Search

In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively. [ABSTRACT FROM AUTHOR]

Remote healthcare monitoring (RHM) has become a pivotal component of modern healthcare, offering a crucial lifeline to numerous patients. Ensuring the integrity and privacy of the data generated and transmitted by IoT devices is of paramount importance. The integration of blockchain technology and smart contracts has emerged as a pioneering solution to fortify the security of internet of things (IoT) data transmissions within the realm of healthcare monitoring. In today's healthcare landscape, the IoT plays a pivotal role in remotely monitoring and managing patients' well-being. Furthermore, blockchain's decentralized and immutable ledger ensures that all IoT data transactions are securely recorded, timestamped, and resistant to unauthorized modifications. This heightened level of data security is critical in healthcare, where the integrity and privacy of patient information are nonnegotiable. This research endeavors to harness the power of blockchain and smart contracts to establish a robust and tamper-proof framework for healthcare IoT data. Employing smart contracts, which are self-executing agreements programmed with predefined rules, enables us to automate and validate data transactions within the IoT ecosystem. These contracts execute automatically when specific conditions are met, eliminating the need for manual intervention and oversight. This automation not only streamlines the process of data processing but also enhances its accuracy and reliability by reducing the risk of human error. Additionally, smart contracts provide a transparent and tamper-proof mechanism for verifying the validity of transactions, thereby mitigating the risk of fraudulent activities. By leveraging smart contracts, organizations can ensure the integrity and efficiency of data transactions within the IoT ecosystem, leading to improved trust, transparency, and security. Our experiments demonstrate the application of a blockchain approach to secure transmissions in IoT for RHM, as will be illustrated in the paper. This showcases the practical applicability of blockchain technology in real-world scenarios. [ABSTRACT FROM AUTHOR]

With the widespread adoption of blockchain technology, its public ledger characteristic enhances transaction transparency but also amplifies the risk of privacy breaches. Attackers can infer users' real identities and behaviors by analyzing public transaction patterns and address relationships, posing a severe threat to users' privacy and security, and thus hindering further advancements in blockchain applications. To address this challenge, covert communication has emerged as an effective strategy for safeguarding the privacy of blockchain users and preventing information leakage. But existing blockchain-based covert communication schemes rely solely on the immutability of blockchain itself for robustness and suffer from low transmission efficiency. To tackle these issues, this paper proposes a stealthy communication model with blockchain smart contract for bidding systems. The model initiates by preprocessing sensitive information using a secret-sharing algorithm-the Shamir (t, n) threshold scheme-and subsequently embeds this information into bidding amounts, facilitating the covert transfer of sensitive data. We implemented and deployed this model on the Ethereum platform and conducted comprehensive performance evaluations. To assess the stealthiness of our approach, we employed a suite of statistical tests including the CDF, the Kolmogorov–Smirnov test, Welch's t-test and K–L divergence. These analyses confirmed that amounts carrying concealed information were statistically indistinguishable from regular transactions, thus validating the effectiveness of our solution in maintaining the anonymity and confidentiality of information transmission within the blockchain ecosystem. [ABSTRACT FROM AUTHOR]

In the development of the Power Industry Internet of Things, the security of data interaction has always been an important challenge. In the power-based blockchain Industrial Internet of Things, node data interaction involves a large amount of sensitive data. In the current anti-leakage strategy for power business data interaction, regular expressions are used to identify sensitive data for matching. This approach is only suitable for simple structured data. For the processing of unstructured data, there is a lack of practical matching strategies. Therefore, this paper proposes a deep learning-based anti-leakage method for power business data interaction, aiming to ensure the security of power business data interaction between the State Grid business platform and third-party platforms. This method combines named entity recognition technologies and comprehensively uses regular expressions and the DeBERTa (Decoding-enhanced BERT with disentangled attention)-BiLSTM (Bidirectional Long Short-Term Memory)-CRF (Conditional Random Field) model. This method is based on the DeBERTa (Decoding-enhanced BERT with disentangled attention) model for pre-training feature extraction. It extracts sequence context semantic features through the BiLSTM, and finally obtains the global optimal through the CRF layer tag sequence. Sensitive data matching is performed on interactive structured and unstructured data to identify privacy-sensitive information in the power business. The experimental results show that the F1 score of the proposed method in this paper for identifying sensitive data entities using the CLUENER 2020 dataset reaches 81.26%, which can effectively prevent the risk of power business data leakage and provide innovative solutions for the power industry to ensure data security. [ABSTRACT FROM AUTHOR]

The paper presents a comprehensive exploration of a novel image encryption and decryption methodology, leveraging finite state machines (FSM) for the secure transformation of visual data. The study meticulously evaluates the effectiveness of the proposed encryption algorithm using a diverse image dataset. The encryption algorithm demonstrates high proficiency in obfuscating the original content of images, producing cipher images that resemble noise, thereby substantiating the encryption's effectiveness. The robustness of the proposed methodology is further evidenced by its performance in the National Institute of Standards and Technology Statistical Test Suite (NIST STS). Such achievements highlight the algorithm's capability to maintain the stochastic integrity of encrypted data, a critical aspect of data security and confidentiality. Histogram analysis revealed that the encryption process achieves a uniform distribution of pixel values across the encrypted images, masking any identifiable patterns and enhancing the security level. Correlation analysis corroborated the success of the encryption technique, showing a substantial reduction in the correlation among adjacent pixel values, thereby disrupting spatial relationships essential for deterring unauthorized data analysis. This improvement indicates the algorithm's efficiency in altering pixel patterns to secure image data. Additionally, a comparative analysis of correlation coefficients using various encryption methods on the Lenna image offered insights into the relative effectiveness of different techniques, emphasizing the importance of method selection based on specific security requirements and data characteristics. [ABSTRACT FROM AUTHOR]

Situational awareness (SA) is of tremendous significance for successful operations in many domains, such as surveillance, humanitarian, search, and rescue missions, and national security. SA is particularly important for the defense sector, and is regarded as the decisive factor in military and air combat engagements. Commanders and operators rely on the accuracy and fidelity of SA for comprehending the environment, decision-making, and carrying out actions based on these decisions for accomplishing a mission. SA, however, is susceptible to adversarial attacks that can compromise the security and trust of SA systems. In this paper, we discuss the significance of security and trust of SA from an air force perspective. We then propose a model for quantifying the trustworthiness of an SA system. We further present numerical examples that demonstrate the quantification of trustworthiness of an SA system using our proposed model. Finally, we conclude this paper with future research directions for quantifying the security of SA systems. [ABSTRACT FROM AUTHOR]

In an era marked by rapid advancements in information technology, the task of risk assessment for data security within the complex infrastructure of the power grid has become increasingly vital. This paper introduces a novel methodology for dynamic, scenario-adaptive risk assessment, specifically designed to address the entire lifecycle of power data. Integrating hierarchical analysis with fuzzy comprehensive evaluation, our approach provides a flexible and robust framework for assessing and managing risks in various scenarios. This method enables the generation of adaptive weight matrices and precise risk level determinations, ensuring a detailed and responsive analysis of data security at each lifecycle stage. In our study, we applied predictive analytics and anomaly detection to conduct a thorough examination of diverse data scenarios within the power grid, aiming to proactively identify and mitigate potential security threats. The results of this research demonstrate a significant enhancement in the effectiveness of risk detection and management, leading to improved data protection and operational efficiency. This study contributes a scalable, adaptable model for data security risk assessment, meeting the challenges of big data and complex information systems in the power sector. [ABSTRACT FROM AUTHOR]

In actual operation, there are security risks to the data of the network control system, mainly in the form of possible eavesdropping of signals in the transmission channel and parameters in the controller leading to data leakage. In this paper, we propose a scheme for encrypting linear controllers using fully homomorphic encryption, which effectively removes these security risks and substantially improves the security of networked control systems. Meanwhile, this paper uses precomputation to handle data encryption, which eliminates the encryption time and solves the drawback of fully homomorphic encryption that it is difficult to apply due to the efficiency problem. Compared to previous schemes with precomputation, for the first time, we propose two methods to mitigate the problem of the slight security degradation caused by precomputation, which makes our scheme more secure. Finally, we provide numerical simulation results to support our scheme, and the data show that the encrypted controller achieves normal control and improves safety and efficiency. [ABSTRACT FROM AUTHOR]

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access. [ABSTRACT FROM AUTHOR]

With the rapid growth of power demand and the advancement of new power system intelligence, smart energy measurement system data quality and security are also facing the influence of diversified factors. To solve the series of problems such as low data prediction efficiency, poor security perception, and "data islands" of the new power system, this paper proposes a federated learning system based on the Improved Hunter–Prey Optimizer Optimized Wavelet Neural Network (IHPO-WNN) for the whole-domain power load prediction. An improved HPO algorithm based on Sine chaotic mapping, dynamic boundaries, and a parallel search mechanism is first proposed to improve the prediction and generalization ability of wavelet neural network models. Further considering the data privacy in each station area and the potential threat of cyber-attacks, a localized differential privacy-based federated learning architecture for load prediction is designed by using the above IHPO-WNN as a base model. In this paper, the actual dataset of a smart energy measurement master station is selected, and simulation experiments are carried out through MATLAB software to test and examine the performance of IHPO-WNN and the federal learning system, respectively, and the results show that the method proposed in this paper has high prediction accuracy and excellent practical performance. [ABSTRACT FROM AUTHOR]

A Cyber–Physical–Social System (CPSS) is an evolving subset of Cyber–Physical Systems (CPS), which involve the interlinking of the cyber, physical, and social domains within a system-of-systems mindset. CPSS is in a growing state, which combines secure digital technologies with physical systems (e.g., sensors and actuators) and incorporates social aspects (e.g., human interactions and behaviors, and societal norms) to facilitate automated and secure services to end-users and organisations. This paper reviews the field of CPSS, especially in the scope of complexity theory and cyber security to determine its impact on CPS and social media's influence activities. The significance of CPSS lies in its potential to provide solutions to complex societal problems that are difficult to address through traditional approaches. With the integration of physical, social, and cyber components, CPSS can realize the full potential of IoT, big data analytics, and machine learning, leading to increased efficiency, improved sustainability and better decision making. CPSS presents exciting opportunities for innovation and advancement in multiple domains, improving the quality of life for people around the world. Research challenges to CPSS include the integration of hard and soft system components within all three domains, in addition to sociological metrics, data security, processing optimization and ethical implications. The findings of this paper note key research trends in the fields of CPSS, and recent novel contributions, followed by identified research gaps and future work. [ABSTRACT FROM AUTHOR]

This paper defines new classes of algorithms for securing and sharing visual information. Algorithms offering data protection against unauthorised access are cryptographic protocols for data sharing and splitting. These protocols ensure the division of information among a trusted group of secret holders, with every protocol participant being allocated a specified number of shares in the executed algorithm. Proposing and defining new solutions in the field of cryptographic algorithms for data sharing constitutes the main topic of this paper. This paper discusses a new class of algorithms for secret sharing with the use of linguistic formalisms dedicated to the processes of meaning interpretation and linguistic data sharing. Linguistic threshold schemes serve the processes of data protection in distributed systems; they are also used to distribute the shared secret parts in an optimum way, and to perform the meaning analysis and interpretation of various data sets. Semantic analysis as an element of the impact assessment of the meaning of the interpreted and analysed data will make it possible to take into consideration a much wider aspect of description and interpretation of the analysed phenomenon or data set; it will also enable the assessment of the core of the characterised sets in respect to other information with related meaning. The proposed protocols enhance the security of shared data, and allow the generation of any number of secret shares, which is greater than traditional secret sharing methods. [ABSTRACT FROM AUTHOR]

Cloud cryptography is the art of converting plain text into an unreadable format, which protects data and prevents the data from being misused by the attacker. Different researchers designed various Caesar cipher algorithms for data security. With the help of these algorithms, the data can be converted into a nonreadable format, but the data cannot be completely secured. In this paper, data security is provided in different phases. Firstly, data are secured through a bit-reversing mechanism in which those replace the actual values with no relation to the original data. Then the four-bit values are added at the beginning and end of bits using a salting mechanism to interlink the salting and existing bit-values and hide the original data. A Caesar cipher value is obtained by applying the Caesar cipher algorithm to the resulting bits. The Caesar cipher algorithm is used to implement number-of-shifting on the obtained values. An efficient cipher matrix algorithm is then developed in which different rules are designed to encrypt the data. Afterward, a secure cipher value is obtained by implementing Cipher XORation rules on the result obtained and the user-defined key. In the end, the proposed algorithm is compared with various papers. It identifies how much better the proposed algorithm performs than all the previous algorithms and how much the attack rate can be reduced if this algorithm is used for data security. [ABSTRACT FROM AUTHOR]

The theoretical and practical progress that has occurred in the field of computer networks during the past fifteen years has enhanced the economical efficiency and social relevance of related real-world use cases. Nevertheless, this ubiquitous usage has also introduced numerous security risks. Therefore, monitoring hardware and software resources represents one of the main instruments used in order to prevent potential attacks and to ensure the security and reliability of a network. Various solutions have been reported in the related scientific literature. In essence, most of the existing approaches are not suitable to implement a real-time hardware monitoring and management solution, particularly in heterogeneous networks. Therefore, the main contribution of this paper is represented by an architectural and implementational model, which is effective in order to build an interconnected system that can help system and network administrators to secure a network. This requirement is met by considering symmetrical design and implementation features related to various operating systems. Thus, the existing symmetrical relationships among identified parameters allow for the data to be wrapped into the same custom network packages, which are transported over the communication medium or are stored using the same data structures or tables. The system has been thoroughly assessed considering several real-world use case scenarios, and the results demonstrate that the proposed model can be applied to software-defined networks, which can be protected by relevant intrusion detection systems (IDS). [ABSTRACT FROM AUTHOR]

To ensure the security of online-shared power data, this paper adopts a robust soliton distribution-based zero-watermarking approach for tracing semi-structured power data. The method involves extracting partial key-value pairs to generate a feature sequence, processing the watermark into an equivalent number of blocks. Robust soliton distribution from erasure codes and redundant error correction codes is utilized to generate an intermediate sequence. Subsequently, the error-corrected watermark information is embedded into the feature sequence, creating a zero-watermark for semi-structured power data. In the tracking process, the extraction and analysis of the robust zero-watermark associated with the tracked data facilitate the effective identification and localization of data anomalies. Experimental and simulation validation demonstrates that this method, while ensuring data security, achieves a zero-watermark extraction success rate exceeding 98%. The proposed approach holds significant application value for data monitoring and anomaly tracking in power systems. [ABSTRACT FROM AUTHOR]

In the face of numerous challenges in supply chain management, new technologies are being implemented to overcome obstacles and improve overall performance. Among these technologies, blockchain, a part of the distributed ledger family, offers several advantages when integrated with ERP systems, such as transparency, traceability, and data security. However, blockchain remains a novel, complex, and costly technology. The purpose of this paper is to guide decision-makers in determining whether integrating blockchain technology with ERP systems is appropriate during the pre-implementation phase. This paper focuses on the literature reviews, theories, and expert opinions to achieve its objectives. It first provides an overview of blockchain technology, then discusses its potential benefits to the supply chain, and finally proposes a framework to assist decision-makers in determining whether blockchain meets the needs of their consortium and whether this integration aligns with available resources. The results highlight the complexity of blockchain, the importance of detailed and in-depth research in deciding whether to integrate blockchain technology into ERP systems, and future research prospects. The findings of this article also present the critical decisions to be made prior to the implementation of blockchain, in the event that decision-makers choose to proceed with blockchain integration. The findings of this article augment the existing literature and can be applied in real-world contexts by stakeholders involved in blockchain integration projects with ERP systems. [ABSTRACT FROM AUTHOR]

It is clear that 5G networks have already become integral to our present. However, a significant issue lies in the fact that current 5G communication systems are incapable of fully ensuring the required quality of service and the security of transmitted data, especially in government networks that operate in the context of the Internet of Things, hostilities, hybrid warfare, and cyberwarfare. The use of 5G extends to critical infrastructure operators and special users such as law enforcement, governments, and the military. Adapting modern cellular networks to meet the specific needs of these special users is not only feasible but also necessary. In doing so, these networks must meet additional stringent requirements for reliability, performance, and, most importantly, data security. This scientific paper is dedicated to addressing the challenges associated with ensuring cybersecurity in this context. To effectively improve or ensure a sufficient level of cybersecurity, it is essential to measure the primary indicators of the effectiveness of the security system. At the moment, there are no comprehensive lists of these key indicators that require priority monitoring. Therefore, this article first analyzed the existing similar indicators and presented a list of them, which will make it possible to continuously monitor the state of cybersecurity systems of 5G cellular networks with the aim of using them for groups of special users. Based on this list of cybersecurity KPIs, as a result, this article presents a model to identify and evaluate these indicators. To develop this model, we comprehensively analyzed potential groups of performance indicators, selected the most relevant ones, and introduced a mathematical framework for their quantitative assessment. Furthermore, as part of our research efforts, we proposed enhancements to the core of the 4G/5G network. These enhancements enable data collection and statistical analysis through specialized sensors and existing servers, contributing to improved cybersecurity within these networks. Thus, the approach proposed in the article opens up an opportunity for continuous monitoring and, accordingly, improving the performance indicators of cybersecurity systems, which in turn makes it possible to use them for the maintenance of critical infrastructure and other users whose service presents increased requirements for cybersecurity systems. [ABSTRACT FROM AUTHOR]

Medical health records hold personal medical information and should only be accessed by authorized medical personnel or concerned patients. The importance of medical health records privacy is increasing as these records are shared in cloud environments. In this paper, we propose an enhanced system for securing patient data (Medical Health Records) embedded in multiple medical images in fragments for secure transmission and public sharing on the cloud or other environments. To protect the patient's privacy, Medical Records are first encrypted, and then the ciphertext is broken into several fragments based on the number of multimodal medical images of a patient. A key generator randomly selects medical images from the multimodal image data to embed the encrypted patient health record segment using a modified least significant bit embedding process. The proposed technique enables an extra layer of security as even if files fall into the wrong hands and a fragment of the file is decrypted, it will not present any understandable information until all fragments from other medical images are extracted and combined in the correct order. The experiments are performed using multimodal 3255 MRI scans of 21 patients. The robustness of the proposed method was measured using different metrics such as PSNR, MSE, and SSIM. The results show that the proposed system is robust and that image quality is also maintained. To further study the stego image quality, a deep learning-based classification was applied to the images, and the results show that the diagnosis using stego medical images and performance remains unaffected even after embedding the encrypted data. [ABSTRACT FROM AUTHOR]

Secret sharing is a data security technique that divides secret information into multiple parts, embeds these parts into various shares, and distributes these shares to different participants. The original secret information can be retrieved only when the number of shares gathered meets a required threshold. This paper proposes a secret sharing method that can hide data in encrypted images with reversibility and allows content owners to add an additional layer of security before uploading data to the cloud. This method enables the independent extraction of images and data, ensuring that the recovered images and extracted data can serve as validation information for each other. The proposed method not only enhances data security but also guarantees the accuracy of the extracted information. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) continues to proliferate, the demand for efficient and secure data processing at the network edge has grown exponentially. Fog computing, a paradigm that extends cloud capabilities to the edge of the network, plays a pivotal role in meeting these requirements. In this context, the reliable and trustworthy forwarding of data is of paramount importance. This paper presents an innovative mechanism designed to ensure the trustworthiness of data forwarding in the context of MQTT (Message Queuing Telemetry Transport), a widely adopted IoT communication protocol. Our proposed mechanism leverages the inherent advantages of MQTT to establish a robust and secure data-forwarding scheme. It integrates fog computing resources seamlessly into the MQTT ecosystem, enhancing data reliability and security. The mechanism employs trust models to evaluate the credibility of IoT devices and fog nodes involved in data forwarding, enabling informed decisions at each stage of the transmission process. Key components of the mechanism include secure communication protocols, authentication mechanisms, and data integrity verification. The proposed secure communication protocols (TLS/SSL, MQTTS, and PKI) and data integrity verification methods (MAC, digital signatures, checksums, and CRC) provide a robust framework for ensuring secure and trustworthy data transmission in IoT systems. These elements collectively contribute to the establishment of a reliable data forwarding pipeline within MQTT. Additionally, the mechanism prioritizes low-latency communication and efficient resource utilization, aligning with the real-time requirements of IoT applications. Through empirical evaluations and simulations, the research demonstrates the effectiveness of our proposed mechanism in improving the trustworthiness of data forwarding, while minimizing overhead, as the experiment was conducted with 15 fog nodes, and the maximum Level of Trust (LoT) score was 0.968, which is very high, with an estimated accuracy of 97.63%. The results indicate that our approach significantly enhances data security and reliability in MQTT-based IoT environments, thereby facilitating the seamless integration of fog computing resources for edge processing. [ABSTRACT FROM AUTHOR]

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model's effectiveness in providing secure and high-performance DApp solutions. [ABSTRACT FROM AUTHOR]

Security adversaries are rampant on the Internet, constantly seeking vulnerabilities to exploit. The sheer proliferation of these sophisticated threats necessitates innovative and swift defensive measures to protect the vulnerable infrastructure. Tools such as honeypots effectively determine adversary behavior and safeguard critical organizational systems. However, it takes a significant amount of time to analyze these attacks on the honeypots, and by the time actionable intelligence is gathered from the attacker's tactics, techniques, and procedures (TTPs), it is often too late to prevent potential damage to the organization's critical systems. This paper contributes to the advancement of cybersecurity practices by presenting a cutting-edge methodology, capitalizing on the synergy between artificial intelligence and threat analysis to combat evolving cyber threats. The current research articulates a novel strategy, outlining a method to analyze large volumes of attacker data from honeypots utilizing large language models (LLMs) to assimilate TTPs and apply this knowledge to identify real-time anomalies in regular user activity. The effectiveness of this model is tested in real-world scenarios, demonstrating a notable reduction in response time for detecting malicious activities in critical infrastructure. Moreover, we delve into the proposed framework's practical implementation considerations and scalability, underscoring its adaptability in diverse organizational contexts. [ABSTRACT FROM AUTHOR]

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) plays an essential role in people's daily lives, such as healthcare, home, traffic, industry, and so on. With the increase in IoT devices, there emerge many security issues of data loss, privacy leakage, and information temper in IoT network applications. Even with the development of quantum computing, most current information systems are weak to quantum attacks with traditional cryptographic algorithms. This paper first establishes a general security model for these IoT network applications, which comprises the blockchain and a post-quantum secure identity-based signature (PQ-IDS) scheme. This model divides these IoT networks into three layers: perceptual, network, and application, which can protect data security and user privacy in the whole data-sharing process. The proposed PQ-IDS scheme is based on lattice cryptography. Bimodal Gaussian distribution and the discrete Gaussian sample algorithm are applied to construct the fundamental difficulty problem of lattice assumption. This assumption can help resist the quantum attack for information exchange among IoT devices. Meanwhile, the signature mechanism with IoT devices' identity can guarantee non-repudiation of information signatures. Then, the security proof shows that the proposed PQ-IDS can obtain the security properties of unforgeability, non-repudiation, and non-transferability. The efficiency comparisons and performance evaluations show that the proposed PQ-IDS has good efficiency and practice in IoT network applications. [ABSTRACT FROM AUTHOR]

This paper addresses the scientific problem of enhancing the security and capacity of steganographic methods for protecting digital media. The primary aim is to develop an advanced steganographic technique that optimizes quantization tables to surpass the traditional F5 algorithm in terms of security, capacity, and robustness. The novelty of this research lies in the introduction of the F5A method, which utilizes optimized quantization tables to significantly increase the capacity for concealed information while ensuring high-quality image retention and resistance to unauthorized content recovery. The F5A method integrates cryptographic keys and features to detect and prevent copyright infringement in real time. Experimental evaluations demonstrate that the F5A method improves the mean square error and peak signal-to-noise ratio indices by 1.716 and 1.121 times, respectively, compared to the traditional F5 algorithm. Additionally, it increases the steganographic capacity by up to 1.693 times for smaller images and 1.539 times for larger images. These results underscore the effectiveness of the F5A method in enhancing digital media security and copyright protection. [ABSTRACT FROM AUTHOR]

To ensure the security of sensitive data, elliptic curve cryptography (ECC) is adopted as an asymmetric method that balances security and efficiency. Nevertheless, embedding messages into elliptic curve (EC) points poses a significant challenge. The intricacies of this process can greatly affect the overall security and efficiency of the cryptosystem, reflecting security vulnerabilities observed in many existing schemes that utilize ElGamal ECC-based encryption. In this paper, we introduce an innovative hash-based technique for securely embedding messages into EC points before encryption. A random parameter and a shared secret point generated through the EC Diffie–Hellman protocol are used to bolster the scheme's security. The security of the proposed method is evaluated against various attack models; moreover, the complexity, and sensitivity of the encryption scheme, as well as its inputs, are analyzed. The randomness assessment of the ciphertext was performed using the NIST statistical test suite. Additionally, we propose a mechanism to ensure the integrity of the message by securely appending a tag to the ciphertext. As a consequence, a comprehensive analysis of our scheme demonstrates its effectiveness in maintaining data security and integrity against various attack models. The algorithm also meets more criteria such as the strict avalanche criterion, linear complexity, and operability. [ABSTRACT FROM AUTHOR]

Team score orienteering, a challenging and interesting sport, is gradually becoming known by the majority of sports enthusiasts. Integrating team score orienteering with the Internet can enhance the interactive experience for athletes. However, this integration increases the risk of the leakage of the athletes' information. In order to protect the privacy of athletes, it is necessary to employ encryption. Therefore, this paper proposes an efficient lattice-based revocable certificateless public key encryption (RCL-PKE) scheme with decryption key exposure resistance (DKER). The adoption of certificateless encryption not only avoids the complex certificate management required for traditional public key encryption, but also addresses the key escrow problem of identity-based encryption, thereby significantly ensuring data security and privacy. Furthermore, the revocable mechanism enables the organizing committee to flexibly manage the athletes' qualification for competitions, and DKER can effectively prevent the leakage of decryption keys, which further enhances data security. The constructed RCL-PKE scheme was proven to be IND-CPA secure under the learning with errors (LWE) assumption. Experiments indicated that the proposed RCL-PKE scheme had lower computation and communication costs, making it particularly suitable for team score orienteering. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency. [ABSTRACT FROM AUTHOR]

Developing a security solution for spatial files within today's enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in "data silos" of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution's usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS. [ABSTRACT FROM AUTHOR]

Emerging cyber threats' sophistication, impact, and complexity rapidly evolve, confronting organizations with demanding challenges. This severe escalation requires a deeper understanding of adversary dynamics to develop enhanced defensive strategies and capabilities. Cyber threat actors' advanced techniques necessitate a proactive approach to managing organizations' risks and safeguarding cyberspace. Cyber risk management is one of the most efficient measures to anticipate cyber threats. However, it often relies on organizations' contexts and overlooks adversaries, their motives, capabilities, and tactics. A new cyber risk management framework incorporating emergent information about the dynamic threat landscape is needed to overcome these limitations and bridge the knowledge gap between adversaries and security practitioners. Such information is the product of a cyber threat intelligence process that proactively delivers knowledge about cyber threats to inform decision-making and strengthen defenses. In this paper, we overview risk management and threat intelligence frameworks. Then, we highlight the necessity of integrating cyber threat intelligence and assessment in cyber risk management. After that, we propose a novel risk management framework with integrated threat intelligence on top of EBIOS Risk Manager. Finally, we apply the proposed framework in the scope of a national telecommunications organization. [ABSTRACT FROM AUTHOR]

In recent years, marked progress has been made in wearable technology for human motion and posture recognition in the areas of assisted training, medical health, VR/AR, etc. This paper systematically reviews the status quo of wearable sensing systems for human motion capture and posture recognition from three aspects, which are monitoring indicators, sensors, and system design. In particular, it summarizes the monitoring indicators closely related to human posture changes, such as trunk, joints, and limbs, and analyzes in detail the types, numbers, locations, installation methods, and advantages and disadvantages of sensors in different monitoring systems. Finally, it is concluded that future research in this area will emphasize monitoring accuracy, data security, wearing comfort, and durability. This review provides a reference for the future development of wearable sensing systems for human motion capture. [ABSTRACT FROM AUTHOR]

Featured Application: Analysis for encrypted traffic. To safeguard user privacy, critical Internet traffic is often transmitted using encryption. While encryption is crucial for protecting sensitive information, it poses challenges for traffic identification and poses hidden dangers to network security. As a result, the precise classification of encrypted network traffic has become a crucial problem in network security. In light of this, our paper proposes an encrypted traffic identification method based on the C-LSTM model for encrypted traffic recognition by leveraging the power of deep learning. This method can effectively extract spatial and temporal features from encrypted traffic, enabling accurate identification of traffic types. Through rigorous testing and evaluation, our system has achieved an impressive accuracy rate of 96.4% on the widely used ISCXVPN2016 dataset. This achievement demonstrates the effectiveness and reliability of our method in accurately classifying encrypted network traffic. By addressing the challenges posed by encrypted traffic identification, our research contributes to enhancing network security and privacy protection. [ABSTRACT FROM AUTHOR]

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection. [ABSTRACT FROM AUTHOR]

This research paper investigates the integration of blockchain technology to enhance the security of Android mobile app data storage. Blockchain holds the potential to significantly improve data security and reliability, yet faces notable challenges such as scalability, performance, cost, and complexity. In this study, we begin by providing a thorough review of prior research and identifying critical research gaps in the field. Android's dominant position in the mobile market justifies our focus on this platform. Additionally, we delve into the historical evolution of blockchain and its relevance to modern mobile app security in a dedicated section. Our examination of encryption techniques and the effectiveness of blockchain in securing mobile app data storage yields important insights. We discuss the advantages of blockchain over traditional encryption methods and their practical implications. The central contribution of this paper is the Blockchain-based Secure Android Data Storage (BSADS) framework, now consisting of six comprehensive layers. We address challenges related to data storage costs, scalability, performance, and mobile-specific constraints, proposing technical optimization strategies to overcome these obstacles effectively. To maintain transparency and provide a holistic perspective, we acknowledge the limitations of our study. Furthermore, we outline future directions, stressing the importance of leveraging lightweight nodes, tackling scalability issues, integrating emerging technologies, and enhancing user experiences while adhering to regulatory requirements. [ABSTRACT FROM AUTHOR]

This paper proposes a solution for ensuring the security of IoT devices in the cloud environment by protecting against distributed denial-of-service (DDoS) and false data injection attacks. The proposed solution is based on the integration of simple network management protocol (SNMP), Kullback–Leibler distance (KLD), access control rules (ACL), and moving target defense (MTD) techniques. The SNMP and KLD techniques are used to detect DDoS and false data sharing attacks, while the ACL and MTD techniques are applied to mitigate these attacks by hardening the target and reducing the attack surface. The effectiveness of the proposed framework is validated through experimental simulations on the Amazon Web Service (AWS) platform, which shows a significant reduction in attack probabilities and delays. The integration of IoT and cloud technologies is a powerful combination that can deliver customized and critical solutions to major business vendors. However, ensuring the confidentiality and security of data among IoT devices, storage, and access to the cloud is crucial to maintaining trust among internet users. This paper demonstrates the importance of implementing robust security measures to protect IoT devices in the cloud environment and highlights the potential of the proposed solution in protecting against DDoS and false data injection attacks. [ABSTRACT FROM AUTHOR]

Voyage Data Recorders (VDRs), often referred to as the 'black boxes' of the shipping industry, collect and store vital data from key sensors and locations around the ship. This data plays a pivotal role in incident investigation, as was seen in the grounding of the Costa Concordia in 2012, and the sinking of the El Faro in 2015. With such an important role to play, the International Maritime Organization (IMO) has mandated that all SOLAS registered ships carry a VDR, which can demonstrate compliance with internationally agreed standards. Without a VDR compliant with these standards a ship cannot sail. However, the rise in the number, and sophistication, of digital devices are making the sector increasingly vulnerable to cyber-attacks. This paper will demonstrate a number of high-risk VDR cyber security vulnerabilities and review the current international technical standards covering all VDR devices being manufactured and used today, drawing attention to the minimum security requirements. The paper will go on to discuss how these standards fail to promote the necessary levels of cyber security needed to protect VDRs from today's cyber risks, amidst increased demands for digital connectivity for remote and autonomous operations. The paper will conclude by proposing several amendments (technical and non-technical) to the current standards which, if adopted, will help increase the minimum level of security of VDRs. Industry opinions were gathered on this topic, and their beliefs have been included across this paper. [ABSTRACT FROM AUTHOR]

In this work, we elaborate on the concept of process authenticity, which intuitively corresponds to the validity of all process steps and their proper binding. It represents the most exciting forefront of distributed ledger technology research concerning the primary challenge of reliably connecting distributed ledger networks to the physical context it must operate. More in detail, the paper describes a novel methodological approach to ensure the authenticity of business processes through blockchain and several security mechanisms applied to the digital twins of the actual processes. We illustrate difficulties and opportunities deriving from implementing process authenticity in concrete case studies in which we were involved as software designers belonging to three critical application domains: document dematerialization, e-voting, and healthcare. [ABSTRACT FROM AUTHOR]

Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) is an advanced data protection technology that allows the embedding of additional information into an original digital medium while maintaining its integrity. Color images are typical carriers for information because of their rich data content, making them suitable for data embedding. Compared to grayscale images, color images with their three color channels (RGB) enhance data embedding capabilities while increasing algorithmic complexity. When implementing RDH in color images, researchers often exploit the inter-channel correlation to enhance embedding efficiency and minimize the impact on image visual quality. This paper proposes a novel RDH method for color images based on inter-channel correlation modeling and improved skewed histogram shifting. Initially, we construct an inter-channel correlation model based on the relationship among the RGB channels. Subsequently, an extended method for calculating the local complexity of pixels is proposed. Then, we adaptively select the pixel prediction context and design three types of extreme predictors. The improved skewed histogram shifting method is utilized for data embedding and extraction. Finally, experiments conducted on the USC-SIPI and Kodak datasets validate the superiority of our proposed method in terms of image fidelity. [ABSTRACT FROM AUTHOR]

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the "Secure Words on Joining Key (SWJK)" algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. [ABSTRACT FROM AUTHOR]

Due to the necessity to realize "building a strong transportation nation", the construction of intermodal transportation is based on the information resource integration of diverse transport systems. To ensure the data security during the interaction between different transport modes, as well as the effect of data application, the status of entities and data flow in the network should be supervised throughout. Therefore, an evaluation framework of comprehensive trust is proposed in this paper. With feature analysis of transportation big data, a quality assessment is conducted by three-dimensional metric sets, which is considered as a significant factor of trust measurement. Furthermore, a hierarchical trust structure is put forward to assess the trust of entities in different levels, in terms of the static and dynamic evidence. Furthermore, the visualization of a dynamic global information security state is discussed, based on temporal knowledge graphs. As shown in practical application and simulation analysis, this framework can meet the requirements of data security supervision and lay the foundation of further intelligent management. This research is of great significance to improve the data security level in intermodal transport, and to promote the utilization and sharing of public information resources. [ABSTRACT FROM AUTHOR]

Real-time electrical impedance tomography (EIT) data sharing is becoming increasingly necessary, due to the extensive use of EIT technology in various sectors, including material analysis, biomedicine, and industrial process monitoring. The prevalence of portable EIT equipment and remote imaging technology has led to a predominance of centralized storage, Internet protocol transmission, and certificates from certificate authorities (CA) in telemedicine data. This has resulted in compromised data security, network communication delays, high CA maintenance costs, increased risks of medical data privacy breaches, and low security. Therefore, this paper offers a consortia blockchain-based method for exchanging EIT data that addresses security and integrity concerns during data storage and exchange, while maintaining transparency and traceability. Proprietary re-encryption techniques are employed to guarantee traceability when exchanging anonymous data, enabling precise control over data access. This scheme serves to protect both data and identity privacy, as well as to trace the actual identity of potential malicious users, while also thwarting any coordinated efforts between partially trusted parties and data requesters seeking unauthorized access to confidential information. Additionally, a combination of blockchain and InterPlanetary File System (IPFS) distributed storage technology is utilized to ease the burden of EIT data storage. The feasibility and effectiveness of the proposed solution were validated through a series of experiments, demonstrating its ability to effectively prevent data tampering and misuse, reduce data management costs, and enhance the efficiency and quality of data sharing. [ABSTRACT FROM AUTHOR]

The digital transformation of critical infrastructures, such as energy or water distribution systems, is essential for their smart management. Faster issue identification and smoother services enable better adaptation to consumers' evolving demands. However, these large-scale infrastructures are often outdated. Their digital transformation is crucial to enable them to support societies. This process must be carefully planned, providing guidance that ensures that the data they rely on are dependable and that the system remains fully operational during the transition. This paper presents a formal model that supports reliable data acquisition in legacy critical infrastructures, facilitating their evolution towards a data-driven smart system. Our model provides the foundation for a flexible transformation process while generating dependable data for system management. We demonstrate the model's applicability in a use case within the water distribution domain and discuss its benefits. [ABSTRACT FROM AUTHOR]

The cloudification and virtualisation of railway functions have the potential to improve railway operation efficiency, reliability, safety, and security, as well as to enhance passenger experience by offering innovative services. This paper considers issues related to the management and orchestration of railway clouds that host cloudified railway functions. A microservices-based approach to the design of railway cloud management and orchestration functionality is proposed. The basic railway cloud concepts were defined, and functionality related to the basic orchestration of the railway cloud and deployments is analysed in order to derive the requirements of platform resources and workload management. This functionality is further designed in the form of microservices, meaning that they could possibly be used in orchestration applications to enable improvements in scalability, fault isolation, and data security. The design of microservices follows the principles of the Representational State of Transfer (REST) application programming interface (API) as a set of interlinked resources. Resources related to railway cloud orchestration are identified with their associated data, relationships to other resources, and applicable methods. The resources' methods are used in railway applications to implement the required orchestration functionality and to maintain the state of railway cloud orchestration processes. To verify the synthesised microservices, the common orchestration application logic and microservices' logic were modelled, and it was proved that the orchestration processes, which run concurrently, expose equivalent behaviour. The proposed approach was validated using a simulation, aiming to evaluate injected latency as a key performance indicator for the reliability and safety of railway operations. Additionally, some safety and security issues related to railway cloud management and orchestration are considered. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a technology that can connect billions of devices or "things" to other devices (machine to machine) or even to people via an existing infrastructure. IoT applications in real-world scenarios include smart cities, smart houses, connected appliances, shipping, monitoring, smart supply chain management, and smart grids. As the number of devices all over the world is increasing (in all aspects of daily life), huge amounts of data are being produced as a result. New issues are therefore arising from the use and development of current technologies, regarding new applications, regulation, cloud computing, security, and privacy. The blockchain has shown promise in terms of securing and preserving the privacy of users and data, in a decentralized manner. In particular, Hyperledger Fabric v2.x is a new generation of blockchain that is open source and offers versatility, modularity, and performance. In this paper, a blockchain as a service (BaaS) application based on Hyperledger Fabric is presented to address the security and privacy challenges associated with the IoT. A new architecture is introduced to enable this integration, and is developed and deployed, and its performance is analyzed in real-world scenarios. We also propose a new data structure with encryption based on public and private keys for enhanced security and privacy. [ABSTRACT FROM AUTHOR]

BIM collaborative design involves numerous participants from various specialties who create and share vast amounts of design data at different design stages to ensure the efficient transmission of design data between these specialties. It is imperative for the BIM collaborative design platform to guarantee the security of design data and effectively trace any instances of malicious leakage or tampering. Therefore, this paper proposes a traceable ciphertext-policy attribute-based encryption scheme (TCP-ABE) that formulates a dynamic data access control mechanism based on different participants and effectively tracks malicious users in the event of risks such as tampering, theft, and unauthorized access to BIM data. In this scheme, the user's identity information is embedded into their private key as a key component, ensuring that only users who satisfy the access policy can decrypt it. The compromised private key allows for tracing of the user's identity. Additionally, Linear Secret Sharing Scheme (LSSS) is employed as the access structure with the user's attribute set divided into an attribute name set and an attribute value set to protect privacy by concealing the latter within the access policy. Furthermore, the scheme integrates blockchain with cloud storage as a trusted third-party storage mechanism to ensure data integrity. Finally, the TCP-ABE scheme is comprehensively evaluated by comparing its strengths and weaknesses with other algorithms. This evaluation includes a theoretical analysis of functional and computational time overhead aspects, as well as an experimental analysis of initialization time, data encryption time, and data decryption time. The scheme exhibits excellent performance across all stages and encompasses the most comprehensive functionalities, as demonstrated by the comparative analysis and experimental results. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have successfully delivered cutting-edge performance in several fields. With the broader deployment of DNN models on critical applications, the security of DNNs has become an active and yet nascent area. Attacks against DNNs can have catastrophic results, according to recent studies. Poisoning attacks, including backdoor attacks and Trojan attacks, are one of the growing threats against DNNs. Having a wide-angle view of these evolving threats is essential to better understand the security issues. In this regard, creating a semantic model and a knowledge graph for poisoning attacks can reveal the relationships between attacks across intricate data to enhance the security knowledge landscape. In this paper, we propose a DNN poisoning attack ontology (DNNPAO) that would enhance knowledge sharing and enable further advancements in the field. To do so, we have performed a systematic review of the relevant literature to identify the current state. We collected 28,469 papers from the IEEE, ScienceDirect, Web of Science, and Scopus databases, and from these papers, 712 research papers were screened in a rigorous process, and 55 poisoning attacks in DNNs were identified and classified. We extracted a taxonomy of the poisoning attacks as a scheme to develop DNNPAO. Subsequently, we used DNNPAO as a framework by which to create a knowledge base. Our findings open new lines of research within the field of AI security. [ABSTRACT FROM AUTHOR]

From general systems to mission-critical systems at financial and government institutions, the application scope of cloud computing services is continuously expanding. Therefore, there is a need for better methods to ensure the stability and security of the cloud data and services. Monitoring the abnormal behavior of virtual machines (VMs) is one of the most-important means to identify the causes of security incidents related to the cloud. However, current traditional abnormal-behavior-detection methods for VMs on cloud platforms face multiple challenges such as privacy protection and the semantic gap. Virtualization technology plays a key role in cloud computing. Meanwhile, virtualization security is the core issue of cloud computing security as well. To address these issues, this paper proposes a feature-fusion-based abnormal-behavior-detection method (FFABD) in a virtualization environment. This method acquires the hardware features and syscalls of the VM at the physical machine level and the virtualization level, respectively. Therefore, this method is not limited by the operating system running on the VM. This makes our method more efficient and universally applicable compared to traditional abnormal-VM-detectionmethods. The ensemble learning model performs the best among all the models, achieving an Accuracy of 99.7%. [ABSTRACT FROM AUTHOR]

With the continuous development of urbanization, it has become an important issue to effectively alleviate urban road traffic congestion and improve traffic efficiency. By combining blockchain technology and shared buses, this paper builds an intelligent traffic-service scheduling management system based on blockchain. The system effectively solves the core problems of shared buses, improves data security and privacy protection, realizes intelligent scheduling and route planning, and simplifies cross-organization cooperation and settlement processes. The research shows that the system can reduce the distance and number of buses, and improve the service quality and operation efficiency while ensuring the waiting time of passengers. The results of this paper verify the feasibility and advantages of the system, bring innovation and improvement to the field of traffic management, and promote the sustainable development of urban intelligent traffic management system. Future research could further explore the application of blockchain technology in traffic management to achieve more intelligent and sustainable urban traffic management. [ABSTRACT FROM AUTHOR]

Free Space Optical (FSO) communication has potential terrestrial and non-terrestrial applications. It allows large bandwidth for higher data transfer capacity. Due to its high directivity, it has a potential security advantage over traditional radio frequency (RF) communications. However, eavesdropping attacks are still possible in long non-terrestrial transmission FSO links, where the geometry of the link allows foreign flying objects such as Unmanned Aerial vehicles (UAVs) and drones to interrupt the links. This exposes non-terrestrial FSO links to adversary security attacks. Hence, data security techniques implementation is required to achieve immune FSO communication links. Unlike the commonly proposed physical layer security techniques, this paper presents a lab-based demonstration of a secured FSO communication link based on data cryptography using the GNU Radio platform and software-defined radio (SDR) hardware. The utilized encryption algorithm (Xsalsa20) in this paper requires high-time complexity to be broken by power-limited flying objects that interrupt the FSO beam. The results show that implementing cryptographic encryption techniques into FSO systems provided resilience against eavesdropping attacks and preserved data security. The experiment results show that, at a distance of 250 mm and laser output power of 10 mW, the system achieves a packet delivery rate of 92 % and transmission rate of 10 Mbit/s. This is because the SDR used in this experiment requires a minimum received electrical amplitude of 27.5 mV to process the received signal. Long distance and higher data rates can be achieved using less sensitive SDR hardware. [ABSTRACT FROM AUTHOR]