Search

Data owners can store extensive data in a flexible and cost-efficient manner by utilizing cloud data storage. Thus, the number of online cloud service providers and their users have undergone a substantial increase. A significant concern in the field of cloud computing is the security and confidentiality of data. A cryptographic access control technique can be used to guarantee data privacy, which prevents unauthorized access and allows only authorized users to access the data. Nevertheless, the trust issue is not fully addressed in this kind of cryptographic approach. The study primarily aims to identify a solution that effectively addresses the trust issues within access control methods, thereby reducing the security risks associated with cloud storage systems. This, in turn, enhances the quality of decisions made by data owners as well as cloud operators. In this paper, we proposed integrating task-role-based access control (T-RBAC) as a new cloud access control model with a comprehensive trust model using various criteria to offer high security for cloud storage systems, with taking flexibility into consideration. There are trust-based systems integrated with various existing access control models in cloud computing; however, these models do not provide accurate trust value computations, and may still be at risk of certain security threats. Hence, these threats can be eliminated through an accurate trust-based T-RBAC model.

The most widely used asymmetric cipher is ECC. It can be applied to IoT applications to offer various security services. However, a wide range of sectors have been investigated for applying ECC. The field of elliptic curve cryptographic processors for GF (2191) has received less attention. This study presents a low-resource, high-efficiency architecture for a 191-bit ECC processor. This design uses a novel hybrid Karatsuba multiplier for the multiplication of finite fields. For GF (2191), the Quad-Itoh-Tsuji algorithm has been altered to provide a small-size inversion unit. PlanAhead software synthesizes the CPU, which is then implemented on several Xilinx FPGAs. With savings in slice consumption ranging from 16 to 43 percent, the implemented design is the most restricted compared to the current designs. Compared to previously published designs, it is 3.8–1000 times faster. The elliptic curve scalar multiplication on the Virtex-7 FPGA is computed in $7.24~\mu $ s. Additionally, the proposed design achieves savings in area-time products of 77 to 90 percent. It may be beneficial for IoT edge devices. It utilizes 3120 mW of power for the operation. A state-of-the-art comparison based on the figure of merit (FoM) reveals that the proposed design outclasses the newest designs by a large margin. It also exhibits a throughput of 138.121 Kbps.

With the continuous development of Artificial Intelligence (AI), AI services are becoming increasingly influential in society, affecting both individual lives and enterprise production. However, the field of AI model training grapples with a conflict between the desire to maximize the utilization of private and external data, and the necessity to limit collaborative data sharing for privacy protection. Strict regulations on sensitive data give rise to data silos, impeding the smooth flow of information as well. In response to these challenges, Federated Learning (FL) emerges as a promising solution, enabling collaborative machine learning model training across isolated data silos. Despite its potential, securing FL systems is still challenging, primarily due to the absence of a canonical reference model that hierarchically summarizes existing works in this field. This lack complicates users’ understanding of federated learning in the context of data flow and impedes their ability to pinpoint specific security issues and corresponding solutions when utilizing an FL system. To address this gap, we propose a layered reference model for federated learning through a comprehensive survey. The model encompasses five layers: the data interaction layer, client management layer, local model layer, network transmission layer, and remote management layer. Prioritizing the promotion of system security awareness, we conduct a threat analysis for each layer and explore corresponding defense strategies drawn from existing techniques. As a result, readers can gain insights into the federated learning system from the perspective of data flow, comprehend the security risks their private and sensitive data might encounter at each step, and explore relevant solutions to safeguard their information.

Healthcare has seen extensive use of internet technology for the transmission of electronic healthcare records among healthcare professionals and patients for diagnosis and treatment. Now with the advent of digital and internet technology, multimedia data also be transmitted in the form of images, and videos which include X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and so forth. It is direly important to secure these medical images which are transmitted digitally among healthcare professionals and patients. Visual Cryptography (VC) comes as a solution for encrypting and securing these multimedia data for transmission. The process of VC involves dividing the original Secret Color Image into Shares and distributing them to the intended recipients. These shares are then combined, either physically or digitally, at the receiving end to reveal the original multimedia content. VC faces three main challenges which are the quality of reconstructed images, pixel expansion, and computational complexity. So, we in research developed a novel approach called Color Secret Sharing Protocol (CSSP) for the secure transmission of individual secret images within multimedia systems. The newly proposed protocol which is CSSP is been validated on sample medical images using MATLAB where the reconstructed image quality is improved by up to 1.345% compared to other existing methods. CSSP enhances multimedia security by utilizing cover images and simultaneously reducing complexity, effectively addressing pixel expansion within the context of multimedia transmission.

Nowadays, powerful image editing applications not only simplify image processing significantly but also enhance the realism of processed digital images. However, this convenience has presented unprecedented challenges in verifying the authenticity of images. Although existing methods have achieved significant results in image forgery localization, most of them struggle to obtain satisfactory performance when dealing with tampered areas of various sizes, especially for large-scale tampered regions. To enhance the localization performance for various types and sizes of tampered regions, we propose a novel dual-stream intermediate fusion network for image forgery localization, named DIF-Net. This network adopts an encoder-decoder architecture composed of an adaptive convolutional pyramid and dual-stream intermediate fusion modules. Specifically, the former extracts multi-scale information from different depths by utilizing two depth-wise strip convolutions instead of standard large-kernel convolutions. Moreover, during feature fusion, learnable parameters are employed to dynamically allocate weights to each feature scale, so that the network can adaptively select the most relevant features at the target scale. The latter effectively reduces category information differences between the two feature streams by utilizing two learnable intermediate representations to model channel and spatial consistency in the dual-stream features. Compared to traditional and previous deep learning methods, the DIF-Net can generate high-quality prediction masks with fewer parameters. Through extensive experimental validation, our DIF-Net demonstrates outstanding performance on various datasets, surpassing the state-of-the-art forgery localization methods currently available. On the commonly used CASIA2 dataset, our DIF-Net achieves an improvement of 3.3% in F1 and 2.4% in AUC compared to previous methods.

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries $({\mathcal {A}}_{d})$ can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system.

This paper describes a simulation-based approach for automated risk assessment of complex cyber-physical systems to support implementers of ISO 27005. The approach is based on systematic cause-and-effect modelling of threats, their causes and effects, and the ways in which the effects of one threat can lead to other threats. In this way, the approach deals with inter-dependencies within the target system, automatically finding attack paths and secondary effect cascades, which generally are very complex and the source of many challenges when implementing ISO 27005. The approach uses a knowledgebase describing classes of system assets and their possible relationships, along with the associated threats, causes and effects in a generic context. A target system can then be modelled in terms of related assets, describing the intended system structure and purpose (in the absence of any deviations). The knowledgebase is then used to identify which threats are relevant and create a cause-and-effect simulation of those threats. This allows threat likelihoods and risk levels to be found based on input concerning trust assumptions and the presence of controls in the system. The approach has been implemented by the open source Spyderisk project and validated by modelling a published case study of an attack on a steel mill. Given reasonable assumptions about security controls in place, the shortest, highest likelihood attack path found coincides with the published analysis. The case study demonstrates the strengths of the approach: transparency, reproducibility, and performance.

Both criminals and state actors are using the cyberspace to pursue their interests, including obtaining information, sabotaging networks, and disseminating disinformation. Advanced Persistent Threats (APTs) are state and non-state threat actors with high levels of expertise, target knowledge, and available financial and material resources. To effectively counter APT campaigns, it is necessary to have a deep understanding of the methods used by threat actors. Cyber Ranges provide a realistic training environment to develop and train the skills needed to respond to future attacks. However, this requires the ability to simulate APT attacks in a Cyber Range in an automated manner. This article presents an approach to implementing APT scenarios in fully virtualized Cyber Ranges. To achieve this, we extended a theoretical model to enable the formalized representation of APT attacks. Based on this model, we developed a concept for the technical implementation resulting in a framework for an automated simulation of APT attacks in Cyber Ranges. We evaluated both by formalizing two different real-world APT scenarios and implementing an abstract one.

The availability of log data recorded by computer-based systems such as operating system and network logs, makes it possible for the stakeholder to look after the system for monitoring, evaluation, and improvement purposes. If an incident happens to the system, the log is the first and most important artefact to recover so that investigations may be performed to gather an understanding of why such incidents may have occurred. Log-based anomaly detection is one of the common approaches to uncovering incident scenarios and finding the root cause of such incidents. In the context of drone flight, incidents reported in logs include errors during take-off, flight range issues, and cancellations of actions. Existing studies employ sequence anomaly detection to check whether an event during a drone flight is anomalous. It needs several preceding events and includes deciding if the following event is legitimate or malicious. However, one single log record can have no relationship to other log events and be malicious at the same time. Thus, several studies explored point anomaly detection, where one log record is the only feature needed. Dividing the anomalies into two categories can be overwhelming as the number of logs generated by a system is large. At the same time, it can be helpful to separate critical anomalies from the less severe ones. Therefore, this study proposes DroLoVe, a severity-oriented multiclass anomaly detection approach for drone flight log data. In accordance with the dataset characteristics, where the samples from different severity levels share common features, this paper employs a multitask-based label vector representation to train deep neural network models. After an extensive experiment on several baselines, the proposed scenario outperforms other models from existing studies with promising results. The proposed representation of the label improves the prediction confidence score on various encoder types with 8.6% and 1.8% from focal and cross-entropy scenarios on average, respectively.

This paper explores the potential of the hybrid classical deep learning quantum in addressing contemporary information security challenges, including cyber-attack detection and prevention, botnet detection, IP-theft, hardware defect detection, and more, with a specific emphasis on steganalysis. We begin by offering a succinct overview of current information security research trends, focusing on the hybrid classical deep learning-quantum paradigm. Subsequently, we introduce a classical-quantum hybrid framework tailored for uncovering concealed information within a significant public steganalysis dataset, ALASKA2. To enable image extraction and classification, we present a two-layer architecture that integrates convolutional neural network layers into both the classical deep learning and quantum layers. Notably, the proposed approach has improved steganalysis accuracy by achieving a 97% implementation accuracy rate.

Applying blockchain to copyright protection is currently a popular research trend. However, the characteristics of blockchain open data also lead to the threat of copyright privacy leakage. Targeting at this issue, we design a secure digital copyright protection scheme based on blockchain and ring signature algorithm, which can achieve privacy protection of copyright information and improve the efficiency of data authentication. In this paper, we design a new ring signature scheme based on lattice. In this scheme, by using the lattice basis delegation algorithm, user’s public-private key pair is generated without expanding the dimension of lattice. Subsequently, according to the rejection sampling, message is signed by signer’s secret keys and other ring participants’ public keys. It can reduce scheme’s computational complexity. Finally, by combining ring signatures with blockchain technology, a new copyright protection scheme is proposed. More importantly, this scheme is proven to be secure with its correctness and anonymity. Meanwhile, it has less communication costs and shorter key sizes than those in other similar schemes. The results show that the proposed new scheme has good performance and efficiency.

With the advancements in computer networks and systems, the number of security vulnerabilities and cyber attacks targeting/using these vulnerabilities continues to increase. Consequently, various intrusion detection systems (IDS) have been developed to detect cyber attacks and ensure information security. IDSs are categorized into two classes based on the data sources: Network-based intrusion detection system (NIDS) and host-based intrusion detection system (HIDS). In this systematic literature review (SLR), studies are examined that focus on HIDS or propose methods applicable to HIDS, as well as those related to IDSs that can be converted into HIDSs. The studies published between 2020 and 2023 are collected from widely used academic databases through various query statements. Filtering based on specific selection and elimination criteria is undergone by the collected studies, resulting in 21 studies for examination. Subsequently, these studies and their advantages and disadvantages are discussed. In addition, while examining the studies, five research questions are addressed. Finally, the defects, potential areas for improvement, and future research directions related to HIDSs are discussed.

Quantum computing is emerging as a significant threat to information protected by widely used cryptographic systems. Cryptographic methods, once deemed secure for decades, are now at risk of being compromised, posing a massive threat to the security of sensitive data and communications across enterprises worldwide. As a result, there is an urgent need to migrate to quantum-resistant cryptographic systems. This is no simple task. Migrating to a quantum-safe state is a complex process, and many organisations lack the in-house expertise to navigate this transition without guidance. In this paper, we present a comprehensive framework designed to assist enterprises with this migration. Our framework outlines essential steps involved in the cryptographic migration process, and leverages existing organisational inventories. The framework facilitates the efficient identification of cryptographic assets and can be integrated with other enterprise frameworks smoothly. To underscore its practicality and effectiveness, we have incorporated case studies that utilise graph-theoretic techniques to pinpoint and assess cryptographic dependencies. This is useful in prioritising crypto-systems for replacement.

Malicious assaults and information leakage have grown in importance in practically every area of information and communication technology (ICT) in recent years. Enterprise ledger organization, preservation, security, and protection are all greatly aided by information security (IS). To maintain their positions in the market, industries must safeguard their data and other vital assets. The main goals of this paper’s systematic review, which covers the entire process of privacy protection and security, are to introduce cryptographic IS for Industry 5.0 (the Fifth Industrial Revolution) as a comprehensive solution that provides regulatory compliance and IS policies in addition to the industrial goals. The review covers the four main areas: 1) recognizing the need for frameworks to be developed for a secure Industry 5.0 lifecycle; 2) emphasizing security guidelines, procedures, and countermeasures for Industry 5.0; 3) applying control, access, and availability in real-time; and 4) proposing a futuristic architecture for industrial information security. Furthermore, the analysis of previously published state-of-the-art techniques is presented in this paper. These techniques survive with various limitations and challenges, including intercommunication exchange, fine-grained control access, and interconnectivity-related issues, which affect the adoption of an IS in industrial programs. At the end of this systematic review, we investigated a few open research problems and mentioned those that are involved in the design of cryptographic re-encryption enabled IS for future Industry 5.0 developments.

In a rapidly advancing world of technology, information security studies have become the backbone of the digital age, and steps in this area are critical. In this context, cryptography, in particular, plays a key role in ensuring the confidentiality, integrity and authentication of data. s-box structures provide a certain diversity and security layer in encryption algorithms, forming one of the key elements in this area. This study focuses on the design and analysis of s-box structures, examining the potential impact of chaos theory-based structures on encryption systems. First, it provides a comprehensive classification of existing s-box design proposals in the literature, and explores the contribution of chaos theory to the security features of these structures. The original contribution of the study is the results obtained with the help of the developed analysis and design program. The program optimizes levels of complexity, randomness, and resistance, and demonstrates the resistance of these new structures to cryptanalysis attacks. The paper also draws attention to open issues in the field of chaos-based s-box design and provides a road map for future research. It is estimated that all these findings will provide a common motivation for researchers in the relevant literature and constitute the basis for many practical practices.

Identifying the number of transmit antennas and the channel order is an important step in recovering the original information for a non-cooperative communication system. Traditional methods overestimate the number of transmit antennas and the channel order due to the coupling effect. This paper proposes a novel joint identification scheme to determine the number of transmit antennas and the channel order, which consists of two stages. Specifically, in the first stage, an improved weighted Gerschgorin disk identifier is developed to obtain the number of transmit antennas using frequency domain signals. In the second stage, we first derive an equivalent time-domain model to improve the utilization of the receiver antenna, and then a novel test statistic following $F$ distribution is constructed to perform the sequential binary hypothesis testing. Finally, the channel order is obtained using the estimated number of transmit antennas in the first stage. The simulation results show that the proposed joint identification scheme significantly outperforms the existing approaches.

Progress in Deep Learning (DL) has introduced alternative methods for tackling complex challenges, such as the steganalysis of spatial domain images, where Convolutional Neural Networks (CNNs) are employed. In recent years, various CNN architectures have emerged, enhancing the precision of detecting steganographic images. Nevertheless, current CNNs encounter challenges related to the inadequate quality and quantity of available datasets, high imperceptibility of low payload capacities, and suboptimal feature learning processes. This paper proposes an enhanced secret data detection approach with a CNN architecture that includes convolutional, depth-wise, separable, pooling, and spatial dropout layers. An improved fuzzy Prewitt approach is employed for pre-processing the images prior to being fed into CNN to address the issues of low payload capacity detection and dataset quality and quantity in learnability of the image features. Experimental results, which achieved an overall accuracy and F1-score of 99.6 and 99.3 per cent, respectively, to detect a steganographic payload of 0.5 bpp hidden with Wavelet Obtained Weights (WOW), show a significant outperformance over the state-of-the-art methods.

Secure exchange of sensitive information between different entities is a serious challenge in today’s environment. Therefore, various cryptosystems have been created specifically for this purpose. Various operations are used in these cryptosystems. The most common operations used are substitution and permutation. The substitution-box (S-box), a look-up table that takes x bits as input and replaces them with y bits, is responsible for the substitution operation. The construction of an S-box with strong cryptographic attributes is an essential part of building a robust and secure cryptosystem.Numerous researchers have dedicated their efforts to devising intricate techniques for constructing S-boxes. In this study, a novel approach is proposed to design a robust and dynamic S-box based on a compound-chaotic map. The suggested method for constructing the S-box is characterized by its simplicity and resilience. Notably, the Tent-Sine chaotic system significantly expands the chaotic range, setting it apart from other chaotic systems and rendering it more suitable for cryptographic applications. This research introduces a promising avenue for enhancing the security and effectiveness of S-boxes, contributing to the advancement of cryptographic systems. A number of widely accepted metrics, such as Non-Linearity (NL), Strict Avalanche Criterion (SAC), Bit Independence Criterion (BIC), Linear Probability (LP), and Differential Probability (DP), are extensively exercised for the analysis and evaluation of the proposed S-Box’s cryptographic strength. The results of the proposed of S-box are compared with the S-boxes of other researchers, and it is observed that it is cryptographically sound and worthy of inclusion in modern cryptosystems.

The Advanced Encryption Standard (AES) is used for achieving quantum-resistant cryptography when a 256-bit key is applied. This paper presents a high-throughput, seven-stage hardware pipeline architecture for SubByte computations in the AES for information security applications. Composite field arithmetic–based calculations are employed for logic optimization. The proposed architecture includes dedicated multistage multiplication processes based on Galois field polynomials for constants, squaring, and variables; thus, the critical path of SubByte computations is shortened, and the maximum operating frequency is enhanced. The proposed architecture was synthesized using a TSMC 40-nm cell library, and the throughput of the proposed architecture (34.78 Gbps) was observed to be superior to that of an existing state-of-the-art architecture by 43.47%. Moreover, our architecture was noted to consume lower dynamic power for combinational logic circuits, indicating that the proposed architecture has greater computational logic optimization than existing designs. The proposed architecture is feasible for communication security applications in the Internet of Things systems because of its high throughput and area efficiency.

Advancements in Internet of Things devices allow for the collection and analysis of big data. Moreover, cloud computing has emerged as an ideal platform for big data analysis because it quickly provides computing resources for analysis at scale. However, concerns exist regarding the privacy and security of this information. Secure computation is a technology that enables computation while maintaining data encryption. In this study, we present a new secure computation based on a client-server model, in which a set of servers perform computations using the inputs of multiple clients. We adopt the $(k, n)$ threshold secret sharing approach, where an input $s$ is divided into $n$ shares and can be recovered from shares with a threshold $k$ . However, conventional secure computation using $(k,n)$ threshold secret sharing generally requires the condition $n \geq 2k-1$ and communication among multiple servers for each multiplication. To the best of our knowledge, no previous study has solved this problem completely. We demonstrate that processes that require communication can be concentrated in the preprocessing phase and propose a secure computation using $(k,n)$ threshold secret sharing that does not require communication during the secure computation phase, even when multiplication is performed. Moreover, we show that the number of communications does not depend on the number of multiplications, unlike in conventional methods. As communication often requires more processing time than the actual secure computation, our method makes it possible to realize faster overall processing than conventional methods. We also present an extensive security analysis and experimental simulation of the proposed method. Our proposed method achieves information-theoretic security against semi-honest adversaries under certain conditions with $n < 2k-1$ .

The growth of data generation capabilities, facilitated by advancements in communication and computation technologies, as well as the rise of the Internet of Things (IoT), results in vast amounts of data that significantly enhance the performance of machine learning models. However, collecting all necessary data to train accurate models is often unfeasible due to privacy laws. Federated Learning (FL) evolved as a collaborative machine learning approach for training models without sharing private data. Unfortunately, several in-design vulnerabilities have been exposed, allowing attackers to infer private data of participants and negatively impacting the performance of the federated model. In light of these challenges and to encourage the development of FL solutions, this paper provides a comprehensive analysis of secure FL proposals that both protect user privacy and enhance the performance of the model. We performed a systematic review using predefined criteria to screen and extract data from multiple electronic databases, resulting in a final set of studies for analysis. Through the systematic review methodology, the paper groups the security vulnerabilities of FL into model performance and data privacy attacks. It also presents an analysis and comparison of potential mitigation strategies against these attacks. Additionally, the paper conducts a security analysis of state-of-the-art FL applications and proposals based on the vulnerabilities addressed. Finally, the paper outlines the main applications of secure FL and lists future research challenges. The survey highlights the crucial role of security strategies in ensuring the protection of user privacy and model performance in the context of future FL applications.

The issue of information security in photonics environment has attracted more and more attention, especially when the secure communication of optical digital images has become a research hotspot. In this paper, we propose a hybrid encryption scheme for color images in the frequency and spatial domains based on double random transform and memristor hyperchaotic system. Firstly, we decompose the color image into RGB channels and then perform fast fourier transform (FFT) to transform the digital image from the spatial domain to the frequency domain. Secondly, two-phase masks are generated using the memristor hyperchaotic system, and the Fresnel diffraction optical transformation method is performed twice for encryption. Thirdly, the transformation from the frequency domain to the spatial domain is completed using inverse fast fourier transform (IFFT). Finally, the image is permuted and diffused using the chaotic sequences to obtain the final cipher image. Double random phase encryption expands the key space, while the combination of spatial and frequency domains improves the resistance to attacks. Based on cryptanalysis theory, we introduce a dynamic key associated with plain image, which can effectively resist plain attack. The experimental results show that the optical digital image encryption scheme has a large key space, excellent comprehensive performance, and can resist common attacks. In addition, we verify the hardware feasibility and ease of implementation of the proposed algorithm in an embedded optical communication network experimental platform. Therefore, our proposed scheme in this paper is a preferred optical digital image secure communication technology scheme with good application prospects.

This paper uses blockchain technology to conduct in-depth research and analysis on enterprise cost optimization control. Based on the analysis of the cost control status in enterprises, the concept of target cost optimization control and specific control ideas are proposed. And the study of optimization control under target cost based on genetic algorithm is carried out in combination with the current three major controls of quality, schedule, and cost. It provides the technical basis for the realization of the target profit of the enterprise. The Optimized Scalable Byzantine Fault Tolerance (OSBFT) algorithm, which is suitable for spectrum sharing, is proposed based on PBFT (Practical Byzantine Fault Tolerance) algorithm. So, in this paper, an improved consensus algorithm OSBFT (Optimized Scalable Byzantine Fault Tolerance) is proposed based on it. The improved genetic algorithm is used to solve the objective function and verify the validity, reasonableness and applicability of the model and algorithm. It is shown that the introduction of delay cost in the multilevel inventory model reduces the total cost of the optimized model by 16.87% compared to previous studies. The algorithm reduces the consensus steps, incorporates a data synchronization mechanism, and enables nodes to join and exit consensus.

As organizations increasingly view information as one of their most valuable assets, which supports the creation and distribution of their products and services, information security will be an integral part of the design and operation of organizational business processes. Yet, risks associated with cyber-attacks are on the rise. Organizations that are subjected to attacks can suffer significant reputational damage as well as loss of information and knowledge. As a consequence, effective leadership is cited as a critical factor for ensuring corporate level attention for information security. However, there is a lack of empirical understanding as to the roles strategic leaders play in shaping and supporting the cyber-security strategy. This article seeks to address this gap in the literature by focusing on how senior leaders support the cyber-security strategy. The authors conducted a series of exploratory interviews with leaders in the positions of Chief Information Officer, Chief Security Information Officer, and Chief Technology Officer. The findings revealed that leaders are engaged in both transitional, where the focus is on improving governance and integration and transformational support, which involves fostering a new cultural mindset for cyber-resiliency and the development of an ecosystem approach to security thinking. [ABSTRACT FROM AUTHOR]

With the rapid development of the Industrial Internet of Things, the social and commercial value of digital information is greatly increased, and the sensitive information of groups and individuals faces more and more problems. Due to providing accurate prediction errors, pixel value ordering (PVO)-based reversible data hiding is an active research topic. In this article, a new PVO-based embedding method is proposed first, in which the adaptive data embedding is adopted according to the difference between the maximum/minimum three pixels. Second, a pixel collecting and sorting model is designed to increase expandable prediction errors. By converting a shifting prediction error into an expandable one, the proposed PVO-based embedding method creates conditions for data embedding. In this way, the space location of pixels in the block can be better utilized to achieve improved capacity-distortion performance. Finally, an adaptive embedding is designed based on the content of the target block and its neighboring blocks. By measuring the complexity of the target pixel block, smoothing blocks are preferentially used and the appropriate embedding is determined by the complexity. The experimental results show that the proposed method outperforms a series of PVO-based multipass embedding methods. The proposed method is expected to address some security issues in the industrial and privacy fields, such as enhancing confidentiality and security during the transmission of sensitive information. [ABSTRACT FROM AUTHOR]

Information is inevitable when it comes to national security. The information revolution seems to hold the massive potential to strengthen national security against current and upcoming threats and cyber-attacks. However, advancements in information accessibility possess innumerable complications for retaining stable national security. One of the preeminent information sources is social media which certainly raises information manipulation factors and destabilizes national security. To accomplish better national security plans, information technology can help countries to identify potential threats, share information securely, and protect mechanisms in them. Artificial Intelligence (AI) is one of the smart areas that robustly facilitates secure information handling to avoid threats and cyber-attacks. It intelligently scrutinizes information available to the public through social media and assists in refraining negative effects on national security. This research article widely focuses on four main analytical milestones; 1) Information available to the public 2) Information affecting national security 3) Risks of cyber-attacks 4) AI as paramount to national security for accomplishing competent information role. Our principal objective is to demystify information accessibilities perspectives for readers to understand the fundamentals of information accessibility and inaccessibility corresponding to national security. To support and manifest our milestones and objectives, Systematic Literature Review (SLR) is methodologically adapted to draw suitable conclusions and develop a farsighted model and frame of reference. This paper concludes with AI tool based categorization, algorithmic function and domain-specific analysis with area-based limitations to highlight current needs. Above all, this article is a thought-provoking kick-start for many naive social media users that usually avoid information-bearing elements and are victimized by cyber-attacks followed by national security compromises.

The rapid growth of online communication has increased the demand for secure communication. Most government entities, healthcare providers, the legal sector, financial and banking, and other industries are vulnerable to information security issues. Text steganography is one way to protect secure communication by hiding secret messages in the cover text. Hiding a large amount of secret information without raising the attacker’s suspicion is the main challenge in steganography. This paper proposes the Color and Spacing Normalization stego (CSNTSteg) model to resolve the low capacity and invisibility problems in text steganography. CSNTSteg consists of two stages: the pre-embedding stage, which achieves high capacity by utilizing RGB coding and character spacing. It is designed to increase the number of bits per location and usable characters. Besides, it applies the Huffman coding technique to compress the secret message to add more capacity enhancement. The second stage is color and space normalization, which accomplishes high invisibility by normalizing the RGB coding and character spacing of the cover and stego text. CSNTSteg overcomes the color difference issue between the cover and stego texts regardless of the color of the cover text. To assess the quality of CSNTSteg, the experimental results are compared with existing works. CSNTSteg shows superior capacity over the existing studies with a percentage of 98.85%. CSNTSteg also achieves high invisibility by reducing the color difference with a percentage of 4.7% and 5.07% for black and colored cover text, respectively. Furthermore, CSNTSteg improves robustness by 94.22% by reducing the distortion in stego text. Overall, the CSNTSteg model embeds a high capacity of secret data while maintaining invisibility and security, offering a new perspective on text steganography to protect against visual and statistical attack issues.

The ever-increasing acceptance of cryptocurrencies has fueled applications beyond investment purposes. Crypto-payment is one such application that can bring radical changes to financial transactions in many industries, particularly e-commerce and online retail. However, characteristics of the technology such as transaction disintermediation, lack of central authority, and lack of adequate regulations may introduce new privacy and security concerns among the users. This coincides with another trend of rising individuals’ concerns pertaining to information privacy and security issues in online transactions. The current paper investigates how consumer trust in crypto-payment, a key determinant of consumer intentions and relational exchanges over the long-term, is formed based on their perceptions towards privacy and security aspects of the technology. Using data from 327 survey participants, the study found that perceived information privacy risk, perceived anonymity, and perceived traceability of transactions are significant determinants of consumer trust in crypto-payment; but their perceptions of information security fraud risk have no significant effect. It also provided support for the hypothesis that perceived trust contributes to consumers’ intention to adopt crypto-payment. The findings highlight the need to enhance consumer understanding and awareness of information privacy and potential security issues in crypto-payment as well as what needs to be done to address consumer concerns in this regard. The paper creates novel insights into the requirements of trust in crypto-payment services and the consequences of consumers’ perceptions of privacy and security in this domain.

High capacity and high imperceptibility are the primary targets for ideal image steganography. For the traditional transform-based schemes, the main challenge is to balance the imperceptibility, hiding capacity, and running efficiency. To obtain a high-quality image in dense embedding, existing high-capacity schemes usually sacrifice running efficiency and security. These disadvantages make the schemes less appealing. In this paper, based on a lightweight transform Discrete Hadamard Transform (DHT), we introduce a simple but high-performance image steganographic model. In the case of only stego-image passed, the experiment results demonstrate that the proposed scheme achieves high imperceptibility and security even in the dense embedding of 8 BPP (Bits Per Pixel). Furthermore, the proposed scheme withstands various tests and shows desirable robustness. The comparative analyses are demonstrated that our scheme is efficient and feasible image steganography.

In the current digital era, personal data storage on public platforms is a major cause of concern with severe security and privacy ramifications. This is true especially in e-health data management since patient’s health data must be managed following a slew of established standards. The Cloud Service Providers (CSPs) primarily provide computing and storage resources. However, data security in the cloud is still a major concern. In several instances, Blockchain technology rescues the CSPs by providing the robust security to the underlying data by encrypting data using the unique and secret keys. Each network user in Blockchain has its own unique and secret keys linked directly to the transaction keys as a digital signature to protect the data. However, Blockchain technology suffers from the latency and throughput issues in high workload scenarios. To overcome e-healthcare records privacy issues in a third-party cloud, we designed a Patient’s E-Healthcare Records Management System (PRMS) that focuses on latency and throughput. A comprehensive performance analysis of PRMS is carried out on different third-party clouds to validate its applicability. Moreover, the proposed PRMS system is compared with Blockchain platforms such as Hyperledger Fabric v0.6 and Etherium 1.5.8 against latency and throughput by adjusting the workload for each platform up to 10,000 transactions per second. The proposed PRMS is compared to the Secure and Robust Healthcare-Based Blockchain (SRHB) approach using Yahoo Cloud Serving Benchmark (YCSB) and small bank datasets. The experimental results indicate that deploying PRMS on Amazon Web Services decreases System Execution Time (SET) and the Average Delay (AD) time by 2.4%, 8.33%, and 25.15%, 15.26%, respectively. Additionally, deploying PRMS on the Google Cloud Platform decreases System Execution Time (SET) and Average Delay (AD) by 2.27%, 2.4%, and 2.72%, 4.73% AD, respectively. The experimental results confirm the superiority of the PRMS under the high workload scenario over SRHB and its applicability in cloud data centers.

Currently, malware is increasing in both number and complexity dramatically. Several techniques and methodologies have been proposed to detect and neutralize malicious software. However, traditional methods based on the signatures or behaviors of malware often require considerable computational time and resources for feature engineering. Recent studies have applied machine learning to the problems of identifying and classifying malware families. Combining many state-of-the-art techniques has become popular but choosing the appropriate combination with high efficiency is still a problem. The classification performance has been significantly improved using complex neural network architectures. However, the more complex the network, the more resources it requires. This paper proposes a novel lightweight architecture by combining small Convolutional Neural Networks and advanced Variational Autoencoder, enhanced by channel and spatial attention mechanisms. We achieve overperformance and sufficient time through various experiments compared to other cutting-edge techniques using unbalanced and balanced Malimg datasets.

Forged videos are commonly spread online. Most have malicious content and cause serious information security problems. The most critical issue in deepfake detection is the identification of traces of tampering in fake videos. This study designs a Dual Attention Forgery Detection Network (DAFDN), which embeds a spatial reduction attention block (SRAB) and a forgery feature attention module (FFAM) to the backbone network. DAFDN embeds the two proposed attention mechanisms and enables the convolution neural network to extract peculiar traces left by images’ warping. This study uses two benchmark datasets, DFDC and FaceForensics++, to compare the performance of the proposed DAFDN with other methods. The results show that the proposed DAFDN mechanism achieves AUC scores of 0.911 and 0.945 in the datasets DFDC and FaceForensics++, respectively. These results are better than those of previously developed methods, such as XceptionNet and EfficientNet-related methods.

Due to advancements in technology, digital images are widely used in various applications like medical field, military communication, remote sensing, etc. These images may contain sensitive and confidential information. Therefore, images are required to be protected from unauthorized access. Many image protection techniques have been proposed in past years. The most common technique to protect the images is encryption. In this technique, a secret key and an encryption algorithm are used to change the plain image into an encrypted image. The encrypted image looks like a noisy image and can easily attract the attacker’s attention. If an image gets captured and stacked, sensitive information can be revealed. In this regard, Visually Meaningful Encrypted Image (VMEI) technique is developed, which initially encrypts the original image and then hides it into a reference image. The final encrypted image looks like a normal image. Hence, the VMEI technique provides more security as compared to simple image encryption techniques. Therefore, a systematic survey of existing VMEI techniques is presented in this paper. The VMEI techniques are divided into different categories based on their characteristics. Moreover, this paper elaborates and investigates the improvements and analyses performed on VMEI techniques based on various evaluation parameters. These evaluation parameters are divided into different categories such as security attacks, encryption key attacks, quality analysis, and noise attacks. Finally, this paper discusses the potential applications and future challenges of VMEI techniques.

Security event correlation approaches are necessary to detect and predict incremental threats such as multi-step or targeted attacks (advanced persistent threats) and other causal sequences of abnormal events. The use of security event correlation techniques also makes it possible to reduce the volume of the original data stream by grouping the events and eliminating their redundancy. The variety of event correlation methods, in turn, requires choosing the most appropriate way to handle security events, depending on the purpose and available resources. This paper presents a systematization of security event correlation methods into several categories, such as publication year, applied correlation methods, knowledge extraction methods, used data sources, architectural solutions, and quality evaluation of correlation methods. The research method is a systematic literature review, which includes the formulation of research questions, the choice of keywords and criteria for inclusion and exclusion. The review corpus is formed by using search queries in Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, and selection criteria. The final review corpus includes 127 publications from the existing literature for 2010–2021 and reflects the current state of research in the security event correlation field. The results of the analysis include the main directions of research in the field of event correlation and methods used for correlation both single events and their sequences in attack scenarios. The review also describes the datasets and metrics used to evaluate security event correlation approaches. In conclusion, the existing problems and possible ways to overcome them are identified. The main contribution of the review is the most complete classification and comparison of existing approaches to the security event correlation, considered not only from the point of view of the algorithm, but also the possibility of unknown attack detection, architectural solutions and the use of event initial data.

The tremendous development of the Internet of Things (IoT) technology in the last decades has fostered advancement in automatic medical assistive devices to support the existing Electronic Health Record (EHR) system. As an integral part of the EHR electronic model, public cloud servers store medical data. Unfortunately, public cloud servers are prone to security and privacy breach. This paper introduces a novel non-cryptographic approach to preserve electrocardiograph (ECG) data confidentiality and integrity in the EHR environment. The main objective of the proposed anonymization algorithm is to obscure the patient’s cardiac information during transmission and to protect information stored in the cloud database. Although we focus on ECG data, generalization to other types of clinical data can be derived using the proposed method. Performance evaluation of the proposed scheme showed that the algorithm conceals both fiducial and non-fiducial features of the data. Therefore, confidentiality feature is preserved. This paper examined confidentiality of the anonymized data using the Percentage Residual Difference (PRD) and investigated the integrity of the reconstructed data in terms of cross-correlation. Security analysis carried out using the PRD, brute force attack, and performance comparison between the proposed algorithm and existing methods. Evaluation showed that the proposed scheme offers a secure non-cryptographic model for transmission and storing clinical data in the cloud. Moreover, in terms of processing time, the proposed algorithm is ten times faster than the existing wavelet packet method when processing long ECG data, 65,536 sample points. In a real-time experimental testbed, the implemented proposed system was successful.

To solve the problem of privacy leakage and response latency in outsourced image watermark embedding in cloud computing, an efficient and privacy-preserving watermark embedding method for outsourced digital images was proposed by introducing edge computing technology. We had proposed a perturbing encryption method with homomorphism to ensure the information security and the correctness of discrete wavelet transformation in the encrypted domain. In addition, the framework was designed to guarantee the safety of singular value decomposition that edge server could not recover the original image matrix. The experimental results show that the proposed method is superior to similar secure watermarking schemes in terms of encryption/decryption time and ciphertext expansion. The proposed method enables the watermarking operation to be performed in an unsafe outsourced environment while achieving a watermarking effect similar to the plaintext equivalent.

Tensor core is a newly introduced hardware unit in NVIDIA GPU chips that allows matrix multiplication to be computed much faster than in the integer and floating-point units. In this paper, we show that for the first time, tensor core can be used to accelerate state-of-the-art lattice-based cryptosystems. We employed tensor core to speed up polynomial convolution, which is the most time consuming operation in lattice-based cryptosystems. Towards that aim, several parallel algorithms are proposed to allow the tensor core to handle flexible matrix sizes and ephemeral key pairs. Experimental results show that the polynomial convolution computed using the tensor core is at least $2\times $ faster than the version implemented with conventional integer units of the NVIDIA GPU. The proposed tensor-core-based polynomial convolution technique was applied to NTRU, one of the finalists in NIST post-quantum cryptography (PQC) standardization. It achieved $2.02\times /1.98\times $ (encapsulation) and $1.56\times /1.90\times $ (decapsulation) higher throughput on two parameter sets (ntruhps2048509 and ntruhps2048677), compared to the conventional integer-based implementations on a GPU. In particular, the proposed implementation techniques achieved throughput up to 793651 key encapsulations per second and 505051 decapsulations per second on a RTX2060 GPU. To demonstrate the flexibility of the proposed technique, we extend the implementation to other lattice-based cryptosystems that have a small modulus: LAC and two variant parameter sets in FrodoKEM. Considering that the IoT gateway devices and cloud servers need to handle massive connections from the sensor nodes, the proposed high throughput implementation on GPU is very useful in securing the IoT communication.

Identity-based encryption is a public key-based method of encryption that enables communicating identities to use some individual and unique information, such as their physical IP addresses and MAC addresses, to identify them and as public keys. The scheme does not require the extra device to store long-term public keys. So, it is convenient for use in practical applications, including smart industry and smart manufacturing. This study develops a novel, efficient, and secure identity-based encryption scheme using an extended Chebyshev chaotic map that has recently been demonstrated to outperform traditional cryptography, including modular exponential computations or scalar multiplications on elliptic curves. Besides demonstrating that the proposed scheme satisfies the security requirements of identity-based encryption, the simulation results of this study show that the proposed scheme requires less response time than related identity-based encryption schemes. Due to hardware limitations, not all industrial devices can load heavy computations. Therefore, the proposed identity-based encryption scheme outperforms related identity-based encryption approaches, and is suitable for industrial environment.

The number of ways to obtain information has increased by 5th Generation Mobile Communication Technology commercial. But easier access to information also means more security issues. Digital watermarking is an effective method to solve the security problem of information. Aiming at the problems of one-dimensional single chaotic image encryption algorithm, such as simple structure, small key space, and the invisibility and robustness of traditional information hiding technology cannot be well reconciled. The improved encryption algorithm based on Low Density Parity Check Code and double Logistic chaotic system is proposed, which can expand the key space, reduce the bit error rate, and enhance the security and anti-interference ability of the system. The feasibility, reliability and practicability of the algorithm are verified by MATLAB simulation.

Public-key encryption is extensively used to provide digital data confidentiality and deliver the security features, such as nonrepudiation (digital signature) and secure key exchange. Conventional public-key schemes are based on mathematical problems with inflexible constraints, and the security of digital contents relies on computational complexity. In the era of emerging technologies, most public-key image encryption schemes are susceptible to various threats. We propose a novel public-key encryption in this article with near-ring criteria and provide confidentiality to private data with the microstates of the Einstein crystal model. The virtual oscillator generated by microstates of initial oscillators for the common secrets with the public-key scheme produces unique states to encrypt digital data. The privacy-preserved structure, that mimics the data stream of digital content with the behavior of the improved Einstein crystal model, describes a system in terms of microstates to generate diffusion in the plain data with unique states of a virtual oscillator. The performance and digital forensic evaluations, such as randomness, histogram uniformity, pixels’ correlation, pixels’ similarity, visual strength, pixels’ incongruity, key sensitivity, linear and differential attacks, noise, and occlusion attacks analyses, certify the resistivity of the proposed algorithm against potential threats and provide superior capacity in comparison to existing methodologies to hostile certain attacks.

Towards the massive amount of data generated in our daily work and life, embedded systems, with economical but powerful storage and computing resources, are inevitably becoming the most suitable platform for the Edge Computing for the Internet of Things. However, embedded system servers may also threaten individuals by storing individuals’ private data for years. This paper proposes a Resilient Tag-based Privacy Protection (RTPP) scheme for embedded systems. Specifically, to protect the privacy against the hackers and other non-users, we employ a pseudo-random number encryption technique with the chaos-based principle so that the third party cannot easily steal the private data and reduce the risk of personal privacy leakage. To protect the individuals’ interests, we propose a new approach to controlling the life cycle table of data to enable individuals themselves the flexibility to control the life cycle of private data. Unlike existing data lifetime management methods, the RTPP can support the retrieval of tags in the data life cycle table to control the corresponding privacy while automatically adding or removing tags. Our system automatically adjusted the survival period of private data in the life cycle table through the change of leaf weights, controlled the charge movement on the surface of flash memory, and finally achieved the resilient adjustment process of the life cycle of private data in the embedded system. The security proof and performance evaluation show that the proposed RTPP scheme is provable secure in the automatic privacy lifecycle tuning model for embedded systems and efficient in practice.

All-or-nothing transforms (AONTs) were proposed by Rivest as a message preprocessing technique for encrypting data to protect against brute-force attacks, and have numerous applications in cryptography and information security. Later the unconditionally secure AONTs and their combinatorial characterization were introduced by Stinson. Informally, a combinatorial AONT is an array with the unbiased requirements and its security properties in general depend on the prior probability distribution on the inputs $s$ -tuples. Recently, it was shown by Esfahani and Stinson that a combinatorial AONT has perfect security provided that all the inputs $s$ -tuples are equiprobable, and has weak security provided that all the inputs $s$ -tuples are with non-zero probability. This paper aims to explore on the gap between perfect security and weak security for combinatorial $(t,s,v)$ -AONTs. Concretely, we consider the typical scenario that all the $s$ inputs take values independently (but not necessarily identically) and quantify the amount of information $H(\mathcal {X}|\mathcal {Y})$ about any $t$ inputs $\mathcal {X}$ that is not revealed by any $s-t$ outputs $\mathcal {Y}$. In particular, we establish the general lower and upper bounds on $H(\mathcal {X}|\mathcal {Y})$ for combinatorial AONTs using information-theoretic techniques, and also show that the derived bounds can be attained in certain cases. Furthermore, the discussions are extended for the security properties of combinatorial asymmetric AONTs. [ABSTRACT FROM AUTHOR]

In this work, we study a full-duplex (FD) cloud radio access network (C-RAN) from the aspects of infrastructure sharing and information secrecy, where the central unit utilizes FD remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. Furthermore, the communication takes place in the presence of untrusted external receivers, i.e., eavesdropper nodes. The communicated uplink (UL) and downlink (DL) waveforms are quantized in order to comply with the limited capacity of the fronthaul links. In order to provide information secrecy, we propose a novel utilization of the quantization noise shaping in the DL, such that it is simultaneously used to comply with the limited capacity of the fronthaul links, as well as to degrade decoding capability of the individual eavesdropper and the untrusted RUs for both the UL and DL communications. In this regard, expressions describing the achievable secrecy rates are obtained. An optimization problem for jointly designing the DL and UL quantization and precoding strategies are then formulated, with the purpose of maximizing the overall system weighted sum secrecy rate. Due to the intractability of the formulated problem, an iterative solution is proposed, following the successive inner approximation and semi-definite relaxation frameworks, with convergence to a stationary point. Numerical evaluations indicate a promising gain of the proposed approaches for providing information secrecy against the untrusted infrastructure nodes and/or external eavesdroppers in the context of FD C-RAN communications. [ABSTRACT FROM AUTHOR]

The rising popularity of ICT and the Internet has enabled Unmanned Aerial Vehicle (UAV) to offer advantageous assistance to Vehicular Ad-hoc Network (VANET), realizing a relay node’s role among the disconnected segments in the road. In this scenario, the communication is done between Vehicles to UAVs (V2U), subsequently transforming into a UAV-assisted VANET. UAV-assisted VANET allows users to access real-time data, especially the monitoring data in smart cities using current mobile networks. Nevertheless, due to the open nature of communication infrastructure, the high mobility of vehicles along with the security and privacy constraints are the significant concerns of UAV-assisted VANET. In these scenarios, Deep Learning Algorithms (DLA) could play an effective role in the security, privacy, and routing issues of UAV-assisted VANET. Keeping this in mind, we have devised a DLA-based key-exchange protocol for UAV-assisted VANET. The proposed protocol extends the scalability and uses secure bitwise XOR operations, one-way hash functions, including user’s biometric verification when users and drones are mutually authenticated. The proposed protocol can resist many well-known security attacks and provides formal and informal security under the Random Oracle Model (ROM). The security comparison shows that the proposed protocol outperforms the security performance in terms of running time cost and communication cost and has effective security features compared to other related protocols. [ABSTRACT FROM AUTHOR]

Information security is one of the biggest threats to enterprises; how to identify the determinants of enterprise information security investment (EISI) and put forward corresponding countermeasures is related to the continuity of enterprise operation, which is an urgent problem to be solved at present. This article identifies the index system of factors that affect the EISI by using the Delphi method. The interpretive structural model (ISM) of the influence factors of EISI is constructed. The result shows that the ISM of EISI determinants is a multilevel hierarchical structure with five levels. According to the logical structure of the model, the most direct reasons affecting EISI are the level of enterprise information security technology and enterprise information security behavior; information security laws and regulations and national policies are the deep-seated reasons for EISI. It is of theoretical value and reference to understand the formation, model establishment, and evolution mechanism of EISI decision making. [ABSTRACT FROM AUTHOR]

Increasing usage of digital manufacturing (DM) in safety-critical domains is increasing attention on the cybersecurity of the manufacturing process, as malicious third parties might aim to introduce defects in digital designs. In general, the DM process involves creating a digital object (as CAD files) before using a slicer program to convert the models into printing instructions (e.g., g-code) suitable for the target printer. As the g-code is an intermediate machine format, malicious edits may be difficult to detect, especially when the golden (original) models are not available to the manufacturer. In this work, we aim to quantify this hypothesis through a red team/blue team case study, whereby the red team aims to introduce subtle defects that would impact the properties (strengths) of the 3-D printed parts, and the blue team aims to detect these modifications in the absence of the golden models. The case study had two sets of models, the first with 180 designs (with two compromised using two methods) and the second with 4320 designs (with 60 compromised using six methods). Using statistical modeling and machine learning (ML), the blue team was able to detect all the compromises in the first set of data, and 50 of the compromises in the second. [ABSTRACT FROM AUTHOR]

This paper focuses on securing confidential communication in multiple intelligent reflecting surfaces (IRS) assisted terahertz (THz) systems, where a potential eavesdropper can intercept either the base station (BS)-IRS link or the IRS-user link. Notably, the secure transmission may be intercepted and blocked by the eavesdropper due to the blockage-prone nature in THz bands. To characterize the blocking effects of the eavesdropper, the blocking-based path loss is first investigated. With the imperfect eavesdropper channel state information (ECSI), the worst-case secrecy rate (WCSR) is derived, and a joint optimization problem of hybrid beamforming at the BS and reflecting beamforming at the IRS is formulated. For the BS-IRS link eavesdropping, the zero-forcing (ZF) principle-based hybrid beamforming and the closed-form phase shifts of multiple IRSs are respectively proposed. For the IRS-user link eavesdropping, an iterative algorithm is proposed to tackle the non-convex optimization problem with a given information leakage threshold. Finally, a robust secure transmission strategy for multi-eavesdropper systems is further investigated. Simulation results demonstrate that compared with blockage-unaware scenarios, our proposed scheme can resist the adverse effects of the blockage-prone nature of THz waves on information security, and significantly boost secrecy performance. [ABSTRACT FROM AUTHOR]

Due to the gap between calculation-intensive and latency-sensitive features of self-driving tasks and the vehicle's limited computation and storage resources, the development of autonomous vehicles is situated in a bottleneck. Fortunately, multi-access edge computing (MEC) architecture offers a promising measure to bridge this gap. It brings resources proximity to mobile devices at the network edge to support task offloading. Nonetheless, it introduces the problem of insecure wireless communication. Beyond that, the autonomous vehicle also has strict requirements on latency and energy consumption of task offloading technique owing to its safety-related and electricity-driven characteristics. Many scholars have studied the related problems widely. Nevertheless, few papers combine these three significant factors, namely information security, task latency and energy consumption. Thus, we put forward a mobility-ware task offloading scheme in cellular vehicle-to-everything system, which carefully considers these three requirements together. In this scheme, a time approximation algorithm is proposed to tackle the channel condition variations caused by the vehicle movement. Small-cell base station proactively sends artificial noise to debase wiretapper decoding capability and further prevent data exchanged between vehicles and MEC from being eavesdropped at the physical layer. Moreover, the optimal offloading proportion is found to minimize the weighted sum of latency and energy consumption. Experimental performances validate the effectiveness and feasibility of the proposed task offloading scheme. [ABSTRACT FROM AUTHOR]

The spread of digital technologies in production has increased production capacity and enterprise potential. Practice shows the negative consequences associated with the leakage of confidential information. The cyberattacks on the industrial sector demonstrate that information security is the most important strategic task at the international level. [ABSTRACT FROM AUTHOR]

Digital watermarking is a technique in which secret information is embedded inside a host signal, which may be an audio, video, or image signal. Watermarking is also defined as a branch of information hiding that is used to hide proprietary information in digital media. Digital watermarking may be used for authentication, integrity, or information security. In this article, we present a comprehensive survey on watermarking techniques based on their characteristics and applications, including visible, invisible, robust, fragile, and semifragile watermarking methods. The parameters that are used for the analysis of watermarking algorithms are peak signal-to-noise ratio (PSNR), mean square error (MSE), embedding capacity, bit rate, and bit error rate (BER). [ABSTRACT FROM AUTHOR]