disruptive. This raises issues about new technologies' influence on future business structures, particularly in service‐based industries like finance. Even though numerous assumptions in reality point to the complementary use of these two technologies to produce new value creation potentials, there is little contemporary literature and study on the subject. Different perspectives on any of the two technologies mentioned above are insufficient for understanding potential synergies in financial services. Similarly, the internet of things (IoT) assistance for FinTech solutions has risen its significance in the upcoming transaction world. Therefore, this research proposes a distributed hyper ledger technology and AI for IoT‐based FinTech platform (DHLT‐AI‐FTP), which incorporates the benefits of each technology for secured and energy‐efficient financial transactions and communication. The AI technology and distributed hyper ledger technology handle the security part, and the communication part holds the efficient design of IoT implementation. The simulation evaluation of the proposed model ensures the improved security and efficiency of FinTech management and processing for real‐time business scenarios. The experimental analysis shows the significant performance of the proposed DHLT‐AI‐FTP with a better adaptability ratio of 98.56% compared to other existing approaches. [ABSTRACT FROM AUTHOR]

Objectives: Understanding how covert networks are formed is key to disrupting the operation of illicit activities. Applying standard network measures to a covert network, while useful, is limited in identifying the peculiar properties of the network in question. It is important that we compare the covert network against a benchmark, be it random networks or, more ideally, the counterpart (overt) network in the same context. We report a study in collaboration with law enforcement agency to examine the co-voyage network of criminals and non-criminals. A comparison of the two groups of actors in their network positions allows us to test whether criminals tend to blend in or hide out from the population of non-criminals. Methods: Drawing on data on maritime activities in Taiwan recorded from years 2016 to 2018, we map a maritime co-voyage network of 53,009 nodes and 2,592,288 weighted links. We follow a bootstrap resampling procedure to estimate the structural features of the co-voyage networks of criminals and non-criminals. Results: Criminals are more likely to co-voyage with their own type than non-criminals. Similarly, criminals are more clustered in the co-voyage network than non-criminals. Conclusions: It is more supported that criminals segregate from than blend in non-criminals in the co-voyage network. [ABSTRACT FROM AUTHOR]

This article explores the growing connections between the Persian Gulf states and the heavily militarised Israeli innovation ecosystem. The Gulf actors now play an increasing role in support of Israel as a globally expanding 'Start‐up Nation', and are involved in transforming Palestinian land into a regional frontier of technology in the West Bank and East Jerusalem. Israeli‐Gulf cooperation and its impact on Palestinians, however, has not been thoroughly researched. Drawing from fieldwork among Palestinian and Israeli innovation elites, we map these growing connections between Gulf state actors and Israel and analyse how the occupied Palestinian territories are further enmeshed in complex networks of financial capitalist and settler colonial security interests. Theoretically, the paper advances the critique of the contemporary settler colonial frontier, venturing beyond its usual Eurocentric and binary geographies. We conclude that 'innovation' is both (a) the terrain of expanding structures of Israeli colonisation and (b) the site of productive ambivalences and novel forms of resistance in an evolving regional and global reality. This article explores the growing connections between the Persian Gulf states and the heavily militarised Israeli innovation ecosystem. The Gulf actors now play an increasing role in support of Israel as a globally expanding 'Start‐up Nation', and are involved in transforming Palestinian land into regional frontiers of technology in the West Bank and East Jerusalem. Drawing from fieldwork among Palestinian and Israeli innovation elites, we map these growing connections between Gulf state actors and Israel and analyse how the occupied Palestinian territories are further enmeshed in complex networks of financial capitalist and settler colonial security interests. [ABSTRACT FROM AUTHOR]

Hyperspectral anomaly detection can separate sparse anomalies from the low-rank background component under an unsupervised behavior due to sufficient spectral information. Therefore, hyperspectral image anomaly detection technology has great application potential and value in public security and national defense. Currently, most existing models attempt to detect anomalous targets with a sparsity prior, without further considering the visual saliency of the targets themselves. To tackle this issue, this paper proposes a saliency-guided sparse low-rank tensor approximation model, called SSLR, to detect anomalous targets from hyperspectral remote sensing images in an unsupervised manner. Specifically, we first explore the saliency information of each pixel for regularizing the sparse anomaly matrix. We then suggest a three-directional tensor nuclear norm to obtain a low-rank background to characterize the background component. We solve the SSLR optimization problem by an efficient alternating direction method of multipliers framework. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed SSLR outperforms some state-of-the-art anomaly detection methods. [ABSTRACT FROM AUTHOR]

Who has responsibility for addressing the security implications of climate change? States and the United Nations justify their existence on the promise of providing security. Yet, although the national and international security implications of climate change are increasingly acknowledged, incorporation of climate change in national security planning or institutional arrangements is far from universal, while debates in the UN Security Council about its role in addressing climate change have been characterized by contestation. This article examines key debates about the responsibilities these institutions have for providing security in the face of the threats posed by climate change, examining the extent to which these institutions accept responsibility for providing security in these contexts. Drawing on Toni Erskine's notion of institutional moral agency, the article examines a 2017 inquiry into the national security implications of climate change in Australia, and the September 2021 UN Security Council debate on the international security implications of climate change. These two case studies explicitly focus on the question of institutional responsibility – of the Australian Government and the UN Security Council respectively – for addressing the threat of climate change. In both cases these institutions stop short of accepting responsibility for providing security in the face of climate change, with limited policy responses or institutionalization as the result. With the security implications of climate change increasingly apparent, and increasingly recognized by these (and other) actors, the failure to accept responsibility raises potentially significant questions about the legitimacy of these institutions themselves. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed paradigm that trained large-scale neural network models with the participation of multiple edge nodes and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client–server architecture, which leads to the single-point-of-failure (SPoF) attack, and the random selection of edge devices for model training compromised the accuracy of the model. Furthermore, adversaries try to initiate inference attack i.e., attack on privacy leads to gradient leakage attack. Hence, we proposed a blockchain-based optimized edge node selection and privacy-preserved framework to address the aforementioned issues. We have designed three kinds of smart contracts (1) registration of edge nodes (2) forward bidding to select optimized edge devices for FL model training, and (3) payment settlement and reward smart contracts. Moreover, fully homomorphic encryption with the Cheon, Kim, Kim, and Song (CKKS) method is implemented before transmitting the local model updates to the server. Finally, we evaluated our proposed method on the benchmark dataset and compared it with other state-of-the-art studies. Consequently, we have achieved a higher accuracy and privacy-preserved FL framework with a decentralized nature. [ABSTRACT FROM AUTHOR]

Recently, many image encryption algorithms have been proposed. Part of them failed to pass the cryptanalysis test and were found to be unsecure. Some others were more secure and could resist the known cryptanalysis methods. Many of those recent schemes followed a well-known model that consists of encrypting the image's pixels using a variety of techniques and methods. Many algorithms were claimed to be secure and performant to be used in real applications. This paper investigates this claim by performing a comparative analysis between a chaotic algorithm recently proposed and the standard general purpose AES cryptosystem in a web application context. The experimental tests conducted on both algorithms in this paper proved that the pixel-wise algorithms are not suitable for this kind of applications. [ABSTRACT FROM AUTHOR]

Human microarray gene profile analysis has become popular for early cancer detection. Due to security risks, Healthcare 4.0 standards make processing patient gene data difficult. Healthcare 4.0 guidelines must secure gene profile data storage and exchange. Recently several blockchain-based methods have been proposed for secure data processing for Cloud-based Healthcare 4.0 architecture. However, existing solutions not providing all the essential features like scalability, strong security measures, computational efficiency, and multiple security parameters. To overcome these problems, we propose a novel approach for the secure processing of the microarray gene expression data using computationally efficient, scalable, and highly secured Healthcare 4.0 standards. We propose a new Healthcare 4.0 architecture based on lightweight cryptography and blockchain technologies. Blockchain technology's recent advancements in cloud data storage and sharing indicate potential security features. Smart healthcare applications need a centralized approach for safe data storage and exchange. Edge layer (patients/gene users), fog, cloud storage, and blockchain make up Healthcare 4.0. Fog nodes store and search edge-layer gene data in blockchain-enabled cloud storage using lightweight cryptography. Elliptic Curve Cryptography (ECC) protects data processing and privacy using Elliptic Curve Diffie–Hellman (ECDH) and Elliptic Curve Digital Signature Algorithm (ECDSA). Meta-data from cloud-saved data is stored in blockchain to prevent tampering, forging, and quantum. We designed different threat-based case studies to demonstrate the model's efficiency and scalability. Computing efficiency and security are better with the proposed strategy. The outcome case studies showed that the suggested approach protected against various opponent models. The proposed method reduces encryption and decryption times by 325 and 20 ms, respectively. [ABSTRACT FROM AUTHOR]

Smart Autonomous Vehicles (AVSs) are networks of Cyber-Physical Systems (CPSs) in which they wirelessly communicate with other CPSs sub-systems (e.g., smart -vehicles and smart-devices) to efficiently and securely plan safe travel. Due to unreliable wireless communication among them, such vehicles are an easy target of malware attacks that may compromise vehicles' autonomy, increase inter-vehicle communication latency, and drain vehicles' power. Such compromises may result in traffic congestion, threaten the safety of passengers, and can result in financial loss. Therefore, real-time detection of such attacks is key to the safe smart transportation and Intelligent Transport Systems (ITSs). Current approaches either employ static analysis or dynamic analysis techniques to detect such attacks. However, these approaches may not detect malware in real-time because of zero-day attacks and huge computational resources. Therefore, we introduce a hybrid approach that combines the strength of both analyses to efficiently detect malware for the privacy of smart-cities. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have advanced technologically, allowing for a wide range of Internet of Things (IoT) applications. The Internet of Things (IoT) provides a platform for connecting items to achieve a specific goal with minimal human intervention. Various practical sectors, such as environmental monitoring and defense, demand IoT support with maximum object connectivity, and low human participation, yet face security concerns from strong unauthorized entities. As location privacy is one of these major vulnerabilities, continual monitoring of contextual information may lead the attacker to the asset's position. The location privacy of the source node that detects and reports the presence of an event is critical. In this study, we provide a source location privacy (SLP) preservation technique based on confounding domain angular routing (SLP-CDAR) that attempts to improve security while increasing network lifespan. To avoid traffic analysis attacks, packets first undergo a random walk, followed by a perplexing time domain routing that creates additional diversionary pathways. The packets are then routed via angle routing, followed by a random path, and finally delivered to the base station using the forward random walk technique. The experimental results prove that the proposed scheme enhances the location privacy strength while providing an improved network lifetime, and a swift increase in the entropy. [ABSTRACT FROM AUTHOR]

In general, the attackers probably create security-related threats from diverse insecure applications. These attackers execute various nasty activities like establishing hidden rootkit processes, hindering host-based security systems, changing the characteristics of applications, etc. The attackers utilize compromised tenant virtual machines for producing attacks with minor changes to avoid accurate prediction. To deal with these shortcomings, we proposed an efficient novel Deep Q-learning network-based circle search (DQL-CS) algorithm to detect intrusion in a cloud environment. The dataset is pre-processed initially by employing the Term Frequency-Inverse Document Frequency (TF-IDF) approach, which sequentially organizes normal and intrusion traces, and then the collected feature sequences are extracted using n-gram sampling. This process makes the classifier enhance the detection and classification performance. Followed by the extraction process, the classification process is executed using the proposed Deep Q-learning network-based circle search (DQL-CS) algorithm. The proposed DQL-CS algorithm accurately identifies and categorizes the attacks as two different classes normal and malevolent. Then the applications generating the malicious behavior are examined by the cloud admin using an alert generation system and are isolated automatically. The developed intrusion detection system is evaluated using three different malware databases such as the UNM dataset, windows malware dataset, and KDD99 dataset as the input. The accuracy rate of the UNM dataset of the proposed DQL-CS method is higher than other conventional approaches. [ABSTRACT FROM AUTHOR]

ABSTRACT Terrorism is not a new phenomenon. Nevertheless, since September 11, 2001, it appears that there has been a global consensus on the need for a more uniform and comprehensive response to terrorism. Thus, countries develop their counter‐terrorism strategy that comprises different approaches, including using the criminal justice system. The criminal justice response to terrorism covers special legislation, strategies, policies, investigation, prosecution, and sentencing. To combat terrorism and improve the criminal justice system in response to existing threats of terrorism, KRI legislated a special anti‐terrorism law. This paper investigates the trajectory of international and national terrorism law in the Kurdistan Region of Iraq (KRI). Accordingly, this research was set to examine international and national terrorism law critically. The focus is placed on the Anti‐terrorism Act No. 3 of 2006, emphasizing the principle of legality. As such, this paper adopted the doctrinal legal methodology that utilized qualitative techniques in analyzing the secondary data. The gathered information and data were then analyzed thematically. Based on the legal analysis, this study found that local circumstances and international pressure influence the trajectory of the counter‐terrorism laws in KRI. The absence of uniformity in the definition indicates the complexity of conceptualizing the phenomenon. The KRI anti‐terrorism law did not define international terrorism and may affect the principle of legality in counter‐terrorism strategy. Consequently, the study will benefit law, security, and international relations researchers. It serves as a basis for future empirical research examining the effects of anti‐terrorism laws in KRI or other jurisdictions. [ABSTRACT FROM AUTHOR]

On the one hand, feminists critique the securitization of conflict-related sexual violence (CRSV) on various grounds, but, on the other hand, also raise concerns that the Western hyper-fixation on CRSV reproduces colonial narratives about race, gender, and sexuality. However, the productive effects of specifically Western state discourse about CRSV have yet to be fully examined in this context. How do Western states make use of the colonial gaze in constructing CRSV as an issue of grave concern, and to what ends? Further, what are the implications of this for the nexus of feminist critique and praxis? This article utilizes a macro-structural approach to discourse analysis to analyze the 2014 Global Summit to End Sexual Violence in Conflict, led by the United Kingdom. It argues that Western delegates securitized CRSV by relying on tropes of racialized men as maintaining backwards practices of gender and sexuality, and the construction of CRSV as a “pre-modern” weapon of war. This frame reproduces a racialized hierarchy of masculinity that constructs men from “conflict countries” as potential perpetrators in need of intervention, and men from “safe countries” as role models for gender-conscious militarized manhood. The article concludes by identifying potential practical implications of this discourse. [ABSTRACT FROM AUTHOR]

Botnets have become a severe security threat not only to the Internet but also to the devices connected to it. Factors like the exponential growth of IoT, the COVID-19 pandemic, and the ever-larger number of cybercriminals are incentivising the growth of botnets in this domain. The recent outbreak of botnets like Dark Nexus (derived from Qbot and Mirai), Mukashi, LeetHozer, Hoaxcalls, etc. shows the alarming rate at which this threat is converging. The botnets have attributes that make them an excellent platform for malicious activities in IoT devices used by organisations to safeguard the personal and confidential data of their customers, employees, and business partners. The IoT devices have built-in sensors or actuators that can be exploited to monitor or control the physical environment of the entities connected to them, thereby violating the fundamental concept of privacy-by-design of these devices. In this paper, we design and describe a modular botnet framework for IoT. The framework uses an enhanced centralised architecture associated with a novel ‘Domain Fluxing Technique’. The proposed framework will provide insights into how privacy in IoT devices can be incorporated at design time to check the sensors and actuators in these devices against malicious exploitation, consequently preserving privacy. [ABSTRACT FROM AUTHOR]

Recently, Metaverse is gaining prominence within the field of radiology due to its potential to revolutionize image visualization. Radiologists can harness its capabilities to access dynamic, highly detailed results, thereby enhancing diagnostic precision. Digital twins, at the core of the Metaverse, are digital replicas of real-world objects and entities. They serve as the foundational building blocks, enabling the creation of virtual counterparts for everything within the Metaverse. To ensure the reliability of these digital twins, blockchain technology offers a multi-dimensional data storage solution, reinforcing data integrity and trustworthiness. It is used to ensure a transparent and 3D visualization of each communication and interaction for further looking up any criticality if present in the network. With the rapid increase in value and volume of data, the evolution of the metaverse faces a number of privacy and security concerns. Furthermore, the metaverse in Industry 4.0 is a trending topic that further needs to focus on its security challenges at its initial stage. Fortunately, blockchain is considered as one of the significant solutions. The aim of this paper is to propose a secure and efficient fuzzy evaluation matrix by computing the trust values along with integrating with blockchain mechanism in industry 4.0 enabling metaverse environment. The proposed mechanism is validated against various security concerns such as broken authentication, eavesdropping, personal information leakage, data injection, and unauthorized access. The proposed mechanism showed the validation rate against existing schemes with an improvement of 94% in comparison of several security metrics. [ABSTRACT FROM AUTHOR]

Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual's identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information. [ABSTRACT FROM AUTHOR]

This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper's structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are a critical component of the Internet of Things (IoT) which can be used in various fields, including environmental monitoring, smart agriculture, etc. However, WSNs used in environmental monitoring particularly in border areas are vulnerable to various security threats caused by illegal intruders who perform eavesdropping, garnishing, gray-hole attacks, etc. to compromise the network and forge the sensitive data. A multi-level hierarchical secure and optimal routing (ML-HSOR) protocol is proposed to overcome these issues. The proposed ML-HSOR protocol has four stages: registration, clustering, authentication, and optimal routing. In the registration step, the new sensor nodes are registered with the base station (BS) using unique identities. In the clustering stage, a Markov model with adaptive weighting mechanism is used to choose the most suitable node as the cluster head (CH), enhancing network lifespan and performance. The authentication stage involves a multi-level trust evaluation to detect malicious nodes. The aggregated message and timestamp are encrypted, and finally, the optimal data transmission path is chosen based on the polarity learning-based chimp optimization algorithm (PL-COA). This approach computes node fitness based on residual energy, distance, link quality, delay, and trust, ensuring high packet delivery ratio (PDR) (99.8% for 500 nodes), high throughput (48,000 bits per second for 500 nodes), less delay (0.058s for 500 nodes), high detection rate (95%), and less energy consumption (0.39 J for 500 nodes). [ABSTRACT FROM AUTHOR]

In cloud computing environment, the workflow allocation mainly focuses on task assignment to satisfy the quality-of-service (QoS) standards and workload balancing by the best use of the demanded resources as a service on pay per use basis to optimize objectives from service level agreements. In secured workflow allocation (SWA), the intermediate data transfers in sensitive workflow tasks demand security requirements during the execution across the virtual machines. Security-aware workflow execution challenges result in a significant share of security overhead which affects the QoS performance. The aim of this work is to facilitate the researcher's selection of suitable SWA approaches from the existing approaches in the literature. To achieve this, we have comprehensively reviewed the current state-of-the-art approaches of the SWA models in cloud computing environments. Also, a secured workflow allocation challenges taxonomy by describing the security overhead model and QoS objectives and constraints is presented. Finally, it determines and discusses the existing open issues, challenges, and future research direction in the SWA model based on the study's literature review. The study will help researchers in the domain with current security challenges for SWA research trends. [ABSTRACT FROM AUTHOR]

The aim of the study was to isolate and characterize seven strains of Bacillus from the intestinal tract of healthy Columba and to evaluate their probiotic functions and safety. The probiotic performance was screened by identifying its colony morphology, staining characteristics, biochemical tests, 16s rRNA, growth curve, heat tolerance, acid-base tolerance, bile salt tolerance, enzyme production capacity, and survival rate in an artificial simulated gastrointestinal fluid environment, and safety evaluation by antibiotic resistance, strain hemolysis, and mouse safety test. The results showed that seven strains of Bacillus were obtained, and three strains with better tolerance, enzyme-producing capacity, probiotic properties and safety were screened out. They grew well in liquid medium at pH 5 to 8; all had survival rates above 30% at 65 ℃ for 10 min; all had survival rates above 84% after 24 h in 0.07% bile salt; all had survival rates above 42% after 3 h in simulated gastrointestinal fluid; all could produce Protease, α-amylase, and cellulase were produced; they were not sensitive to lincomycin; none of them produced hemolysin; the mice after gavage were mentally normal and there were no pathological changes in the organs. In summary, the three Bacillus strains obtained from the intestine of healthy domestic pigeons have good probiotic properties and can be used as alternative strains for microecological preparations. [ABSTRACT FROM AUTHOR]

As NATO marks its 75th anniversary it is facing a future more uncertain than at any point since the end of the Cold War. The European security environment is changed and challenged by the ongoing Russian invasion of Ukraine, while the Israel-Gaza war poses further challenges. Given this critical moment, this article offers a strategic intervention to highlight and reinforce the essential role of the Women, Peace, and Security (WPS) agenda in defence and deterrence, including through the integration of a gender perspective, for NATO members and partners. It does so by bringing together former Chairs of the NATO Committee on Gender Perspectives (NCGP) into conversation about the continued importance of NATO’s oldest committee in ensuring NATO’s future approach to WPS is fit for purpose. Drawing upon their reflections and insights, we recommend policy points to embed and advance WPS and the gender perspective in NATO’s response to this new security environment. [ABSTRACT FROM AUTHOR]

In today's world, the Internet of Things (IoT) plays a major role to interconnect all the devices and improve the overall Quality of Life (QoL) for people. The main concern among IoT systems revolve around three pillars namely security, confidentiality, and privacy owing to the sensitive nature of the data being transmitted and processed byIoT devices. Traditional cryptographic approaches address these concerns by ensuring the authenticity and confidentiality of IoT systems. However, the majority of IoT devices are resource-constrained, which implies that they operate under significant resource constraints such as limited computational power, constrained battery life, physical compactness, and restricted memory capacity. To this end, Lightweight cryptography (LWC) offers methods specifically designed to accommodate the limitations of resource-constrained IoT devices. This work establishes the role of light weight cryptography for such resource constrained IoT networks in terms of security perspectives. In this work, we explore the security vulnerabilities of IoT systems and the associated lightweight cryptographic methods highlighting four components namely lightweight block ciphers, lightweight stream ciphers, hash functions, and Elliptic Curve Cryptography. The work further discusses the role of LWC and reviews the recent advancements in different sectors of IoT such as smart city, industries, healthcare, smart grids, and agriculture. Finally, several open research directions are highlighted in order to guide future LWC and IoT researchers. [ABSTRACT FROM AUTHOR]

Microfluidic-based biochip is a prominent class of lab-on-a-chip systems for the healthcare, biomedical, and biochemical industries, and was recently used for the detection of the Covid-19 pandemic. This paper is a survey of current attack models in different proactive and reactive remedial actions. Finally, this paper also aims at future directions for researchers to work towards enhancing security and privacy challenges in state-of-the-art microfluidic biochips. [ABSTRACT FROM AUTHOR]

Summary: Since its inception, the metaverse has evolved drastically and opened up many opportunities for many stakeholders, including individuals and businesses. The agri‐food supply chain is currently starting to incorporate metaverse technology. Retailers and prominent companies use this technology to achieve particular goals, such as ensuring traceability or boosting sales and reputation. Concurrently, there is an opportunity for the metaverse to affect food chain performance more broadly. Even so, the literature provides little evidence for this effect and primarily focuses on specific indicators, leaving little knowledge about it. By exploring the rapidly evolving field of metaverse technologies and their potential impacts on the security of agri‐food supply chains, this study aims to fill this knowledge gap. The study aims to improve collaboration and communication throughout the agri‐food supply chain by providing a structured framework covering a variety of metaverse technologies, from the development of immersive virtual worlds to the deployment of blockchain for traceability. The findings offer valuable insights, particularly for researchers, decision makers, and industry partners who aim to better comprehend the potential benefits and challenges related to integrating metaverse solutions into the agri‐food industry. The study concludes with recommendations for future research directions and strategic factors that could assist in ensuring the effective adoption of metaverse technologies and the security and resilience of agri‐food supply chains. [ABSTRACT FROM AUTHOR]

The interconnection of Operational Technology (OT) and Information Technology (IT) has created new opportunities for remote management, data storage in the cloud, real-time data transfer over long distances, or integration between different OT and IT networks. OT networks require increased attention due to the convergence of IT and OT, mainly due to the increased risk of cyber-attacks targeting these networks. This paper focuses on the analysis of different methods and data processing for protocol recognition and traffic classification in the context of OT specifics. Therefore, this paper summarizes the methods used to classify network traffic, analyzes the methods used to recognize and identify the protocol used in the industrial network, and describes machine learning methods to recognize industrial protocols. The output of this work is a comparative analysis of approaches specifically for protocol recognition and traffic classification in OT networks. In addition, publicly available datasets are compared in relation to their applicability for industrial protocol recognition. Research challenges are also identified, highlighting the lack of relevant datasets and defining directions for further research in the area of protocol recognition and classification in OT environments. [ABSTRACT FROM AUTHOR]

This paper addresses the developments witnessed by the concept of security in strategic studies, based on the insights of the Egyptian thinker Bahgat Korany, who formulated the concepts of high and low politics. The former is linked to traditional military and security domains, while the latter is associated with social, economic, and cultural spheres, which current historical contexts have highlighted the importance of, particularly for countries described as lagging behind. The study adopts the cultural security model through analyzing the contemporary Arab world's reality, demonstrating that cultural security plays a fundamental role in securing various other sectors, especially in preserving identity and cultural distinctiveness. [ABSTRACT FROM AUTHOR]

Research Summary: The increasing presence of armed security personnel in schools has led to concerns about the criminalization of students. While many schools use sworn law enforcement (e.g. school resource officers or SROs), many schools also use security guards who are not sworn law enforcement. Recent policy changes in a number of states and political pressures resulting from acts of mass school violence have led policymakers to face decisions between the use of SROs and security guards. This study informs this policy debate by empirically examining the relationships between security guards and SROs with rates of student behavior, discipline, and arrest using nationally representative data from the Civil Rights Data Collection. Controlling for observable covariates and school district fixed effects, we find that, in contrast to SROs, the use of security guards in schools is not systematically related to student arrest or suspension rates. The findings suggest that non‐sworn security guards may provide a form of security personnel that minimizes the criminalization of students relative to SROs. Policy Implications: As school districts nationwide grapple with how to respond to school shootings and other acts of school violence, policymakers have frequently turned to the use of school security personnel, including SROs and security guards, as a response. Doing so, however, has been contentious given concerns about the overcriminalization of students. This study provides evidence to inform these policy debates by demonstrating the relationships between security guards and SROs and a number of outcomes. The results suggest that the use of security guards as a policy response may minimize arrests and exclusionary discipline relative to the use of SROs. [ABSTRACT FROM AUTHOR]

Summary: The Maintenance of Wakefulness Test is widely used to objectively assess sleepiness and make safety‐related decisions, but its interpretation is subjective and normative values remain debated. Our work aimed to determine normative thresholds in non‐subjectively sleepy patients with well‐treated obstructive sleep apnea, and to assess intra‐ and inter‐scorer variability. We included maintenance of wakefulness tests of 141 consecutive patients with treated obstructive sleep apnea (90% men, mean (SD) age 47.5 (9.2) years, mean (SD) pre‐treatment apnea–hypopnea index of 43.8 (20.3) events/h). Sleep onset latencies were independently scored by two experts. Discordant scorings were reviewed to reach a consensus and half of the cohort was double‐scored by each scorer. Intra‐ and inter‐scorer variability was assessed using Cohen's kappa for 40, 33, and 19 min mean sleep latency thresholds. Consensual mean sleep latencies were compared between four groups according to subjective sleepiness (Epworth Sleepiness Scale score < versus ≥11) and residual apnea–hypopnea index (< versus ≥15 events/h). In well‐treated non‐sleepy patients (n = 76), the consensual mean (SD) sleep latency was 38.4 (4.2) min (lower normal limit [mean – 2SD] = 30 min), and 80% of them did not fall asleep. Intra‐scorer agreement on mean sleep latency was high but inter‐scorer was only fair (Cohen's kappa 0.54 for 33‐min threshold, 0.27 for 19‐min threshold), resulting in changes in latency category in 4%–12% of patients. A higher sleepiness score but not the residual apnea–hypopnea index was significantly associated with a lower mean sleep latency. Our findings suggest a higher than usually accepted normative threshold (30 min) in this context and emphasise the need for more reproducible scoring approaches. [ABSTRACT FROM AUTHOR]

This study explored the association between renewable energy uptake and digitalization in the sector by reviewing relevant literature (published 2010–2022), with the aim of identifying the existing utilization of digital technologies within the sector, challenges to adoption, and future prospects. Different search engines (SCOPUS, Web of Science, and Google Scholar) were used to locate relevant papers and documents. The results revealed the high significance of digital technologies in supporting the renewable energy sector, with high costs and security risks representing the key challenges. Most papers reviewed had a positive outlook, but recommended further research and development for effective energy transition and resilient infrastructure. The current drivers of the integration of digital technologies to support the diffusion of renewable energy sources appear to extend beyond energy demand and involve many aspects of sustainability and sustainable development. Compared with previous reviews, this work has unique scope and novelty since it considers the bigger picture of the coupling between digitalization and the renewable energy sector, with a greater focus on critical areas in these two interconnected bodies that need to be addressed. The relatively small sample of relevant papers (69 from 836 hits) located in the literature review confirms the need for more research covering the subject in greater depth. [ABSTRACT FROM AUTHOR]

In the past two decades, we have seen the proliferation of Internet and its applications. The information exchange happens via images, videos, texts, audio clips, etc. Medical experts use images as the most common form of information exchange such as X-rays, MRI scans, CT scans, etc. These scanned images contain confidential information about a patient. With images being the most attractive mode for medical information exchange, it becomes the target of many security attacks. Many researchers have put out their best efforts to protect the images from these attacks by proposing several techniques. Despite all efforts, many cryptosystems still compromise with one or more key attributes like efficiency or time required for them to be top-notch. Along with an appreciation of work, careful inspection is also required that can influence the future work and make them better. Hence, this survey aims to summarize and analyze the existing chaos-based medical image encryption techniques introduced in the last five years. The paper starts with a brief introduction about the basic chaos maps, security parameters, and then, progresses to analysis of the encryption algorithms based on categories followed by comparative study and open security issues. [ABSTRACT FROM AUTHOR]

Strategic narratives have become an important tool with which states define their geopolitical reality and shape the types of foreign policy decisions that emerge. In order to build a more favourable international environment, China has deployed substantial resources to disseminate its strategic narratives and communicate its role, identity and vision and legitimise Chinese Communist Party (CCP) rule. Despite the obvious importance of narratives on conflict and global security, remarkably few studies have examined China's strategic narratives on security-related issues. This article marks the first systematic effort to map out China's strategic narratives in the context of the ongoing conflict in Ukraine. Based on a corpus linguistics approach and qualitative content analysis, the findings reveal that China does not project itself as a neutral player and that its strategic narratives often echo Russia's portrayal of the Ukrainian war. However, the analysis also highlights China's efforts to maintain its distance from Russia while presenting itself against the backdrop of the US/West-Russian rivalry as an equidistant player belonging to the wider international community as well as the most suitable actor to manage a peaceful global order. [ABSTRACT FROM AUTHOR]

Nowadays, it is evident that the traditional power grid system is changing to smart grid in a revolutionary and evolutionary manner. Compared to other systems, advanced metering infrastructure has three distinctive features that must be carefully considered, including hybrid mode of message transmission, constraints on the computation and storage capability of smart meters ( S M s ) and variable number of participants in demand response (DR) projects. In order to ensure confidentiality, integrity and availability, an efficient and robust scheme for key management is required for secure two-way transmission between S M s and data control unit ( D C U ), which is still an unsolved problem. To deal with the security requirements and consider the specific features, this paper proposes a novel key management scheme that drastically reduces the computation cost of the D C U and S M s . Further, in the proposed scheme, there is no need to change the keys of existing S M s when a S M joins/leaves the group. Therefore, the computation cost of D C U and S M s for updating the keys is reduced to O (1). Finally, we have analyzed the performance and security of our scheme which evidently shows that our proposed scheme provides better results than the related schemes. [ABSTRACT FROM AUTHOR]

The rise of exclusionary populism is widely regarded as one of the most significant phenomena in today's political world. Despite this, the relationship between populism and security remains under-explored in the literature, including the affective power of populist security narratives. Against this background, this paper conducts a comparative analysis of radical right populist discourse in response to two recent shocking crimes in France and Ireland. The different expression given to security concerns in the two countries, such as a much less antagonistic 'flaunting of the low' in France, is suggestive of a more contingent and institutionally mediated relationship between security and populism than the existing literature would suggest. [ABSTRACT FROM AUTHOR]