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Low-resource utilization and high-energy consumption have become progressively protuberant issues in cloud data centers. Virtual Machine (VM) migration is the key objective to resolve this issue. Moreover, extreme VM migration might empower Service-Level Agreement (SLA) violations. Few works are considered for optimizing throughput and energy consumption. An efficient VM migration must consider different parameters like network communication overhead, migration overhead, resource utilization, energy consumption and quality of service which is a multi-objective issue. Hence, in this paper, a Modified Pelican Optimization-based Variable Load Mean Function (MPO-VLMF)-based host overload detection is presented and security enhancement is developed. The main motive of this study is to achieve host overload detection and security enhancement. To obtain host overload detection, the variable load mean function is developed. In this mean function, the weight parameter is selected by considering Modified Pelican Optimization (MPO). The Levy flight (LF) is considered for enhancing the updating process of Pelican Optimization (PO). To enhance the security of the system, the Digital Signature-based Encryption (DSE) is developed. Based on this proposed approach, security and host overload detection are obtained. The proposed technique is implemented and evaluated by performance measures. It is compared with the conventional approaches to justify the performance of the system. [ABSTRACT FROM AUTHOR]

Innovations in wearable technology and artificial intelligence have enabled consumer devices to process and transmit data about human mental states (cognitive, affective, and conative) through what this paper refers to as "cognitive biometrics." Devices such as brain-computer interfaces, extended reality headsets, and fitness wearables offer significant benefits in health, wellness, and entertainment through the collection and processing and cognitive biometric data. However, they also pose unique risks to mental privacy due to their ability to infer sensitive information about individuals. This paper challenges the current approach to protecting individuals through legal protections for "neural data" and advocates for a more expansive legal and industry framework, as recently reflected in the draft UNESCO Recommendation on the Ethics of Neurotechnology, to holistically address both neural and cognitive biometric data. Incorporating this broader and more inclusive approach into legislation and product design can facilitate responsible innovation while safeguarding individuals' mental privacy. Biometric data relating to human cognitive, affective, and conative states ("cognitive biometrics") are not adequately protected under existing privacy laws. The authors argue for an expansive approach that would treat neural and cognitive biometric data holistically as categories of "sensitive data" and provide them with analogous privacy protections. [ABSTRACT FROM AUTHOR]

This article examines the foundational principles and protocols that guide the development, implementation and use of encryption and their relevance and impact on technology law and policy. It first focuses on the technical aspects of encryption and explains the meanings of key terms and the different kinds and uses of this technology. It then expounds on the technical principles and protocols of encryption including information security, openness, provable security, and its adversarial nature. The article thereafter analyses the significance and import of these standards and rules on the proposed ghost protocol in encrypted messaging services and encryption law and policy more generally. It argues that technical principles such as information security, primacy of keys and resistance to attacks should be recognised and upheld in the development and adoption of encryption regulation. [ABSTRACT FROM AUTHOR]

Recently, wireless body area network (WBAN) becomes a hot research topic in the advanced healthcare system. The WBAN plays a vital role in monitoring the physiological parameters of the human body with sensors. The sensors are small in size, and it has a small-sized battery with limited life. Hence, the energy is limited in the multi-hop routing process. The patient data is collected by the sensor, and the data are transmitted with high energy consumption. It causes failure in the data transmission path. To avoid this, the data transmission process should be optimized. This paper presents an advanced authentication and energy-efficient routing protocol (AAERP) for optimal routing paths in WBAN. Patients' data are aggregated from the WBAN through the IoMT devices in the initial stage. To secure the patient's private data, a hybrid mechanism of the elliptic curve cryptosystem (ECC) and Paillier cryptosystem is proposed for the data encryption process. Data security is improved by authenticating the data before transmission using an encryption algorithm. Before the routing process, the data encryption approach converts the original plain text data into ciphertext data. This encryption approach assists in avoiding intrusions in the network system. The encrypted data are optimally routed with the help of the teamwork optimization algorithm (TOA) approach. The optimal path selection using this optimization technique improves the effectiveness and robustness of the system. The experimental setup is performed by using Python software. The efficacy of the proposed model is evaluated by solving parameters like network lifetime, network throughput, residual energy, success rate, number of packets received, number of packets sent, and number of packets dropped. The performance of the proposed model is measured by comparing the obtained results with several existing models. [ABSTRACT FROM AUTHOR]

AbstractIn recent years, transmitting medical data has been a regular process. Although strong, safe, and dependable encryption techniques are necessary for medical data, cryptography is largely a computational process. The research presents a selective encryption approach for the transfer of sensitive data. This study proposes a novel technique for selecting the optimal keys to offer more security to medical data. Initially, the medical data is encrypted using the hybrid AES–DES technique. To make an efficient encryption method, the most optimal keys are selected utilising an improved Cheetah optimisation algorithm (ICO). Finally, the keys are optimised, and the input medical data is safely kept in the cloud system according to the established model. As a result, the proposed approach utilises the Python tool to evaluate the results. The simulation results show that the proposed method outperforms others in terms of encryption time 96 s, decryption time 92 s, memory usage (16), and latency (0.006). [ABSTRACT FROM AUTHOR]

This article describes a control approach for obtaining predefined‐time robust tracking in multiplicative systems despite positive, bounded, and unknown multiplicative disturbances. The proposed approach is distinguished by imposing predefined‐time convergence, a topic previously studied in conventional calculus in the context of multiplicative systems. Multiplicative calculus is recognized as a beneficial tool that complements standard calculus by simplifying the modeling and comprehension of numerous processes. Simulations are carried out to illustrate that the given control strategy enforces convergence before a predefined time instant and, while inducing robustness against system uncertainties. The findings of this article pave the way for further research into predefined‐time synchronization of multiplicative oscillator systems, which would bring promising implications for data encryption and secure communication. [ABSTRACT FROM AUTHOR]

It is now more crucial than ever to provide data security, as the goals and capabilities of eavesdroppers are constantly changing. Therefore, different developers are creating cipher systems that employ various innovative techniques. Standard ciphers, such as DES and AES, use substitution boxes to ensure secure encryption and decryption of data. The substitution box (S-box) is a core module used in modern ciphers to secure data. This study introduces an efficient and straightforward method that leverages a Residue Number System (RNS) to construct an S-box. Furthermore, the AES algorithm uses the generated S-box to encrypt digital images. Parameters such as entropy, NPCR, and UACI effectively measure the security of the proposed method. The results of the performance and comparison study confirm that the proposed S-box outperforms existing methods, establishing it as a strong candidate for cryptographic use in various image security applications. [ABSTRACT FROM AUTHOR]

Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones. [Display omitted] • Encryption-based data consolidation strategy for 5G in fog computing is presented. • Data consolidation and data extraction algorithm at fog devices and cloud servers. • A comparative analysis based on storage, communication and transmission cost is done. [ABSTRACT FROM AUTHOR]

In this article, we study some new properties of twin primes and algorithms for their generation. We find the necessary conditions to generate a pair of twins. These conditions seem to indicate that the conjecture is true, namely, there are infinitely many twin primes. Furthermore, we developed some algorithms that are very useful from a computer science point of view, which can be applied in cryptography and data encryption. [ABSTRACT FROM AUTHOR]

With the tremendous and rapid evolution taking place in the field of technology and considering the rise in data volume that is dealt with daily, managing this data, whether in terms of security or terms of storage especially if the data is huge, is considered a necessary issue. Therefore, in this research, we established a framework that provides both security and storage/repository management. Where the security issue in the suggested framework is supported by the use of lightweight hash functions and public-key encryption algorithms represented by SPONGENT and elliptic curve cryptography (ECC). Additionally, the fast random number generator is also used to support the security algorithms included in the framework, while managing the repository storage is controlled by the use of a hybrid Blockchain to manage storage for this type of big data. The process of storing this data in databases or any of the traditional centralized methods exposes the data to loss or penetration. After analyzing the proposed framework, it successfully addressed the prevention of malicious within the field of proposed research attacks. Moreover, the performance analysis of the framework proposed was quite effective with the lightweight SPONGENT and ECC results, while the creation of blocks in the storage phase was not more than 0.18 ns. Thus, we obtained an effective framework in terms of security, performance, and terms of data repository management and control. [ABSTRACT FROM AUTHOR]

The widespread availability of large language models (LLMs) has encouraged many individuals to explore chatbot development for their business needs. However, creating chatbots for handling sensitive information, like in healthcare, can be challenging. Mistakes made by these bots when extracting information or providing health recommendations can have serious consequences. When developing a chatbot for pharmacy recommendations, it is essential for the bot to effectively extract symptom-related information and other relevant patient data and then offer recommendations for actions or medications based on that information. In this study, we proposed a straight forward and effective approach that combines regular expression templates for information extraction with forward chaining for inference to create a pharmacy recommendation chatbot called SmartFarma. In scenarios like pharmacy recommendations, we will demonstrate that the use of regular expression templates is sufficient and produces better results than some machine learning methods. Additionally, by using regular expressions, SmartFarma can be developed with transparent data handling, allowing experts to trace, monitor, and evaluate its recommendations. This research primarily focuses on the extraction of patient information. Our model, as proposed, achieved an impressive score of 81.54%, outperforming both the Biomedical Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) models. [ABSTRACT FROM AUTHOR]

An eighteenth-dimensional (18D) memristive chaotic generator characterized by grid multi-double-scroll (GMDS) attractors is proposed. The chaos has the highest chaos dimension reported in the literature for image encryption purposes. The chaotic sequences are used to assist the operation of proposed hybrid digital/optical encryption and decryption systems for multiple color images. The encryption system consists of a digital encryption subsystem (DEsS) cascaded with an optical encryption subsystem (OEsS) which are controlled by six and twelve chaotic sequences, respectively. The DEsS is implemented using four sequential stages (compressive sensing, scrambling, DNA encoding, and diffusion). The OEsS works parallelly on the RGB components of the digital encrypted image and implemented using double-chaotic phase encoding in Fourier, fractional Fourier, and gyrator optical transforms domains, respectively. All the used methods work interactively to increase the security level and robustness of the proposed encryption system. The simulation results show that the proposed encryption system works very satisfactory even with a 64-image input (plain) template. The entropy factor reaches 7.9997 and 7.9998 for encrypted single and multiple color images, respectively. The key space of the proposed system is 22551 which is the highest among other image encryption systems reported in the literature. The simulation is performed using MATLAB Ver.2022a and applied to images taken from Linnaeus 5 dataset. [ABSTRACT FROM AUTHOR]

Various mobile devices and applications are now used in daily life. These devices require high-speed data processing, low energy consumption, low communication latency, and secure data transmission, especially in 5G and 6G mobile networks. High-security cryptography guarantees that essential data can be transmitted securely; however, it increases energy consumption and reduces data processing speed. Therefore, this study proposes a low-energy data encryption (LEDE) algorithm based on the Advanced Encryption Standard (AES) for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things (IoT) devices. In the proposed LEDE algorithm, the system time parameter is employed to create a dynamic S-Box to replace the static S-Box of AES. Tests indicated that six-round LEDE encryption achieves the same security level as 10-round conventional AES encryption. This reduction in encryption time results in the LEDE algorithm having a 67.4% lower energy consumption and 43.9% shorter encryption time than conventional AES; thus, the proposed LEDE algorithm can improve the performance and the energy consumption of IoT edge devices. [ABSTRACT FROM AUTHOR]

Wearable technology has seen rapid advancement, yet the integration of responsive materials into wearable devices poses significant challenges, particularly in maintaining fabric integrity and user comfort while ensuring sensitivity and responsiveness to environmental stimuli. In this work, these challenges are addressed by developing an ultra‐stable hydrochromic fabric that exhibits both hydro‐ and halochromic responsiveness. Utilizing a bimolecular fluoran dye system composed of a black leuco dye (ODB‐2) and a weak acid developer (benzyl 4‐hydroxybenzoate, B4H), these materials are embedded into a robust fibrous matrix constructed through an electrospinning process with thermoplastic polyurethane (TPU). This approach ensures the breathability, flexibility, and structural integrity of the fabrics, while the hydrophobic nature of TPU contributes to the stability and reversibility of the hydro/halochromic properties. The strategy allows for immediate, high‐contrast color changes upon exposure to water and acidic/basic vapors. These fabrics are also applied in rewritable data encryption, demonstrating their potential in anti‐counterfeiting. Furthermore, the investigation into the mechanical properties of these fabrics confirms their durability and resilience, making them ideal for wearable technology. [ABSTRACT FROM AUTHOR]

In today's globalized agricultural system, information leakage of agricultural biological risk factors can lead to business risks and public panic, jeopardizing corporate reputation. To solve the above problems, this study constructs a blockchain network for agricultural product biological risk traceability based on agricultural product biological risk factor data to achieve traceability of biological risk traceability data of agricultural product supply chain to meet the sustainability challenges. To guarantee the secure and flexible sharing of agricultural product biological risk privacy information and limit the scope of privacy information dissemination, the blockchain-based proxy re-encryption access control method (BBPR-AC) is designed. Aiming at the problems of proxy re-encryption technology, such as the third-party agent being prone to evil, the authorization judgment being cumbersome, and the authorization process not automated, we design the proxy re-encryption access control mechanism based on the traceability of agricultural products' biological risk factors. Designing an attribute-based access control (ABAC) mechanism based on the traceability blockchain for agricultural products involves defining the attributes of each link in the agricultural supply chain, formulating policies, and evaluating and executing these policies, deployed in the blockchain system in the form of smart contracts. This approach achieves decentralization of authorization and automation of authority judgment. By analyzing the data characteristics within the agricultural product supply chain to avoid the malicious behavior of third-party agents, the decentralized blockchain system acts as a trusted third-party agent, and the proxy re-encryption is combined with symmetric encryption to improve the encryption efficiency. This ensures a efficient encryption process, making the system safe, transparent, and efficient. Finally, a prototype blockchain system for traceability of agricultural biological risk factors is built based on Hyperledger Fabric to verify this research method's reliability, security, and efficiency. The experimental results show that this research scheme's initial encryption, re-encryption, and decryption sessions exhibit lower computational overheads than traditional encryption methods. When the number of policies and the number of requests in the access control session is 100, the policy query latency is less than 400 ms, the request-response latency is slightly more than 360ms, and the data uploading throughput is 48.7 tx/s. The data query throughput is 81.8 tx/s, the system performance consumption is low and can meet the biological risk privacy protection needs of the agricultural supply chain. The BBPR-AC method proposed in this study provides ideas for achieving refined traceability management in the agricultural supply chain and promoting digital transformation in the agricultural industry. [ABSTRACT FROM AUTHOR]

Accelerated growth in networking and semiconductor devices led to the increase of Internet-connected devices over recent years. The applications of IoT provide pervasive admittance, scalability, and distribution of data using the cloud for the storage and processing of data. All the actors involved in the grid affect the trust. Managing security and deploying it throughout the IoT environment is required to audit and protect sensitive data. Security management involves authentication to avoid unauthorized entry and encryption at transmission. Though there are standard methodologies employed in data protection in the cloud and network layers, there is a requirement for solutions with low foot-prints suitable for constrained IoT devices. While there are existing lightweight cipher designs performing well with less memory footprint, the performance in the cloud is less compared to traditional cipher techniques. So, a balanced solution with less memory footprint and resistance against various attacks in the cloud environment is required. A lightweight cipher design is proposed to enable secure communication among devices through the cloud, which is combined with Elliptic Light (ELLI) and an Authentication Function (AF) is proposed using RC4 to ensure integrity and authentication and exchange secret key. The cipher design proposed provides increased efficiency and fixes the weakness in standard lightweight algorithms. This algorithm is simple and efficient, with 60% less memory utilization and 53% less execution time than other lightweight solutions. The stability is high in linear and differential cryptanalysis and resistive against DDoS attacks. [ABSTRACT FROM AUTHOR]

Security of modern classical data encryption often relies on computationally hard problems, which can be trivialized with the advent of quantum computers. A potential remedy for this is quantum communication which takes advantage of the laws of quantum physics to provide secure exchange of information. Here, quantum key distribution (QKD) represents a powerful tool, allowing for unconditionally secure quantum communication between remote parties. At the same time, microwave quantum communication is set to play an important role in future quantum networks because of its natural frequency compatibility with superconducting quantum processors and modern near-distance communication standards. To this end, we present an experimental realization of a continuous-variable QKD protocol based on propagating displaced squeezed microwave states. We use superconducting parametric devices for generation and single-shot quadrature detection of these states. We demonstrate unconditional security in our experimental microwave QKD setting. The security performance is shown to be improved by adding finite trusted noise on the preparation side. Our results indicate feasibility of secure microwave quantum communication with the currently available technology in both open-air (up to ~ 80 m) and cryogenic (over 1000 m) conditions. Microwave-based quantum key distribution would allow a secure exchange of information in superconducting local area networks and, potentially beyond, in open-air conditions. Here, the authors provide a proof-of-principle implementation within a cryogenic environment, based on propagating displaced squeezed microwave states. [ABSTRACT FROM AUTHOR]

An AG-group is an algebraic structure that satisfies the left invertive law and includes its left identity and inverse elements. This framework is naturally suited for representing and manipulating various real-life applications in civil engineering, information engineering, artificial intelligence, and more. In this paper, we present the structural application of AG-groups in data encryption and in the rotational transformation of water molecules. Additionally, numerous authors have explored fuzzy set theory over AG-group systems, yielding many valuable results. Spherical fuzzy set theory, an extension of fuzzy set theory, allows for the representation of uncertainty in decision-making, where there may be multiple possible choices or uncertainty about the degree of membership of an element in a set. However, no prior work has combined spherical fuzzy set theory with AG-groups, which motivates our study. In this paper, we apply spherical fuzzy set theory to AG-groups and introduce the concept of spherical fuzzy AG-subgroupoids. We provide the characterization of spherical fuzzy AG-subgroupoids over AG-groupoids and examine their related properties. As an application of spherical fuzzy sets over AG-groups, we develop the concept of normal spherical fuzzy groups over AG-groups and investigate their various properties. [ABSTRACT FROM AUTHOR]

Chiral self‐discrimination plays a critical role in supramolecular chemistry and materials science. However, an ideal strategy for achieving chiral self‐discrimination remains elusive due to the inevitable nonspecific binding of incorrect enantiomers, and insufficient intrinsic optical activity of chiral molecules. Herein, a novel 1,1′‐binaphthol (BINOL) derivative with an imide group fused at the peri‐position of one naphthol scaffold is developed, which combines the dual functionalities of aggregation‐induced emission characteristic of BINOLs, and high emission of 1,8‐naphthalimides. The multiple molecular recognition between two hydroxyl groups in BINOL units and two carbonyl groups in 1,8‐naphthalimide moieties endows the precise chiral self‐discrimination behaviors. As expected, the homochiral aggregates exhibit reversible phase transitions, switching from non‐emission to bright green emission upon absorption and desorption of methanol vapor. In contrast, the heterochiral conglomerates exhibit irreversible yellow emission changes due to the impact of chiral self‐discrimination. Such chiral self‐discrimination‐induced luminescence vapochromism can be further applied to high‐level anti‐counterfeiting and data encryption. This work provides a new perspective on smart chiral organic materials based on chiral self‐discrimination. [ABSTRACT FROM AUTHOR]

At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security. [ABSTRACT FROM AUTHOR]

Nowadays, man-in-the-middle (MITM) attacks have become a large problem due to the advancement of computational power and interactive mobile technologies. Message security is a crucial concern that ought to be managed in order to help protect vital data from unauthorized people, such as MITM. Steganography is the technique of hiding secret data within an ordinary, non-secret file or message in order to avoid detection when communicating through an unsecured network. Steganography applications play a vital role in various fields that involve classified data transfer, such as healthcare, multimedia, and the military. Hence, the application of steganography in those fields attracts MITM attacks. Thus, in an attempt to address the challenge. This study proposes a hybrid approach that integrates an image steganography technique, the Advanced Encryption Standard (AES) for message encryption, and EdDSA (Edward-Curve Digital Signature Algorithm) for signature verification to enhance steganography against MITM attacks. The proposed hybrid approach was tested and measured using image metrics (MSE, PSNR, and SSIM) and histogram visualization and verified through experimentation. The results have proven that the proposed hybrid approach is an enhanced security approach with low execution time, more payload size for hiding messages, and a high invisibility embedded message to MITM compared with other existing approaches. This study has potential limitations. It does not explore tamper resistance or algorithm robustness, and it was not tested on a public image dataset. [ABSTRACT FROM AUTHOR]

Systems for data storage and communication must be secure, and encryption algorithms are essential to this. In this work, the Rivest-Shamir-Adleman (RSA) algorithm and the Advanced Encryption Standard (AES) method are compared. We present a comprehensive comparison of AES and RSA encryption algorithms based on their mathematical principles, security features, performance characteristics, and practical considerations. We also discuss their strengths and limitations in various scenarios, offering insightful information to information security practitioners and decision-makers. By analyzing and contrasting the key aspects of AES and RSA, we aim to contribute to the understanding of these widely used encryption algorithms and assist in selecting the appropriate algorithm for specific security requirements. We discuss the mathematical and arithmetic comparison between these two algorithms, and evaluate their performance in terms of security, speed, and implementation complexity. Our analysis shows that while AES provides better performance for symmetric key encryption, RSA offers a secure mechanism for asymmetric key encryption. We also stress how crucial it is to choose the right encryption algorithm depending on the particular needs of the application. [ABSTRACT FROM AUTHOR]

Healthcare institutions generate massive amounts of valuable patient data in the digital age. Finding the right balance between patient privacy and the demand for data-driven medical enhancements is essential. Since data privacy has become increasingly important, robust technologies must be developed to safeguard private data and allow meaningful exploration. This issue was addressed by ShinyAnonymizer, which was first created to anonymize health data. It achieves this by rendering anonymization methods easily available to users. The enhanced version of ShinyAnonymizer, with an essential improvement in performance, is presented in this study. We explain the merging of data analysis, visualization, and privacy-focused statistics paradigms with data anonymization, hashing, and encryption, offering researchers and data analysts an extensive collection of tools for trustworthy data management. [ABSTRACT FROM AUTHOR]

Medical records are transmitted between medical institutions using cloud-based Electronic health record (EHR) systems, which are intended to improve various medical services. Due to the potential of data breaches and the resultant loss of patient data, medical organizations find it challenging to employ cloud-based electronic medical record systems. EHR systems frequently necessitate high transmission costs, energy use, and time loss for physicians and patients. Furthermore, EHR security is a critical concern that jeopardizes patient privacy. Compared to a single system, cloud-based EHR solutions may bring extra security concerns as the system architecture gets more intricate. Access control strategies and the development of efficient security mechanisms for cloud-based EHR data are critical. For privacy reasons, the Dynamic constrained message authentication (DCMA) technique is used in the proposed system to encrypt the outsourced medical data by using symmetric key cryptography, which uses the Seagull optimization algorithm (SOA) to choose the best random keys for encryption and then resultant data is hashed using the SHA-256 technique. The results of the proposed model are evaluated using performance metrics, and the model attained a security of about 98.58%, which is proven to be superior because it adopts advanced random secret key generation, which adds more security to the system. [ABSTRACT FROM AUTHOR]

With the exponential growth of the Internet of Things (IoT), ensuring robust end-to-end encryption is paramount. Current cryptographic accelerators often struggle with balancing security, area efficiency, and power consumption, which are critical for compact IoT devices and system-on-chips (SoCs). This work presents a novel approach to designing substitution boxes (S-boxes) for Advanced Encryption Standard (AES) encryption, leveraging dual quad-bit structures to enhance cryptographic security and hardware efficiency. By utilizing Algebraic Normal Forms (ANFs) and Walsh–Hadamard Transforms, the proposed Register Transfer Level (RTL) circuitry ensures optimal non-linearity, low differential uniformity, and bijectiveness, making it a robust and efficient solution for ASIC implementations. Implemented on 65 nm CMOS technology, our design undergoes rigorous statistical analysis to validate its security strength, followed by hardware implementation and functional verification on a ZedBoard. Leveraging Cadence EDA tools, the ASIC implementation achieves a central circuit area of approximately 199 μm2. The design incurs a hardware cost of roughly 80 gate equivalents and exhibits a maximum path delay of 0.38 ns. Power dissipation is measured at approximately 28.622 μW with a supply voltage of 0.72 V. According to the ASIC implementation on the TSMC 65 nm process, the proposed design achieves the best area efficiency, approximately 66.46% better than state-of-the-art designs. [ABSTRACT FROM AUTHOR]

Privacy-enhancing techniques and protocols for data aggregation and analytics in wireless networks require the development of novel methods for efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active attackers. Previous approaches to secure privacy-preserving computation of distributed queries incur significant communication overhead and cannot be applied to big data. This paper proposes two solutions to the problem of efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active outsider attackers for a new large class of distributed statistical or numerical queries. This class contains many useful statistics and is larger than other classes considered in the literature previously. We propose two protocols for the Protection of data from Active Attackers (PAA) in the case of distributed privacy-preserving computation: PAA applying Shamir's Secret Sharing (PAA-SSS) and PAA applying homomorphic encryption (PAA-HE). The PAA-HE protocol combines the use of ElGamal and Paillier encryption schemes in one system. Theoretical analysis and experimental results show that both protocols outperform alternative approaches. PAA-HE provides stronger protection and is more efficient than PAA-SSS. [ABSTRACT FROM AUTHOR]

The theory of quadratic residues plays an important role in cryptography. In 2001, Cocks developed an identity-based encryption (IBE) scheme based on quadratic residues, resolving Shamir's 17-year-old open problem. However, a notable drawback of Cocks' scheme is the significant expansion of the ciphertext, and some of its limitations have been addressed in subsequent research. Recently, Cotan and Teşeleanu highlighted that previous studies on Cocks' scheme relied on a trial-and-error method based on Jacobi symbols to generate the necessary parameters for the encryption process. They enhanced the encryption speed of Cocks' scheme by eliminating this trial-and-error method. Based on security analysis, this study concludes that the security of Cotan-Teşeleanu's proposal cannot be directly derived from the security of the original Cocks' scheme. Furthermore, by adopting the Cotan-Teşeleanu method and introducing an additional variable as a public element, this study develops a similar enhancement scheme that not only accelerates the encryption speed but also provides security equivalent to the original Cocks' scheme. [ABSTRACT FROM AUTHOR]

A cryptography algorithm for data transfer encryption provides confidentiality, requires considerable computing power, and is not commonly implemented in embedded systems with limited computing power, such as Programmable Logic Controller (PLC). PLC is the core component for automation and control in industrial automation. For decades, PLC has prioritized speed over security; program execution in PLC must be as efficient as possible. The cryptography algorithm uses a seed, the initialization vector, to encrypt the data with the cryptography key to strengthen the encryption. Pseudo Random Number Generator (PRNG) can be used as the initialization vector. This paper proposes the XORasm PRNG algorithm, the lightweight XORshift-based algorithm with a modified seed from the system's clock. The applied methodology generates and visualizes PRNG, tests the randomness, and implements the PRNG on compact PLC. XORasm is evaluated statistically with runs-test in simulation by comparing the algorithm to one of the simulated compact PLC's PRNG implementations. The findings from this research are that p-values demonstrate that XORasm is statistically and significantly more random than the current implementation, and there is evidence that XORasm's generated data distribution is practically random at a 99.95% confidence level, suitable for implementation in embedded systems as a lightweight PRNG. [ABSTRACT FROM AUTHOR]

Crypto ransomware attacks pose a significant threat by encrypting users' data and demanding ransom payments, causing permanent data loss if not detected and mitigated before encryption occurs. The existing studies have faced challenges in the pre-encryption phase due to elusive attack patterns, insufficient data, and the lack of comprehensive information, often confusing the current detection techniques. Selecting appropriate features that effectively indicate an impending ransomware attack is a critical challenge. This research addresses this challenge by introducing an Enhanced Joint Mutual Information (EJMI) method that effectively assigns weights and ranks features based on their relevance while conducting contextual data analysis. The EJMI method employs a dual ranking system—TF for crypto APIs and TF-IDF for non-crypto APIs—to enhance the detection process and select the most significant features for training various Machine Learning (ML) classifiers. Furthermore, grid search is utilized for optimal classifier parameterization, aiming to detect ransomware efficiently and accurately in its pre-encryption phase. The proposed EJMI method has demonstrated a 4% improvement in detection accuracy compared to previous methods, highlighting its effectiveness in identifying and preventing crypto-ransomware attacks before data encryption occurs. [ABSTRACT FROM AUTHOR]

Context: Organizations constantly strive to protect their assets from outsider attacks by implementing various security controls, such as data encryption algorithms, intrusion detection software, firewalls, and antivirus programs. Unfortunately, attackers strike not only from outside the organization but also from within. Such internal attacks are called insider attacks or threats, and the people responsible for them are insider attackers or insider threat agents. Insider attacks pose more significant risks and can result in greater organizational losses than outsider attacks. Thus, every organization should be vigilant regarding such attackers to protect its valuable resources from harm. Finding solutions to protect organizations from such attacks is critical. Despite the importance of this topic, little research has been conducted on providing solutions to mitigate insider attacks. Objective: This study aims to develop an organizational readiness model to assess an organization's readiness for insider attacks. Method: We conducted a multivocal literature review to identify practices that can be used to assess organizations' readiness against insider attacks. These practices were grouped into different knowledge areas of insider attacks for organizations. The insider attack readiness model was developed using identified best practices and knowledge areas: compliance, top management, human resources, and technical. Results: This model was evaluated at two levels—academic and real‐world environments. The evaluation results show that the proposed model can identify organizations' readiness against insider attacks. Conclusion: The proposed model can guide organizations through a secure environment against insider attacks. [ABSTRACT FROM AUTHOR]

The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing with hierarchical threshold encryption, offering a flexible and efficient privacy protection solution that significantly enhances data security in the smart grid. The methodology involves setting user protection levels by processing missing data and utilizing fuzzy comprehensive analysis to evaluate user importance, thereby assigning appropriate protection levels. Furthermore, a hierarchical threshold encryption algorithm is developed to provide differentiated protection strategies for fog nodes based on user IDs, ensuring secure aggregation and encryption of user data. Experimental results demonstrate that HPPM-AMICFA effectively resists various attack strategies while minimizing time costs, thereby safeguarding user data in the smart grid. [ABSTRACT FROM AUTHOR]

In today's world, encryption is crucial for protecting sensitive data. Neural networks can provide security against adversarial attacks, but meticulous training and vulnerability analysis are required to ensure their effectiveness. Hence, this research explores hybrid encryption based on a generative adversarial network (GAN) for improved message encryption. A neural network was trained using the GAN method to defend against adversarial attacks. Various GAN training parameters were tested to identify the best model system, and various models were evaluated concerning their accuracy against different configurations. Neural network models were developed for Alice, Bob, and Eve using random datasets and encryption. The models were trained adversarially using the GAN to find optimal parameters, and their performance was analyzed by studying Bob's and Eve's accuracy and bits error. The parameters of 8,000 epochs, a batch size of 4,096, and a learning rate of 0.0008 resulted in 100% accuracy for Bob and 52.14% accuracy for Eve. This implies that Alice and Bob's neural network effectively secured the messages from Eve's neural network. The findings highlight the advantages of employing neural network-based encryption methods, providing valuable insights for advancing the field of secure communication and data protection. [ABSTRACT FROM AUTHOR]

In the dynamic field of cryptography, lightweight ciphers play a pivotal role in overcoming resource constraints in modern applications. This paper introduces a lightweight cryptographic algorithm by seamlessly merging the proven characteristics of the Feistel cipher CLEFIA with the advanced substitution-permutation network (SPN) framework of RECTANGLE for key generation. The algorithm incorporates a specially optimized feather S-box, balancing efficiency and security in both CLEFIA and RECTANGLE components. The RECTANGLE key generation, vital for the proposed lightweight technique, enhances overall cryptographic security and efficiency. Meticulous consideration of resource limitations maintains the algorithm's lightweight nature, making it well-suited for applications with restricted computational resources. To validate the efficacy of the lightweight algorithm, extensive evaluation on encrypted data is conducted using National Institute of Standards and Technology (NIST) tools, known for assessing cryptographic algorithm quality. Results reveal a high degree of randomness, indicative of robust resistance against cryptographic attacks. This manuscript provides a comprehensive examination of the lightweight algorithm, emphasizing key attributes, security enhancements, and successful integration of the optimized feather S-box. Rigorous testing, particularly NIST tool-based randomness analysis, offers empirical evidence of the algorithm's resilience against attacks, establishing its suitability for secure data encryption in resource-limited environments. [ABSTRACT FROM AUTHOR]

This study proposes a blockchain-based system that utilizes fully homomorphic encryption to provide data security and statistical privacy when data are shared with third parties for analysis or research purposes. The proposed system not only provides security of data in transit, at rest, and in use but also assures privacy and computational integrity for simple statistical computations. This is achieved by leveraging the attributes of the blockchain technology, which provides availability and data integrity, combined with homomorphic encryption, which provides confidentiality of data in use. The computations are performed on smart contracts residing on the blockchain, providing computational integrity. The proposed system is implemented on the Zama blockchain and performs statistical operations including mean, median, and variance on encrypted data. The results indicate that it is possible to perform fully homomorphic computations on the blockchain. Even though current computing limitations on the blockchain do not allow running the system for large data sets, the technology is available, and with advancements toward more efficient homomorphic operations on blockchains, the proposed system will provide an ultimate solution for providing the much-desired security properties in applications, including data and statistical privacy, confidentiality, and integrity at rest, in transit, and in use. [ABSTRACT FROM AUTHOR]

Homomorphic encryption performs computations on encrypted data without decrypting, thereby eliminating security issues during the data communication between clouds and edges. As a result, there is a growing need for homomorphic encryption hardware (HE-HW) for the edges, where low power consumption and a compact form factor are desired. Here, a Pt/Ta2O5/Mo metallic cluster-type memristors (Mo-MCM) characterized by the Mo as a mobile species, and its utilization for the HE-HW via a 1-trasistor-1-memristor (1T1M) array as a prototype HE-HW is proposed. The Mo-MCM exhibits inherent stochastic set-switching behavior, which can be utilized for generating the random numbers required for encryption key generation. Furthermore, the device can accurately store analog conductance states after set-switching, which can be used as an analog non-volatile memristor. By simultaneously leveraging these two characteristics, encryption key generation, data encryption, and decryption are possible within a single device through an in-memory computing manner. Homomorphic encryption contributes to eliminating security issues during the data communication for edge computing. Here, the authors propose a 1-trasistor−1-memristor array as a homomorphic encryption hardware prototype. By leveraging the stochastic set-switching behavior and analog non-volatile properties of the memristor, the complete homomorphic communication process is demonstrated. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSWith the development of geographic information services, applications based on map data are increasing. Internet and mobile communication technologies have brought great convenience to the distribution, sharing, and acquisition of map data. Unfortunately, it also brings great challenges to protect the security and privacy of confidential map data. Although several encryption algorithms have been developed for map data, most of the existing methods are simple extensions of classical encryption algorithms for text data in cryptography, ignoring special structures and characteristics of geospatial data. Existing methods mainly take the entire map or layers as encryption units, which makes the encryption efficiency for large-scale map data low and limits the ability to perform local and incremental encryption for local areas, making it still challenging to meet the needs of map data in applications, such as autonomous driving. To solve the limitations, a local encryption approach for large-scale vector map data is proposed in this paper considering the structure characteristics of map data. Experiments on map data of different types (including points, lines, and polygons) demonstrate the effectiveness of the proposed method. With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.A local encryption approach for vector map data is proposed, which can be applied to large-scale vector map data and support localized encryption/decryption for user-interested areas instead of the whole map data.The proposed method estimates different encryption parameters for different units which could significantly enhance the resistance to attacks and naturally supports parallel computing.With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.The method has great value and potential for ensuring map data security in ubiquitous GIS-based applications, such as map data sharing and dynamic updating in automatic driving and crowdsourced mapping. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Dynamic fluorescent materials play a crucial role in secure inks for data encryption; however, they are still plagued by issues such as photodegradation, poor latency, and susceptibility to unauthorized access. Herein, we propose a photochemically modulated dynamic fluorescent encryption system based on 1O2 sensitization of fluorescent composites, comprising a 1O2‐sensitive fluorophore (F2) and non‐emissive polymers. After UV irradiation, in‐situ generated 1O2 from the polymer effectively binds with F2 to form endoperoxides (F2EPO), resulting in a significant redshift in emission, up to 150 nm. The 1O2 concentration is closely related to the irradiation time, rendering different fluorescent colors in a time‐gated fashion. Moreover, the emission of F2EPO can be regulated by polymer chemical structure, molecular weight, and crosslinking density. Relying on these merits, we develop a dynamic data encryption method with various non‐emissive polymers as the data storage media, UV light irradiation as the data encoder, and F2 as the data decoder. UV light irradiation of diverse polymer solutions generates 1O2 at different concentrations, effectively encoding the data, which remains invisible under both UV and natural lights. The addition of F2 to these irradiated polymer solutions produces different redshifted fluorescence, enabling secure data decryption. Attributing to the non‐emissive nature of the polymers, time‐gated readout fashion, excellent latency of 1O2, and subtle interactions between 1O2 and F2, this data encryption is nearly undecipherable. This work offers an advantage data encryption approach beyond the reach of conventional fluorophores. [ABSTRACT FROM AUTHOR]

Over the years, there has been a significant surge in the popularity of instant messaging applications (IMAs). However, the message notification functionality in IMAs exhibits certain limitations. Some IMAs fail to alert users about new messages after their phone restarts unless they unlock the phone. This is a consequence of end-to-end encryption (E2EE) and the app not knowing the message is in the queue until the app decrypts it. This approach using E2EE is used to prevent offline attacks, as the key is unavailable to decrypt the notification messages. In this paper, we introduce a novel design and implementation of a message notification protocol for IMAs based on the Signal protocol. The proposed protocol aims to securely display notifications on a locked device and ensures that cryptographic keys are stored in a location that is isolated from the user's device to prevent offline attacks. This approach enhances the security of private key storage, safeguarding private keys against various external threats. The innovative design strengthens the off-site key management system, rendering it resilient against offline attacks and mitigating the risk of key compromise. Additionally, the proposed protocol is highly efficient, requiring no specialized hardware for implementation. It offers confidentiality of cryptographic keys and protection against offline attacks, further enhancing the overall security of the system. We evaluate the protocol's effectiveness by analyzing multiple independent implementations that pass a suite of formal tests via ProVerif. [ABSTRACT FROM AUTHOR]

Traditional anti‐counterfeiting luminescent phosphors are usually composed of single‐mode photoluminescence materials, which greatly limits the security of encryption by its static fluorescent pattern. Herein, multi‐mode luminescence properties are achieved including fluorescence and persistent luminescence (PersL) within single host by co‐doping Bi3+ and Eu3+ in CaNaSb2O6F (CNSOF). A tunable emission is observed from blue (Bi3+, 3P1→1S0) to white and then to orange‐yellow (Eu3+, 5D1→7F2 and 5D0→7F0, 1, 2, 3, 4) as the excitation schemes, environment temperatures and doping level are modulated. Impressively, the blue emission attributed to the Bi3+ rapidly disappears after stopping the excitation light irradiation, and only the intense orange‐yellow PersL produced by Eu3+ can be observed. And a possible model for the energy transfer and PersL mechanism is proposed by the investigation crystal structure and photoluminescence/PersL. A schematic of security logo and digital information encryption is demonstrated using the prepared samples, which shows the dynamic evolution of the emission color and PersL brightness. The excellent property of multiple color outputs, different decay processes, and external field stimulation modes (including low energy light, thermal, and mechanical stimuli) present in CNSOF:Bi3+, Eu3+ provides a fast, low‐cost, and effective method for advanced anti‐counterfeiting and information encryption applications. [ABSTRACT FROM AUTHOR]

Supply chain management (SCM) is most significant place of concentration in various corporate circumstances. SCM has both designed and monitored numerous tasks with the following phases such as allocation, creation, product sourcing, and warehousing. Based on this perspective, the privacy of data flow is more important among producers, suppliers, and customers to ensure the responsibility of the market. This work aims to develop a novel Improved Digital Navigator Assessment (DNA)-based Self Improved Pelican Optimization Algorithm (IDNA-based SIPOA model) for secured data transmission in SCM via blockchain. An improved DNA cryptosystem is done for the process of preservation for data. The original message is encrypted by Improved Advanced Encryption Standard (IAES). The optimal key generation is done by the proposed SIPOA algorithm. The efficiency of the adopted model has been analyzed with conventional methods with regard to security for secured data exchange in SCM. The proposed IDNA-based SIPOA obtained the lowest value for the 40% cypher text is 0.71, while the BWO is 0.79, DOA is 0.77, TWOA is 0.84, BOA is 0.83, POA is 0.86, SDSM is 0.88, DNASF is 0.82 and FSA-SLnO is 0.78, respectively. [ABSTRACT FROM AUTHOR]

Now a days, the Internet of Things (IoT) plays a vital role in every industry including agriculture due to its widespread and easy integrations. The agricultural methods are incorporated with IoT technologies for significant growth in agricultural fields. IoT is utilized to support farmers in using their resources effectively and support decision-making systems with better field monitoring techniques. The data collected from IoT-based agricultural systems are highly vulnerable to attack, hence to address this issue it is necessary to employ an authentication scheme. In this paper, Auth Key_Deep Convolutional Neural Network (Auth Key_DCNN) is designed to promote secure data sharing in IoT-enabled agriculture systems. The different entities, namely sensors, Private Key Generator (PKG), controller, and data user are initially considered and the parameters are randomly initialized. The entities are registered and by using DCNN a secret key is generated in PKG. The encryption of transmitted data is performed in the data protection phase during the protection of data between the controller and the user. Additionally, the performance of the designed model is estimated, where the experimental results revealed that the Auth Key_DCNN model recorded superior performance with a minimal computational cost of 142.56, a memory usage of 49.5 MB, and a computational time of 1.34 sec. [ABSTRACT FROM AUTHOR]

Edge computing serves as a critical intermediary for secure data exchange between IoT devices and data centres in healthcare, where the protection of sensitive patient data is paramount. This study presents UdesMec, a comprehensive edge computing framework designed for efficient and secure data storage and processing in such contexts. Focusing on the challenge of implementing robust data security models in the presence of limited resources at edge nodes, UdesMec employs a unified encryption approach that is particularly suited for expert knowledge-based prediction of complex critical medical events. Using secret sharing and homomorphic encryption, it ensures the confidentiality and integrity of sensitive patient data transmitted by IoT devices, while enabling efficient computation of prediction algorithms on a cloud server. The experimental evaluation confirms the strong encryption performance and the ease of use of UdesMec, positioning it as a promising solution for the secure and reliable prediction of complex critical medical events in healthcare systems based on edge computing. [ABSTRACT FROM AUTHOR]

The secure transmission of medical images and related digital patient records on untrusted channels has recently become a focus in healthcare industries. Data hiding and encryption are important tools for this goal. This paper proposes a data-hiding method for medical images in the context of invisibility, robustness, security and low time cost. A dual watermarking is introduced to accomplish chaos-based encryption to ensure medical images' copyright protection and content security. First, a local binary pattern based on neighbouring pixels is used to compute an optimal value, called an embedding factor, for embedding both marks. Second, the host medical image is marked using the lifting wavelet transform, the lower–upper (LU) decomposition and singular value decomposition with an embedding factor to protect ownership. Last, the marked image is encrypted by using a 3D-chaotic map. The method is tested on two standard datasets, which is convenient for medical applications. Our experimental results and performance analysis demonstrate that the proposed scheme produces a peak signal-to-noise ratio (PSNR) and NC wat 1 / NC wat 2 of 54.82 dB and 0.9916/0.9928, respectively. Furthermore, the key space analysis of our encryption technique is greater than 2 100 , which provides a high protection degree against brute-force attacks. The proposed scheme achieves better robustness, imperceptibility, and security performance than previous schemes. Our method can reduce time costs by 98% (91% on average), which results in lower time costs than other conventional encryption techniques. [ABSTRACT FROM AUTHOR]

• A new optical image encryption and authentication scheme is proposed with the computational ghost imaging. • The problem of inherent linear defect and key management in computational ghost imaging encryption system is solved. • The dual-channel encryption method provides multi-layer protection for the plaintext images. Considering the necessity of identity authentication in most scenarios, a dual-channel (digital channel and optical channel) image encryption and authentication algorithm with computational ghost imaging (CGI) is proposed. The entire encryption process is governed by the pseudo-random sequences obtained by the 4D chaotic system and the Logistic map. During the digital channel encryption, the confusion and diffusion of the plaintext image are achieved by random scrambling and DNA computing, respectively. To alleviate the burden of key transmission and storage in the CGI encryption system, the speckle measurement matrices are constructed with the pseudo-random numbers obtained by the Logistic map. Additionally, a nonlinear encryption operation is carried out to compensate the linear vulnerability of CGI encryption. In the optical encryption channel, the authentication image is first encrypted with the CGI method, followed by the secondary encryption of the intensity values recorded by a bucket detector to produce an intermediate ciphertext. The final encryption image is generated by merging the intermediate ciphertexts from both encryption channels. It is demonstrated that the proposed optical image encryption and authentication scheme with CGI provides satisfactory encryption and decryption effects and can effectively counteract the common attacks. Moreover, the scheme holds promising application prospects due to its authentication capability. [ABSTRACT FROM AUTHOR]

Ensuring data confidentiality is a critical requirement for modern security systems globally. Despite the implementation of various access-control policies to enhance system security, significant threats persist due to insecure and inadequate access management. To address this, Multi-Party Authorization (MPA) systems employ multiple authorities for authorization and authentication, utilizing blockchain technology to store and access data securely, ensuring immutable and trusted audit trails. In this work, we propose a hybrid key-generation approach called the Identity and Attribute-Based Honey Encryption (IABHE) Algorithm combined with Deep Spiking Neural Network (DSNN) denoted by IABHE+DSNN for secure data sharing in a multi-party blockchain-based system. This approach incorporates various entities and multiple security functionalities to ensure data security. The data-sharing process involves several steps: initialization, authentication, initial registration, data protection, validation, and data sharing. Data protection is executed within the MapReduce framework, with data encryption performed using IABHE and key generation managed by DSNN. Experimental results demonstrate that the proposed IABHE+DSNN approach achieves a decryption time of 10.786 s, an encryption time of 15.765 s, and a key complexity of 0.887, outperforming existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we perform neural cryptanalysis on five block ciphers: Data Encryption Standard (DES), Simplified DES (SDES), Advanced Encryption Standard (AES), Simplified AES (SAES), and SPECK. The block ciphers are investigated on three different deep learning-based attacks, Encryption Emulation (EE), Plaintext Recovery (PR), Key Recovery (KR), and Ciphertext Classification (CC) attacks. The attacks attempt to break the block ciphers in various cases, such as different types of plaintexts (i.e., block-sized bit arrays and texts), different numbers of round functions and quantity of training data, different text encryption methods (i.e., Word-based Text Encryption (WTE) and Sentence-based Text Encryption (STE)), and different deep learning model architectures. As a result, the block ciphers can be vulnerable to EE and PR attacks using a large amount of training data, and STE can improve the strength of the block ciphers, unlike WTE, which shows almost the same classification accuracy as the plaintexts, especially in a CC attack. Moreover, especially in the KR attack, the Recurrent Neural Network (RNN)-based deep learning model shows higher average Bit Accuracy Probability than the fully connected-based deep learning model. Furthermore, the RNN-based deep learning model is more suitable than the transformer-based deep learning model in the CC attack. Besides, when the keys are the same as the plaintexts, the KR attack can perfectly break the block ciphers, even if the plaintexts are randomly generated. Additionally, we identify that DES and SPECK32/64 applying two round functions are more vulnerable than those applying the single round function by performing the KR attack with randomly generated keys and randomly generated single plaintext. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices are an essential part of several aspects of daily life for people. They are utilized in a variety of contexts, including industrial monitoring, environmental sensing, and so on. But, secure communication is the major challenge in the IoT environment. Therefore, a decentralized Blockchain-based Key Management protocol using Levy Flight-Equilibrium Optimization and Self-Attention-based Improved Faster Region-based Convolutional Neural Network (BlkKM) method is proposed to determine stable security in tamper-resistant hardware machine that can protect sensitive secret data in the healthcare field i.e., stored cryptographic keys. The keys are categorized as Key Encryption Keys (KEKs) and Data Encryption Keys (DEKs). The number of the keys is decreased by using Levy Flight- Equilibrium Optimization (LF-EO) as organizing nodes with logical sets. Also, Self-Attention-based Improved Faster Region-based Convolutional Neural Network (SA-based IFRCNN) is used for reordering a set of logical nodes to minimize the number of sets after a node exits the network. Additionally, the system makes use of smart contracts for access control as well as proxy encryption to data encryption. The proposed method is compared with existing techniques to validate the security enhancement performance. The evaluation is performed based on throughput, end-to-end delay, storage overheads, and energy consumption. The experimentation results revealed that the proposed method improved the throughput to 220.52bps and diminished the utilization of energy. A greater degree of memory usage is also decreased by using this technique. [ABSTRACT FROM AUTHOR]

Crowdsourcing has long been an effective way for data collection. Despite numerous research efforts devoted to crowdsourcing security, most crowdsourcing platforms in practice are still storing and processing plaintext data, which is vulnerable to in-house attacks. To fill this gap, we envision a full-fledged crowdsourcing platform with end-to-end encryption (E2EE), where the crowdsourced data remains always encrypted to the platform. Such a design would serve as an in-depth defense strategy against data breaches from both internal and external threats and provide technical means for crowdsourcing service providers to meet various stringent regulatory compliance. To realize this vision, we will discuss the technical requirements and related challenges, including: (1) securing worker profiles in task matching to respect individual privacy, (2) assuring high-quality crowdsourced data to enhance data values, (3) enabling versatile data analytics to uncover data insights, (4) protecting data at the front-end to fully achieve E2EE, and (5) preventing the abuse of E2EE for practical deployment. We will overview the limitations of prior arts in meeting all these requirements and identify potential research directions for the roadmap ahead. [ABSTRACT FROM AUTHOR]

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the Harris Hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class. [ABSTRACT FROM AUTHOR]