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Autonomous navigation is currently subject to particular interest for naval and commercial vessels. In addition to the regulatory effort, several pieces of research tackled the development of new guidance laws, stable and robust control algorithms, methodologies to increase situational awareness, and collision avoidance algorithms. However, most of these systems blindly trust information from navigation sensors, which can malfunction or, even worse, have their data altered by cyber-attacks. This last scenario will be increasingly common shortly and represents a dangerous threat that future highly computerised ships must face. The proposed work shows an approach capable of hijacking a state-of-the-art collision avoidance algorithm route by feeding it with rogue data. The effects of such a cyber-attack are shown via an extensive simulation campaign, testing the methodology on three different COLREG scenarios: head-on, crossing, and overtaking. The study additionally highlights the susceptibility of forthcoming navigation systems to security breaches, which still needs to be addressed in the existing literature. According to the experimental findings, attackers can attract the autonomous ship guidance toward their objective at a mean distance of $ 133\,{\rm m} $ 133 m . Finally, suggestions on how to harden these systems against this family of attacks are provided in the conclusion. [ABSTRACT FROM AUTHOR]

Security systems play a crucial role in protecting individuals and assets within diverse infrastructures, including seaports. The uninterrupted operation of these systems heavily relies on a continuous power supply, as any disruptions can lead to severe consequences. Therefore, security systems are classified as critical loads requiring uninterrupted power availability. This study focuses on the investigation of an optimal hybrid energy system (HES) to ensure a reliable power supply for security systems in two seaports located in Turkiye. Through the utilization of the HOMER software, optimization analyses were conducted, considering both conventional sources such as grid-generator or grid-generator-battery configurations, as well as off-grid and on-grid HES solutions integrating photovoltaic (PV) and wind turbine technologies. The findings reveal that on-grid HES solutions incorporating PV and wind technologies offer a more cost-effective and dependable energy supply for security systems in seaports, surpassing traditional alternatives. This study represents a significant contribution to the existing literature, as it presents the first comprehensive optimization study on the design of HES for security systems. The outcomes serve as a valuable reference for future research endeavors in this field. [ABSTRACT FROM AUTHOR]

Use of firearm locking devices may reduce the risk of suicide and injury among older adults. This study describes older adults' preferences when choosing a firearm locking device. We conducted a secondary analysis of a nationally representative survey of US adult firearm owners (N = 2,152). We compared older adults (≥65y) with relatively younger adults (<65y), stratified by self-reported gender. The top three factors cited as impacting firearm locking device selection included speed (53.6%) and ease of firearm access from device (52.4%), and cost of the device (28.7%). These top factors were comparable for all adults across genders. A larger proportion of older vs younger males reported that a primary preference was whether the device allows the firearm to remain loaded with ammunition; smaller proportions of older vs younger males reported strength of device (device durability) and costs. Preference among older adults, particularly older males, for locking devices that maintain the firearms easy to access – especially, loaded with ammunition – might impact firearm injury prevention efforts for this high-risk group. For harm reduction, use of any firearm locking device may reduce the risk of firearm injury or death. Clinicians are encouraged to explore reasons for locking device selection within motivational interviewing frameworks. [ABSTRACT FROM AUTHOR]

In recent years, the NATO member nations have committed to a coordinated approach to strengthening resilience among the Allies, including the development of National Resilience Plans (NRPs). The Allies outlined the extent to which the robustness of their respective military capacities requires the designed resilience of systems that bridge civilian and military domains. This article outlines the role that resilience plays in supporting tactical and strategic measures of national security and defense within military and civilian domains. This exploration provides an outline of how resilience is currently applied in practice by the U.S. Department of Defense (DOD) and NATO. Building on this diversity of applications, various categorical forms of resilience drawn from the empirical science of resilience are positioned within NATO’s emerging frame for ‘layered’ resilience. This article reinforces the scientific debate that an optimal orientation to resilience leaves open the door for the transformative adaptation of function and identity when the single-equilibrium processes of resilience reach their limits. This article concludes with a normative perspective on how military and civilian resilience planning could support the development of NRPs that would amplify the Allies’ collective capacity to face shared security threats. [ABSTRACT FROM AUTHOR]

The advent of Internet of Things (IoT) devices has revolutionized our daily routines, fostering interconnectedness and convenience. However, this interconnected network also presents significant security challenges concerning authentication and data integrity. Traditional security measures, such as Public Key Infrastructure (PKI), encounter limitations when applied to resource-constrained IoT devices. This paper proposes a novel decentralized PKI system tailored specifically for IoT environments to address these challenges. Our approach introduces a unique "zone" architecture overseen by zone masters, facilitating efficient certificate management within IoT clusters while reducing the risk of single points of failure. Furthermore, we prioritize the use of lightweight cryptographic techniques, including Elliptic Curve Cryptography (ECC), to optimize performance without compromising security. Through comprehensive evaluation and benchmarking, we demonstrate the effectiveness of our proposed solution in bolstering the security and efficiency of IoT ecosystems. This contribution underlines the critical need for innovative security solutions in IoT deployments and presents a scalable framework to meet the evolving demands of IoT environments. [ABSTRACT FROM AUTHOR]

The paper is based on the activities of the Maritime Search and Rescue Service units in Poland. The information and data contained in the article were obtained through an interview with the rescuers of the Maritime Rescue Coordination Centre in Gdynia. The analysis aims to illustrate the impact of the activities of SAR units in the structures of maritime transport security systems in the Polish SAR zone of responsibility. In addition, the organizational structures and equipment of the rescue units were neatly described. The results of the analysed accidents at sea in terms of presenting similar events in the future are also included in this paper. [ABSTRACT FROM AUTHOR]

The objective of this study was to examine the roles that organizational security climate and perceived costs and rewards of compliance play in predicting the extent to which people endorse compliance or violation of specific information security policies (ISP). A low-fidelity simulation placed participants in either a strong or weak information security climate and presented them with four cybersecurity scenarios that assessed their judgments of complying with or violating security policies in those situations. Results indicated that information security climate relates to intent to comply with a company's security policies via attitudes, subjective norms, and perceived behavioral control, in line with the predictions of the theory of planned behavior (Ajzen, 1991). Strong intentions to comply with policies, in turn, were associated with greater endorsement of compliant behaviors and decreased endorsement of policy violations in the specific scenarios. However, whether or not individuals chose to endorse compliance with or violation of specific policies, after initial intentions were formed, was also influenced by their perceived costs and rewards of compliance. The effects of costs were particularly strong: as perceived costs increased, participants were more likely to endorse ISP violations. Our findings suggest that establishing a strong information security climate may reduce the chances of security breaches, but that organizations should also intervene to reduce the perceived burden and inconvenience of security tasks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is revolutionising our everyday life by connecting a massive number of smart objects. IoT is considered a key enabler of multiple emerging applications in several areas such as manufacturing, agriculture, healthcare, automotive, smart city, etc. However, as it becomes increasingly prevalent in our life, IoT brings a number of challenging new security risks. The first issue is related to device authentication, especially with the massive number of connected devices with different types (sensors, vehicles, drones, etc.). This issue can be exploited by attackers to break into IoT networks. In addition, IoT devices generate a huge amount of confidential data that must be processed and transmitted independently without human intervention, which cannot be performed by traditional centralized security systems. Besides, the isolation imposed between different networks and the number of messages used for device's association are considered the main limitations in the two recent works. To overcome these limits, we design a novel decentralized scheme leveraging a hybridization of two main types of blockchain, local and public, which ensures authentication of things before establishing communication and exchanging information between devices. Our solution removes the isolation between different networks, contributes in reducing the number of messages required for the association phase, and solves the time delay problem imposed by the PoW consensus mechanism that is used in public blockchain as well. We adopt the Ethereum blockchain in its two versions local and public to implement our solution, and evaluate its performance as compared to the existing literature. The obtained results proved its effectiveness in terms of execution time and energy consumption which are the same metrics used in the most recent works. [ABSTRACT FROM AUTHOR]

Background and Aims: Retroactive jealousy is an unhealthy interest in a partner's romantic/sexual history that has a detrimental effect on sufferers and relationships. Significant numbers of people seek therapeutic help for retroactive jealousy each year, but no research has used their lived experiences to identify potential therapeutic interventions. That was the aim of this research. Methods: Seven adults (21–43 years) who had sought help for retroactive jealousy were interviewed about their experiences. Participants' accounts were analysed with reflexive thematic analysis, and findings were used to identify potential therapeutic interventions. Findings: Three main themes represented participants' experiences. Fears threaten hope and security involved negative self‐comparisons with past rivals, a sense of one's partner and the relationship losing value and of feeling wronged. Feeling compelled to know about the past involved one's mind as a creative generator of distress and feeling compelled to seek reassurance but making things worse. Feeling split and out of control involved feeling like a devil was on one's shoulder and having retroactive jealousy as a constant background to relational life. Implications: Therapeutic guidance for change and growth can be directed at participants' desires to forge a special romantic relationship, to achieve a more stable sense of self, and to act consistently with their underlying values and not in response to their visceral fears. We offer suggestions for psychoeducation regarding characteristics of healthy relationships, for working to move from insecure to secure responses, and for integrating cognitive defusion and implementation intentions to enhance agency. [ABSTRACT FROM AUTHOR]

Robust data security is of paramount importance, particularly for highly classified information. To achieve this, advanced encryption and decryption methods, along with stringent access control and physical security measures, are essential to mitigate the risk of unauthorized access and data exposure. Ghost imaging (GI), a computational imaging technique that operates indirectly, offers a distinct approach compared to traditional spatially resolved imaging techniques. By utilizing computational algorithms, GI enables indirect image reconstruction, making it well‐suited for encryption applications. This proposal introduces a new computational imaging encryption scheme that combines Fourier ghost imaging (FGI) and computational holography. The scheme utilizes an untrained‐tree‐network to generate two camouflaged computer‐generated holograms (CGHs). Integration of CGH‐based steganography and holographic authentication enhances the encoding and decoding processes of FGI encryption. The effectiveness and security of the proposed scheme are rigorously validated through numerical simulations and optical experiments. The results demonstrate promising potential for effectively merging direct and indirect imaging methods in security applications. [ABSTRACT FROM AUTHOR]

This study employs various bibliometric analysis techniques to examine the intellectual structure of the International Journal of Information Security from 2007 to 2023. The aim is to identify the most cited journals, underlying research themes within the article corpus, and gradual changes in the research themes over time. "Lecture Notes on Computer Science" is the most referenced knowledge source. Underlying research themes were identified based on mapping the bibliographically coupled articles on to the knowledge areas from the Cyber Security Body of Knowledge using template analysis. Applied Cryptography is the most prominent knowledge area, followed by Privacy, and Network Security. Additionally, research on distributed systems security and Web & Mobile Security were emerging topics of interest. Qualitative and quantitative comparisons between open-access and regular articles suggested a few notable differences in author keywords but no differences in the number of citations received. Furthermore, regression analysis found a negative correlation between citation counts with the length of the article abstract and article title and a positive correlation with page count, being published in a special issue, and if at least the affiliation of one of the authors is different from others. Finally, prominent authors, articles, institutions, and countries published in this journal were also identified. [ABSTRACT FROM AUTHOR]

Cybersecurity is an essential part of IoT device functionality. Malicious acts cause the disclosure of private data, putting device performance at risk. As a result, creating efficient security solutions for authentication and confidentiality of both IoT devices and data exchange and integrity of data exchange networks has become a significant challenge. Furthermore, the traditional security mechanisms' high computation demands are not suitable for resource-constrained specific IoT devices. Therefore, we have proposed a security system for IoT applications using blockchain called BLISS, which ensures robust identification, authentication, confidentiality, and integrity of IoT devices and data exchange. Using smart contracts, the BLISS creates trustful clusters of IoT devices through an authentication process for data exchange. The BLISS is implemented on the Raspberry Pi 4 and the desktop PC, considering the Raspberry Pi 4 as an IoT device and the desktop as a cluster head. The performance analysis of the BLISS demonstrates the enhanced performance in the context of computation and energy consumption, which is 62–65% reduced. The storage and communication overhead is reduced by up to 70% compared with state-of-the-art schemes. The security analysis showed that the proposed scheme withstands many IoT-specific cyber threads. [ABSTRACT FROM AUTHOR]

The rapidly evolving field of biometric recognition and verification technologies has seen substantial progress recently. However, the storage of biometric data in cloud environments presents significant security challenges, including data theft and unauthorized modifications. Cancelable biometrics emerge as a strategic solution to these issues. This study examines the efficacy of diverse cancelable biometric methods, classified into transformation, encryption, and hybrid approaches. We introduce a novel cancelable biometric system that combines a hybrid optical asymmetric phase truncated Fourier transform (PTFT) with a symmetric cipher scheme, enhanced by a two-level block-based shuffling technique. This innovation improves security through a streamlined shuffling process that divides the PTFT output into four sections for shuffling and an exclusive OR operations with a unique key, enhancing security and computational efficiency. Our empirical evaluation on three distinct datasets (Faces, Iris, and Fingerprints) from FERET, UPOL and CASIA, and FVC2002 DB1 and DB2, respectively, demonstrates superior performance of our system compared to traditional PTFT-based systems. Notable performance metrics include an Area under the receiver operating characteristic curve of 0.9998. Significant improvements were also observed in false acceptance and false rejection ratios, along with more uniform histogram distributions, thereby enhancing randomness in the resultant deformed patterns. From a security standpoint, our system exhibits a high entropy of 7.95 and achieves near-optimal results in the number of pixels change rate and unified average changing intensity, with values of 98.9115 and 23.9755%, indicating robust resilience against potential differential attacks. The research findings, supported by detailed tables and graphical illustrations, underscore the additional security benefits offered by the two-level encryption mechanism. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) encompasses smart devices, manufacturing systems, humans, and networks for automated productive outcomes. The placement of devices and networks is vulnerable to distributed denial of service (DDOS) attacks that degrade the productivity and efficiency of IIoT. In this article, we propose a checkpoint‐intrigued adversary mitigation scheme (CIAMS) for improving the security features and recommendations of the detection systems. Features that use the recommendation to provide relevant information maintain their security level. A DDoS attack is dealt with at the outset, resulting in increased productivity. The IIoT's smart devices are less productive and efficient because of this DDoS attack. This CIAMS method is designed to address vulnerability and the ability to survive the features checkpoints. The proposed scheme substantiates the security breach and lag in the checkpoint systems against DDOS attacks. The checkpoints' vulnerability level and surviving features are assessed using a classified learning approach. In this assessment, the degrading features are reimbursed by improving the security functions, control, and access methods. Periodic checkpoint replacement and mutual security measures are used for mitigating the prolonging DDOS impact in the network. The proposed scheme's performance is verified using false positives, service distribution, lag, an efficiency score. Improvements have been made to the industrial environment's service delivery and efficiency. By reducing false positives by 10.35%, the proposed scheme improves service distribution ratio and efficiency score by 11.68% and 12.55% for different devices. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor real-time processing and low security performance caused by the massive information of the Internet of things, a security early warning model of the Internet of things based on deep learning in edge computing environment is proposed. Firstly, the system architecture of the Internet of things is designed by using the edge computing technology, in which the intelligent router is used to obtain the network stub and send it to the nearest edge computing node for anomaly detection. Then, the attention mechanism is used to improve the long short-term memory network (LSTM), and the multidimensional LSTM model is constructed. At the same time, it is used to analyze the combined network data. Finally, according to the set threshold value, judge whether there is abnormal behavior in the network, and give early warning in time to take security defense measures. The experimental analysis of the proposed model based on NS2 simulation platform shows that its early warning success rate and time are 95.2% and 20.6 ms, respectively, and it can detect and defend various network attacks well, with high security performance. [ABSTRACT FROM AUTHOR]

The increasing popularity and adoption of the Internet of Things (IoT) ecosystem in various domains has brought attention to the security breaches linked with this paradigm. As the number of IoT devices continues to grow, it is essential to ensure that they are secured to protect against potential threats and attacks. IoT network proliferation of interconnected devices has significantly raised security concerns making them attractive targets for cyber attackers seeking to gain unauthorized access to systems and cause disruptions. As IoT networks collect and transmit sensitive data using centralized architecture, ensuring security and integrity of these networks becomes paramount. Distributed Ledger Technology (DLT) has emerged as a promising solution for enhancing IoT security. Two prominent DLT platforms: Blockchain and Internet of Things Application (IOTA) technologies can provide a more secure and resilient foundation for IoT ecosystems, and also help to mitigate risks associated with central node vulnerabilities. DLT-based IoT systems can also enable the creation of decentralized marketplaces and autonomous agents that can operate without human intervention. The objective of this research is to offer a comprehensive as well as fundamental study of IoT ecosystems and its associated security risks. Moreover, this paper provides a holistic study of the DLT platform and bibliometric inspection using VoS viewer tool on generic DLT platform technologies i.e., Blockchain and IOTA for securing data in IoT ecosystem. By leveraging bibliometric insights resulting from both DLT technologies, this study identities the most promising areas for further investigation and contribute to advancing security in IoT ecosystems. This survey contributes to the ongoing discourse on IoT security by providing a thorough comprehensive comparison of DLT solutions i.e., Blockchain and IOTA technologies on various key metrics, revealing that IOTA technology is projected to offer significant improvements over blockchain in securing sustainable IoT ecosystems. [ABSTRACT FROM AUTHOR]

Robust face clustering enjoys a wide range of applications for gate passes, surveillance systems and security analysis in embedded sensors. Nevertheless, existing algorithms have limitations in finding accurate clusters when data contain noise (e.g., occluded face clustering and recognition). It is known that in subspace clustering, the ℓ 1 - and ℓ 2 -norm regularizers can improve subspace preservation and connectivity, respectively, and the elastic net regularizer (i.e., the mixture of the ℓ 1 - and ℓ 2 -norms) provides a balance between the two properties. However, existing deterministic methods have high per iteration computational complexities, making them inapplicable to large-scale problems. To address this issue, this paper proposes the first accelerated stochastic variance reduction gradient (RASVRG) algorithm for robust subspace clustering. We also introduce a new momentum acceleration technique for the RASVRG algorithm. As a result of the involvement of this momentum, the RASVRG algorithm achieves both the best oracle complexity and the fastest convergence rate, and it reaches higher efficiency in practice for both strongly convex and not strongly convex models. Various experimental results show that the RASVRG algorithm outperformed existing state-of-the-art methods with elastic net and ℓ 1 -norm regularizers in terms of accuracy in most cases. As demonstrated on real-world face datasets with different manually added levels of pixel corruption and occlusion situations, the RASVRG algorithm achieved much better performance in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

As an enhanced version of standard CAN, the Controller Area Network with Flexible Data (CAN-FD) rate is vulnerable to attacks due to its lack of information security measures. However, although anomaly detection is an effective method to prevent attacks, the accuracy of detection needs further improvement. In this paper, we propose a novel intrusion detection model for the CAN-FD bus, comprising two sub-models: Anomaly Data Detection Model (ADDM) for spotting anomalies and Anomaly Classification Detection Model (ACDM) for identifying and classifying anomaly types. ADDM employs Long Short-Term Memory (LSTM) layers to capture the long-range dependencies and temporal patterns within CAN-FD frame data, thus identifying frames that deviate from established norms. ACDM is enhanced with the attention mechanism that weights LSTM outputs, further improving the identification of sequence-based relationships and facilitating multi-attack classification. The method is evaluated on two datasets: a real-vehicle dataset including frames designed by us based on known attack patterns, and the CAN-FD Intrusion Dataset, developed by the Hacking and Countermeasure Research Lab. Our method offers broader applicability and more refined classification in anomaly detection. Compared with existing advanced LSTM-based and CNN-LSTM-based methods, our method exhibits superior performance in detection, achieving an improvement in accuracy of 1.44% and 1.01%, respectively. [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]

Background: According to social-ecological models, the built and natural environment has the potential to facilitate or hinder physical activity (PA). While this potential is well researched in urban areas, a current systematic review of how the built and natural environment is related to PA in rural areas is lacking. Methods: We searched five databases and included studies for adults (18–65 years) living in rural areas. We included quantitative studies investigating the association between any self-reported or objectively measured characteristic of the built or natural environment and any type of self-reported or objectively measured PA, and qualitative studies that reported on features of the built or natural environment perceived as barriers to or facilitators of PA by the participants. Screening for eligibility and quality assessment (using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields) were done in duplicate. We used a narrative approach to synthesize the results. Results: Of 2432 non-duplicate records, 51 quantitative and 19 qualitative studies were included. Convincing positive relationships were found between the availability and accessibility of places for exercise and recreation and leisure-time PA as well as between the overall environment and leisure-time PA. Possible positive associations were found between the overall environment and total and transport-related PA, between greenness/natural environment and total PA, between cycling infrastructure and aesthetics and MVPA, and between pedestrian infrastructure and total walking. A possible negative relationship was found between safety and security and total walking. Qualitative studies complemented several environmental facilitators (facilities for exercise and recreation, sidewalks or streets with low traffic, attractive natural environment) and barriers (lack of facilities and destinations, lack of sidewalks, speeding traffic and high traffic volumes, lack of street lighting). Conclusions: Research investigating the relationship between the built and natural environment and PA behaviors of adults living in rural areas is still limited and there is a need for more high-quality and longitudinal studies. However, our most positive findings indicate that investing in places for exercise and recreation, a safe infrastructure for active transport, and nature-based activities are possible strategies that should be considered to address low levels of PA in rural adults. Trial registration: PROSPERO: CRD42021283508. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To examine patient and provider perspectives on privacy and security considerations in telemedicine during the COVID-19 pandemic. STUDY DESIGN: Qualitative study with patients and providers from primary care practices in 3 National Patient- Centered Clinical Research Network sites in New York, New York; North Carolina; and Florida. METHODS: Semistructured interviews were conducted, audio recorded, transcribed verbatim, and coded using an inductive process. Data related to privacy and information security were analyzed. RESULTS: Sixty-five patients and 21 providers participated. Patients and providers faced technology-related security concerns as well as difficulties ensuring privacy in the transformed shared space of telemedicine. Patients expressed increased comfort doing telemedicine from home but often did not like their providers to offer virtual visits from outside an office setting. Providers initially struggled to find secure and Health Insurance Portability and Accountability Act--compliant platforms and devices to host the software. Whereas some patients preferred familiar platforms such as FaceTime, others recognized potential security concerns. Audio-only encounters sometimes raised patient concerns that they would not be able to confirm the identity of the provider. CONCLUSIONS: Telemedicine led to novel concerns about privacy because patients and providers were often at home or in public spaces, and they shared concerns about software and hardware security. In addition to technological safeguards, our study emphasizes the critical role of physical infrastructure in ensuring privacy and security. As telemedicine continues to evolve, it is important to address and mitigate concerns around privacy and security to ensure high-quality and safe delivery of care to patients in remote settings. [ABSTRACT FROM AUTHOR]

We proposed and verified a scheme of chaos synchronization for integrated five-section semiconductor lasers with matching parameters. The simulation results demonstrated that the integrated five-section semiconductor laser could generate a chaotic signal within a large parameter range of the driving currents of five sections. Subsequently, chaos synchronization between two integrated five-section semiconductor lasers with matched parameters was realized by using a common noise signal as a driver. Moreover, it was found that the synchronization was sensitive to the current mismatch in all five sections, indicating that the driving currents of the five sections could be used as keys of chaotic optical communication. Therefore, this synchronization scheme provides a candidate to increase the dimension of key space and enhances the security of the system. [ABSTRACT FROM AUTHOR]

Purpose: Despite the growing concern about security breaches and risks emerging from Shadow IT usage, a type of information security violation committed by organizational insiders, this phenomenon has received little scholarly attention. By integrating the dual-factor theory, unified theory of acceptance and use of technology (UTAUT) and social control theory, this research aims to examine facilitating and deterring factors of Shadow IT usage intention. Design/methodology/approach: An online survey was performed to obtain data. As this study aims at investigating the behavior of organizational insiders, LinkedIn, an employment-oriented network site, was chosen as the main site to reach the potential respondents. Findings: The results show that while performance expectancy, effort expectancy and subjective norms considerably impact intention to use Shadow IT, personal norms and sanctions-related factors exert no influence. Besides, an organizational factor of ethical work climate is found to significantly increase individual perceptions of informal controls and formal controls. Originality/value: This work is the first attempt to extend the generalizability of the dual-factor theory and UTAUT model, which primarily has been utilized in the context of system usage, to the new context of information security. This study is also one of few studies that simultaneously take both organizational and individual factors into consideration and identify its impacts on user's behaviors in the information security context. [ABSTRACT FROM AUTHOR]

The article discusses the challenges of digitization in the German healthcare system. It emphasizes that coordinating information technology between doctors, hospitals, healthcare professions, health insurance companies, and IT service providers is difficult. It also points out that companies from the USA and China have had little influence on the communication and protection of health data on the internet so far. The article warns of the risk of commercializing health data processing and emphasizes the need for security measures and law enforcement. It notes that the protection of confidentiality in healthcare is not yet in line with automation and that the human should be at the center of health protection. [Extracted from the article]

Recently, we proposed an amplitude-shift keying asymmetric quantum communication system and evaluated its reliability when using the quasi-Bell state and two-mode squeezed vacuum state (TSVS) as an entangled light source. In this paper, we evaluate the security of the system and find that either security or reliability can be enhanced depending on the entangled light sources. We also consider an approach to enhance the security of the system as well as its reliability by increasing the number of signal modes. Interestingly, we find that the quasi-Bell state always performs better than the TSVS under certain conditions. [ABSTRACT FROM AUTHOR]

Device-independent quantum secure direct communication (DI-QSDC) is a promising primitive in quantum cryptography aimed towards addressing the problems of device imperfections and key management. However, significant effort is required to tackle practical challenges such as the distance limitation due to the decohering effects of quantum channels. Here, we explore the constructive effect of non-Markovian noise to improve the performance of DI-QSDC. Considering two different environmental dynamics modelled by the amplitude damping and the dephasing channels, we show that for both cases non-Markovianty leads to a considerable improvement over Markovian dynamics in terms of three benchmark performance criteria of the DI-QSDC task. Specifically, we find that non-Markovian noise (i) enhances the protocol security measured by Bell violation, (ii) leads to a lower quantum bit error rate, and (iii) enables larger communication distances by increasing the capacity of secret communication. [ABSTRACT FROM AUTHOR]

An amplitude modulator plays an essential role in the implementation of continuous-variable quantum key distribution (CVQKD), whereas it may bring about a potential security loophole in the practical system. The high-frequency modulation of the actual transmitter usually results in the high rate of the system. However, an imperfect amplitude modulator (AM) can give birth to a potential information leakage from the modulation of the transmitter. To reveal a potential security loophole from the high-frequency AM embedded in the transmitter, we demonstrate an influence on the practical security of the system in terms of the secret key rate and maximal transmission distance. The results indicate the risk of this security loophole in the imperfect AM-embedded transmitter. Fortunately, the legal participants can trace back the potential information leakage that has been produced from the imperfect transmitter at high frequencies, which can be used for defeating the leakage attack in CVQKD. We find the limitations of the imperfect AM-embedded transmitter of the high-frequency quantum system, and hence, we have to trade off the practical security and the modulation frequency of the AM-embedded transmitter while considering its implementation in a practical environment. [ABSTRACT FROM AUTHOR]

Named Data Networking (NDN) stands out as a prominent architectural framework for the future Internet, aiming to address deficiencies present in IP networks, specifically in the domain of security. Although NDN packets containing requested content are signed with the publisher's signature which establishes data provenance for content, the NDN domain still requires more holistic frameworks that address consumers' identity verification while accessing protected contents or services using producer/publisher-preapproved authentication servers. In response, this paper introduces the NDN One-Time Authentication (NDNOTA) framework, designed to authenticate NDN online services, applications, and data in real time. NDNOTA comprises three fundamental elements: the consumer, producer, and authentication server. Employing a variety of security measures such as single sign-on (SSO), token credentials, certified asymmetric keys, and signed NDN packets, NDNOTA aims to reinforce the security of NDN-based interactions. To assess the effectiveness of the proposed framework, we validate and evaluate its impact on the three core elements in terms of time performance. For example, when accessing authenticated content through the entire NDNOTA process, consumers experience an additional time overhead of 70 milliseconds, making the total process take 83 milliseconds. In contrast, accessing normal content that does not require authentication does not incur this delay. The additional NDNOTA delay is mitigated once the authentication token is generated and stored, resulting in a comparable time frame to unauthenticated content requests. Additionally, obtaining private content through the authentication process requires 10 messages, whereas acquiring public data only requires two messages. [ABSTRACT FROM AUTHOR]

To address the concerns about security and privacy in cloud-based data storage, this study proposes a unique approach to secure cloud-based data governance using hybrid machine learning architectures. With the increasing reliance on cloud resources and the need for improved security measures, this research aims to provide a solution that enhances data access governance while ensuring data privacy. To achieve this, the study introduces a Distributed Data Service (DDS) specifically designed for collecting and analyzing data in Internet of Things (IoT) contexts. The DDS enables various IoT middleware methods to exchange common data services from a loosely coupled provider. The functional specifications of the DDS are revised to facilitate effective data gathering, filtering, and storage in this environment. The proposed approach leverages distributed cloud architecture and hybrid machine learning techniques to establish cloud data access governance with enhanced security. A privacy-aware architecture is built upon distributed middleware services and supply chain quantum neural networks to ensure data privacy and protection. Additionally, multi-agent federated Bayes vector-based blockchain and partial homomorphic encryption techniques are employed to enhance network security. Throughout the experimental investigation, several performance metrics are considered. Scalability, latency, quality of service, data integrity, and time consumption are analyzed to assess the efficiency of the proposed approach. By addressing security and privacy concerns in cloud-based data storage through the utilization of hybrid machine learning architectures and distributed cloud infrastructure, this research provides a novel approach to cloud data access governance. Overall, the study offers a comprehensive solution to secure and privacy-aware data management in the cloud, paving the way for further advancements in this field. [ABSTRACT FROM AUTHOR]

In recent years, satellite communication systems (SCSs) have rapidly developed in terms of their role and capabilities, promoted by advancements in space launch technologies. However, this rapid development has also led to the emergence of significant security vulnerabilities, demonstrated through real-world targeted attacks such as AcidRain and AcidPour that demand immediate attention from the security community. In response, various countermeasures, encompassing both technological and policy-based approaches, have been proposed to mitigate these threats. However, the multitude and diversity of these proposals make their comparison complex, requiring a systemized view of the landscape. In this paper, we systematically categorize and analyze both attacks and defenses within the framework of confidentiality, integrity, and availability, focusing on specific threats that pose substantial risks to SCSs. Furthermore, we evaluate existing countermeasures against potential threats in SCS environments and offer insights into the security policies of different nations, recognizing the strategic importance of satellite communications as a national asset. Finally, we present prospective security challenges and solutions for future SCSs, including full quantum communication, AI-integrated SCSs, and standardized protocols for the next generation of terrestrial–space communication. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are crucial in various fields including Health Care Monitoring, Battlefield Surveillance, and Smart Agriculture. However, WSNs are susceptible to malicious attacks due to the massive quantity of sensors within them. Hence, there is a demand for a trust evaluation framework within WSNs to function as a secure system, to identify and isolate malicious or faulty sensor nodes. This information can be leveraged by neighboring nodes, to prevent collaboration in tasks like data aggregation and forwarding. While numerous trust frameworks have been suggested in the literature to assess trust scores and examine the reliability of sensors through direct and indirect communications, implementing these trust evaluation criteria is challenging due to the intricate nature of the trust evaluation process and the limited availability of datasets. This research conducts a novel comparative analysis of three trust management models: "Lightweight Trust Management based on Bayesian and Entropy (LTMBE)", "Beta-based Trust and Reputation Evaluation System (BTRES)", and "Lightweight and Dependable Trust System (LDTS)". To assess the practicality of these trust management models, we compare and examine their performance in multiple scenarios. Additionally, we assess and compare how well the trust management approaches perform in response to two significant cyber-attacks. Based on the experimental comparative analysis, it can be inferred that the LTMBE model is optimal for WSN applications emphasizing high energy efficiency, while the BTRES model is most suitable for WSN applications prioritizing critical security measures. The conducted empirical comparative analysis can act as a benchmark for upcoming research on trust evaluation frameworks for WSNs. [ABSTRACT FROM AUTHOR]