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In today's dynamic technology world, cloud computing environments can accomplish effective outcomes using container-based solutions. Cloud resource management and allocation have altered as an outcome of containerized cloud computing systems. It provides scalable and flexible solutions for effective resource utilization and various factors regarding businesses. Task scheduling is very challenging in cloud systems that use containerization because there are many competing factors to consider. This study suggests VBDPA, a unique multicriteria-based task-scheduling algorithm for containerized cloud environments. The algorithm effectively addresses the complex and dynamic nature of cloud computing by utilizing the multi-criteria decision-making method known as VIKOR (VIekriterijumskoKOmpromisnoRangiranje). VBDPA seeks to improve overall system performance, reduce task completion times, maximize resource utilization, and provide better revenue for cloud providers. An analysis is carried out to compare VBDPA's effectiveness with the current Promethee-II based PBDPA algorithm. The results of the experiments in the Cloudsim simulator show that VBDPA performs better than PBDPA in terms of makespan, revenue generation, and resource utilization. The results highlight VBDPA's potential as a viable option for effective task scheduling in containerized cloud environments [ABSTRACT FROM AUTHOR]

The global market of IoT devices is increasing rapidly. Examples of IoT like networks include smart cities, industrial enterprises, agriculture, home automation, healthcare etc. IoT offers efficient resource utilization, enhanced data collection, minimum human efforts etc. although it is constrained by many challenges such as security, privacy, limited interoperability, complexity and integration challenges. Among all, security and privacy are paramount and require efficient techniques with low power and minimum computer complexity as IoT is a power-constrained network. Traditional encryption methods fail to meet these limitations, so physical layer key generation (PLKG) using Received Signal Strength Indicator (RSSI) preprocessing, is a promising approach for securing such wireless networks. In this paper, the use of Kalman filtering for RSSI preprocessing in secure key generation at the physical layer is presented and compared its performance with the existing Principal Component Analysis (PCA) based preprocessing technique. The performance of the proposed approach is evaluated on three fading channels namely Rician, Rayleigh, and Nakagami to highlight its effectiveness in different environments. The results show that the Kalman filtering is significantly better than PCA in terms of Bit Disagreement Rate (BDR), Spearmen rank Correlation Coefficient (SCC) and Entropy, thus providing stronger security guarantees and more reliable key generation. This makes Kalman filtering a potential solution for PLKG in IoT environments, focusing on computing performance and high security. [ABSTRACT FROM AUTHOR]

Securing the information from the attackers is a crucial aspect of modern digital life. Numerous cryptographic algorithms are employed to provide security for data transmission. Among these, the International Data Encryption Algorithm (IDEA) stands out as a widely utilized algorithm for enhancing security. However, the IDEA algorithm has a notable drawback due to its relatively large number of weak keys. This susceptibility stems from the fixed 25-bit circular left shift during each key generation round. It is made weakens the regular key generation process of the IDEA algorithm. To address these concerns, this research work introduces a Modified International Data Encryption Algorithm (MIDEA) using a key size of 1024 bits. The method suggests a novel approach for the circular left shift by employing different bits that effectively overcome the limitations of fixed bits of the circular left shift. Additionally, this work presented innovative encryption and decryption techniques using a key size of 1024 bits. A comprehensive comparison is conducted between the IDEA and the MIDEA algorithm based on time complexity and security. Furthermore, this work provides a novel phishing attack detection framework using the suggested MIDEA technique. This framework is used for securely sharing patient data in the healthcare sector. The results of the proposed work indicate that encryption time varied from 20.08930 to 494.18258 seconds across file sizes from 0.97 to 40.8 megabytes. The experimental results demonstrate that the MIDEA algorithm exhibits significant performance enhancements with encryption speed improved by 60.67 percent and decryption robustness by 63.24 percent. [ABSTRACT FROM AUTHOR]

Securing the information from the attackers is a crucial aspect of modern digital life. Numerous cryptographic algorithms are employed to provide security for data transmission. Among these, the International Data Encryption Algorithm (IDEA) stands out as a widely utilized algorithm for enhancing security. However, the IDEA algorithm has a notable drawback due to its relatively large number of weak keys. This susceptibility stems from the fixed 25-bit circular left shift during each key generation round. It is made weakens the regular key generation process of the IDEA algorithm. To address these concerns, this research work introduces a Modified International Data Encryption Algorithm (MIDEA) using a key size of 1024 bits. The method suggests a novel approach for the circular left shift by employing different bits that effectively overcome the limitations of fixed bits of the circular left shift. Additionally, this work presented innovative encryption and decryption techniques using a key size of 1024 bits. A comprehensive comparison is conducted between the IDEA and the MIDEA algorithm based on time complexity and security. Furthermore, this work provides a novel phishing attack detection framework using the suggested MIDEA technique. This framework is used for securely sharing patient data in the healthcare sector. The results of the proposed work indicate that encryption time varied from 20.08930 to 494.18258 seconds across file sizes from 0.97 to 40.8 megabytes. The experimental results demonstrate that the MIDEA algorithm exhibits significant performance enhancements with encryption speed improved by 60.67 percent and decryption robustness by 63.24 percent.

Featured Application: Protection and encryption of digital files using hardware fingerprinting. Using an XOR Gate, two Static Random-Access Memory (SRAM) PUFs' challenge–response pairs (CRPs) can be combined into a single larger SRAM PUF. This study focuses on designing enhanced Physically Unclonable Functions (PUFs) based on SRAM devices and improving the security of cryptographic systems. Most SRAM PUFs are limited in their number of CRPs, which makes them vulnerable to enrollment attacks. In this research, we present an SRAM-based PUF design that greatly increases the number of CRPs and the entropy of the generated bits by performing exclusive-or (XOR) on the responses of two SRAM devices. This was implemented using a readily available development board, SRAM devices, and a user-friendly custom circuit board for cryptographic key generation. The cryptographic protocol was implemented using both C++ and python3. The proposed SRAM PUF design was experimentally demonstrated and showed substantial improvements in the security of various cryptographic applications as a hardware authentication device. It also addresses the specific vulnerabilities of legacy designs. [ABSTRACT FROM AUTHOR]

The security of data in an IoT network is of utmost importance. The medical IoT devices need to implement the security protocols in the devices to safeguard the crucial information. This paper proposes a novel high-speed data encryption scheme using elliptic curve cryptography (ECC) for key generation, key length reduction, Diffie–Hellman key exchange, SHA-256 and advance encryption standard (AES). The proposed hybrid data encryption scheme begins with elliptic curve key generation. Each authorized user in the broadcast range generated a private key using ECC. The proposed key reduction algorithm reduces the size of the secret key generated. This key is exchanged efficiently between the broadcast members using Diffie–Hellman key exchange. Each user then generates their final encryption keys. These keys are encoded using SHA-256 algorithm for further security. These keys are then used to encrypt the data using AES algorithm and sent to the cloud. The intended receivers can check the identity of the sender and decrypt the data using their own keys. The proposed method also includes user identity authentication. The origin of the message is verified to authenticate the sender of the message. This implementation is apt for home automation applications where data collected by medical devices like Fitbit and watches need to be shared among multiple users. The proposed method is at par in providing security to the data in much less time. The throughput, key generation time, key length, encryption time, decryption time and memory usage, Avalanche effect are efficient in comparison to existing methods. [ABSTRACT FROM AUTHOR]

Symmetric cryptography forms the backbone of secure data communication and storage by relying on the strength and randomness of cryptographic keys. This increases complexity, enhances cryptographic systems' overall robustness, and is immune to various attacks. The present work proposes a hybrid model based on the Latin square matrix (LSM) and subtractive random number generator (SRNG) algorithms for producing random keys. The hybrid model enhances the security of the cipher key against different attacks and increases the degree of diffusion. Different key lengths can also be generated based on the algorithm without compromising security. It comprises two phases. The first phase generates a seed value that depends on producing a randomly predefined set of key numbers of size n via the Donald E. Knuths SRNG algorithm (subtractive method). The second phase uses the output key (or seed value) from the previous phase as input to the Latin square matrix (LSM) to formulate a new key randomly. To increase the complexity of the generated key, another new random key of the same length that fulfills Shannon's principle of confusion and diffusion properties is XORed. Four test keys for each 128, 192,256,512, and 1024--bit length are used to evaluate the strength of the proposed model. The experimental results and security analyses revealed that all test keys met the statistical National Institute of Standards (NIST) standards and had high values for entropy values exceeding 0.98. The key length of the proposed model for n bits is 25*n, which is large enough to overcome brute-force attacks. Moreover, the generated keys are very sensitive to initial values, which increases the complexity against different attacks. [ABSTRACT FROM AUTHOR]

Creating and testing a biometric key is a critical process used for security and identity verification .When using biometric traits such as fingerprints, facial features, iris patterns, earprints, and voice patterns, a unique key is created and linked to the individual's biometric identity. These biometrics provide inherent uniqueness, resulting in a higher level of security compared to traditional methods. In addition, biometric authentication eliminates the need for users to memorize complex passwords or carry physical tokens, thus enhancing convenience and user experience. Iris recognition systems have received significant attention in biometrics for their ability to provide robust criteria for identifying individuals, thanks to the rich texture of the iris. In this research, the key generation process was created by converting biometrics (the iris) into a digital representation (a set of binary numbers from the two iris) that can be used in the encryption process. This is done by using digital image processing algorithms to extract unique features from the two irises. After the key is generated, it is tested using random metrics. If the key meets the criteria, it is random otherwise the key will be generated again.

With the rapid development of vehicle electronic communication technology, in-vehicle bus network system communicates with external electronic devices such as mobile phones and OBD II, causing in-vehicle bus networks to face severe network security threats. This study aims to explore the security scheme of in-vehicle bus networks based on a key management system to ensure the confidentiality, integrity, authenticity, and availability of vehicle communication, and innovatively propose a key management system. This key management system uses data encryption and signature algorithms based on the elliptic curve cryptographic domain, which is mainly composed of key generation and key distribution modules. By designing a key life cycle management strategy for In-Vehicle Ethernet and using the digital envelope technique, data encryption and digital signatures are combined to ensure the secure generation and distribution of keys. Experimental simulation results show that the session key negotiation speed of the proposed key management system for In-Vehicle Ethernet in this study is 1.533 ms, which improves the speed by 80.5% compared with the traditional key management system. The key management system proposed in this study improves the real-time information processing efficiency in In-Vehicle Ethernet and lays a solid foundation for the stable development of intelligent connected vehicles. [ABSTRACT FROM AUTHOR]

Blockchain (BC) in healthcare can be used for sharing medical records and secure storage and other confidential data. Deep learning (DL) assists in disease recognition through image analysis, specifically in detecting medical conditions from images. Image encryption ensures the security and privacy of medical images by encrypting the image before sharing or storage. The combination of image encryption, BC, and DL provides an efficient and secure system for medical image analysis and disease detection in healthcare. Therefore, we designed a new BC with an Image Encryption-based Optimal DL for Secure Disease Detection and Classification (BIEODL-SDDC) technique. The presented BIEODL-SDDC technique enables the secure sharing of medical images via encryption and BC technology with a DL-based disease classification process. Furthermore, the medical image encryption process took place using the ElGamal Encryption technique with a giraffe kicking optimization (GKO) algorithm-based key generation process. In addition, BC-based smart contracts (SCs) were used for the secure sharing of medical images. For the disease detection process, the BIEODL-SDDC technique encompassed EfficientNet-B7-CBAM-based feature extraction, Adam optimizer, and a fully connected neural network (FCNN). The experimental validation of the BIEODLSDDC technique was tested on medical image datasets and the outcome highlighted an enhanced accuracy outcome of 94.81% over other techniques. [ABSTRACT FROM AUTHOR]

Blockchain (BC) in healthcare can be used for sharing medical records and secure storage and other confidential data. Deep learning (DL) assists in disease recognition through image analysis, specifically in detecting medical conditions from images. Image encryption ensures the security and privacy of medical images by encrypting the image before sharing or storage. The combination of image encryption, BC, and DL provides an efficient and secure system for medical image analysis and disease detection in healthcare. Therefore, we designed a new BC with an Image Encryption-based Optimal DL for Secure Disease Detection and Classification (BIEODL-SDDC) technique. The presented BIEODL-SDDC technique enables the secure sharing of medical images via encryption and BC technology with a DL-based disease classification process. Furthermore, the medical image encryption process took place using the ElGamal Encryption technique with a giraffe kicking optimization (GKO) algorithm-based key generation process. In addition, BC-based smart contracts (SCs) were used for the secure sharing of medical images. For the disease detection process, the BIEODL-SDDC technique encompassed EfficientNet-B7-CBAM-based feature extraction, Adam optimizer, and a fully connected neural network (FCNN). The experimental validation of the BIEODL-SDDC technique was tested on medical image datasets and the outcome highlighted an enhanced accuracy outcome of 94.81% over other techniques.

Abstract The Internet of Things (IoT) security is a highly challenging research domain. The IoT user devices frequently function in vulnerable platforms that cause many security problems that can be considered. The model lightweight cryptographic (LWC) method stake place to reflect the importance of cryptographic systems which offer safety with the employ of an effectual count of resources. The purpose of the lightweight system's design is to strike a balance in many features like low resource demand, performance, and cryptographic algorithm stability and strength. Therefore, this study develops a new Improved Lightning Search Algorithm based End-to-End Lightweight Partially Homomorphic Encryption Approach (ILSA-LPHEA) for Securing the IoT Environment. For securing the data in the IoT environment, a lightweight partially homomorphic encryption (PHE) approach is used. Since key generation remains an important process to establish secure data transmission among IoT devices and servers, the ILSA is used. The hybridization of lightweight encryption with PHE strategy provides better security by enhancing privacy, confidentiality, and authentication, enabling end-to-end encryption. A detailed experimental result analysis highlighted the better solution of the ILSA-LPHEA algorithm with recent models.

The Internet of Things (IoT) has various security needs due to its diversified data management and application support. IoT processing platforms provide security guidelines to ensure that data is used correctly and without errors. This study proposes a Secure Tractable Authentication Scheme (STAS) for IoT-integrated real-time applications that considers the impact of data and associated applications. This approach is designed for rigorous user authentication and application signing. To improve service-level security, shared user credentials are validated at different access times. The new and previous properties are validated during the verification process to improve delegation and access. The transfer learning paradigm enables validation by updating and retaining prior authentication sessions as well as the current one. The access session maintains the learning state while increasing the trustworthiness of the verification. Authentication is achieved using tractable session-attached keys generated using normal digital signature processes. By reducing false positives and improving delegation security, end-to-end security between the Internet of Things and user applications is improved. Verification success percentage, service delay, and authentication time are utilized to validate the performance of the suggested technique.

Secure storage model for digital forensics represents essential progress in the domain, addressing the major problems associated with protecting and maintaining digital evidence. This method employs recent encryption systems and optimal key generation methods to ensure the confidentiality and integrity of data throughout the investigative process. Cloud forensics is an intelligent development of digital forensics to be preserved against online hacking. But, centralized evidence gathered and preservation reduces the reliability of digital evidence. The architecture for digital forensics in an Infrastructure as a Service (IaaS) cloud platform is a crucial structure intended to simplify the collection and protection of evidence while preserving the integrity and origin of digital objects within cloud-based methods. This architecture integrates numerous modules and methods to address the exclusive tasks modeled by cloud computing (CC) environments in the framework of forensic investigations. This paper develops a new digital forensic architecture utilizing the Authentication with Optimal Key Generation Encryption (DFA-AOKGE) technique. The main intention of the DFA-AOKGE method is to use a BC-distributed design to allocate data between numerous peers for data collection and safe storage. Additionally, the DFA-AOKGE model uses the Secure Block Verification Mechanism (SBVM) for the authentication procedure. Also, the secret keys can be produced by the usage of the Enhanced Equilibrium Optimizer (EEO) model. Furthermore, the encryption of the data takes place using a multikey homomorphic encryption (MHE) approach and is then saved in the cloud server. The simulation value of the DFA-AOKGE methodology takes place in terms of different aspects. The simulation results exhibited that the DFA-AOKGE system shows prominent performance over other recent approaches in terms of different measures.

This study focuses on designing enhanced Physically Unclonable Functions (PUFs) based on SRAM devices and improving the security of cryptographic systems. Most SRAM PUFs are limited in their number of CRPs, which makes them vulnerable to enrollment attacks. In this research, we present an SRAM-based PUF design that greatly increases the number of CRPs and the entropy of the generated bits by performing exclusive-or (XOR) on the responses of two SRAM devices. This was implemented using a readily available development board, SRAM devices, and a user-friendly custom circuit board for cryptographic key generation. The cryptographic protocol was implemented using both C++ and python3. The proposed SRAM PUF design was experimentally demonstrated and showed substantial improvements in the security of various cryptographic applications as a hardware authentication device. It also addresses the specific vulnerabilities of legacy designs.

Ensuring information security is indispensable during data communication among a collective of entities. This requirement is exemplified in the context of online voting systems (OVS), which necessitate the conduction of fair and transparent elections. A pivotal aspect of securing the OVS involves authenticating authorized voters prior to vote casting and encrypting the votes before their transfer over a secure channel for tallying. The present study centers on the development of a mathematical model for an authentication scheme that can be implemented in an OVS to facilitate impartial elections. The devised model integrates mathematical and cryptographic principles of Galois fields, group codes, and pseudo-random key stream generators to formulate individual voter passcodes, thereby providing two-factor authentication. The proposed scheme is exemplified through a scenario suitable for orchestrating a medium-scale election involving 65,536 voters via an OVS. Furthermore, with the appropriate selection of inputs, the model exhibits the capacity to support large-scale elections. [ABSTRACT FROM AUTHOR]

In this work the electrical behaviour of CMOS winner take all (WTA) cells is exploited to create a novel topology for physical unclonable functions (PUF) using current mirrors. The basic cell is based on low cascode current mirrors and high-gain Sekkerkiran WTA cells. These cells are capable to select a winner neuron according to manufacture process variations. Post-layout validation of the cell was performed using Cadence Virtuoso tools with a 65nm UMC technology. The PUF energy consumption is 5.670pJ/b with native bit instability of 2.294% among 1024 readings considering temperature variations. The PUF performance is quantified with uniqueness, uniformity and reliability metrics yielding results of 49.614%, 49.662% and 97.706% respectively among 1000 considered instances. An average inter-HD=49.837%, and intra-HD=1.570% are obtained assuming temperature variation from (-20C ∼ 120C) and 300mV of supply voltage fluctuation, the key generation latency is 73ns (8b), while the true randomness of keys is proved by NIST and autocorrelation function (ACF) tests. [ABSTRACT FROM AUTHOR]

This article introduced a new generalized RSA crypto-system based on $ 2n $ prime numbers called generalized RSA (GRSA). This is a modern technique to provide supreme security for the computer world by factoring the variable$ N $, where its analysis process has become much easier nowadays with the development of tools and equipment. $ 2n $ primes (prime numbers) are used in the GRSA crypto-system to provide security over the network system. This includes encryption, key generation, and decryption. In this method we used $ 2n $ primes which are not easily broken, $ 2n $ primes are not comfortably demented. This method provides greater performance and fidelity over the network system.

The rapid tech growth and widespread internet usage caused a surge in sharing multimedia (text, images, videos, audio) across public networks. Protecting this data is vital, demanding encryption to prevent unauthorized access. Image encryption distorts images for security. This paper highlights encryption's vital role in safeguarding multimedia, especially amid rising internet use and media exchange. It introduces a novel solution: a chaotic three-dimensional system for color image encryption. The study scrutinizes system traits using math software. It employs a new chaotic system to generate a crucial key sequence for pixel scrambling. Utilizing stream cipher encryption enhances security. Extensive security analysis tests its resilience against attacks like histogram and correlation techniques. Results are promising: a fairly uniform histogram, minimal correlation among pixels nearing zero, and entropy close to the ideal. Metrics like NPCR and UACI almost match ideal values, ensuring high security. Experiments confirm its effectiveness in encrypting diverse color images. The approach guarantees a uniform histogram, minimal pixel correlation nearing zero, entropy near the ideal value (8), and NPCR/UACI values close to ideals (99.61191% and 33.41068% respectively).

All aspects of human life have become increasingly dependent on data in the last few decades. The development of several applications causes an enormous issue on data volume in current years. This information must be safeguarded and kept in safe locations. Massive volumes of data have been safely stored with cloud computing. This technology is developing rapidly because of its immense potentials. As a result, protecting data and the procedures to be handled from attackers has become a top priority in order to maintain its integrity, confidentiality, protection, and privacy. Therefore, it is important to implement the appropriate security measures in order to prevent security breaches and vulnerabilities. An improved version of Modular Encryption Standard (IMES) based on layered modelling of safety mechanisms is the major focus of this paper's research work. Key generation in IMES is done using a logistic map, which estimates the values of the input data. The performance analysis demonstrates that proposed work performs better than commonly used algorithms against cloud security in terms of higher performance and additional qualitative security features. The results prove that the proposed IMES has 0.015s of processing time, where existing models have 0.017s to 0.022s of processing time for a file size of 256KB. [ABSTRACT FROM AUTHOR]

Cloud Computing, employed in various applications and services, refers to using computational resources as a service depending on customer needs via the Internet. The computing paradigm is built on data outsourcing to third-party-controlled data centers. Despite the significant developments in Cloud Services and Applications, various security vulnerabilities remain. This research proposes the EBBKG Model for Efficient Data Sharing in Cloud. For secure data sharing in the cloud, the approach combines BBKG with ABS. The method offers good data management that efficiently specifies the subsequent processing processes. The paradigm imposes encrypted access control, along with specific enhanced access capabilities. Secondly, the user's privacy may be adequately protected with a secure authentication paradigm that employs ABS to safeguard the user's private data. The key is optimized using BOA to enhance security and cloud providers and limit dangerous user threats using these implementations. Criteria like security, time complexity, and accountability govern the suggested method's effectiveness.

Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on electroencephalogram (EEG), which is usually demonstrated unstable performance due to the low signal-to-noise ratio (SNR). For the first time, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of the high-performance brain biometrics. Specifically, we put forward a novel brain-based key generation approach called multidimensional Gaussian fitted bit allocation (MGFBA). The proposed MGFBA method extracts keys from the local field potential of ten rats with high reliability and high entropy. We found that with the proposed MGFBA, the average effective key length of the brain biometrics was 938 bits, while achieving high authentication accuracy of 88.1% at a false acceptance rate of 1.9%, which is significantly improved compared to conventional EEG-based approaches. In addition, the proposed MGFBA-based keys can be conveniently revoked using different motor behaviors with high entropy. Experimental results demonstrate the potential of using intracortical brain signals for reliable authentication and other security applications. [ABSTRACT FROM AUTHOR]

This article introduced a new generalized RSA crypto-system based on 2 n prime numbers called generalized RSA (GRSA). This is a modern technique to provide supreme security for the computer world by factoring the variable N , where its analysis process has become much easier nowadays with the development of tools and equipment. 2 n primes (prime numbers) are used in the GRSA crypto-system to provide security over the network system. This includes encryption, key generation, and decryption. In this method we used 2 n primes which are not easily broken, 2 n primes are not comfortably demented. This method provides greater performance and fidelity over the network system. [ABSTRACT FROM AUTHOR]

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The Internet of Medical Things (IoMT) is a network of healthcare devices such as wearables, diagnostic equipment, and implantable devices, which are linked to the internet and can communicate with one another. Blockchain (BC) technology can design a secure, decentralized system to store and share medical data in an IoMT-based intelligent healthcare system. Patient records were stored in a tamper-proof and decentralized way using BC, which provides high privacy and security for the patients. Furthermore, BC enables efficient and secure sharing of healthcare data between patients and health professionals, enhancing healthcare quality. Therefore, in this paper, we develop an IoMT with a blockchain-based smart healthcare system using encryption with an optimal deep learning (BSHS-EODL) model. The presented BSHS-EODL method allows BC-assisted secured image transmission and diagnoses models for the IoMT environment. The proposed method includes data classification, data collection, and image encryption. Initially, the IoMT devices enable data collection processes, and the gathered images are stored in BC for security. Then, image encryption is applied for data encryption, and its key generation method can be performed via the dingo optimization algorithm (DOA). Finally, the BSHS-EODL technique performs disease diagnosis comprising SqueezeNet, Bayesian optimization (BO) based parameter tuning, and voting extreme learning machine (VELM). A comprehensive set of simulation analyses on medical datasets highlights the betterment of the BSHS-EODL method over existing techniques with a maximum accuracy of 98.51%, whereas the existing methods such as DBN, YOLO-GC, ResNet, VGG-19, and CDNN models have lower accuracies of 94.15%, 94.24%, 96.19%, 91.19%, and 95.29% respectively. [ABSTRACT FROM AUTHOR]

Cloud computing has become ubiquitous in our daily lives in recent years. Data are the source of technology that is generated hugely by various sources. Big data is dealing with huge data volumes or complex data. The major concern in big data is security threats. Security concerns create a negative impact on the user on the aspect of trust. In big data still, security threats exist as commonly known as DDOS (Distributed-Denial-of-Service) attacks, data loss, Inadequate Data Backups, System Vulnerabilities, and Phishing as well as Social Engineering Attacks. In our work, we have taken the data loss and Inadequate Data Backups issues into consideration. We analyze that RSA (Rivest, Shamir, & Adleman) is the most secure cryptography mechanism. In cloud computing, user authentication is the weaker section to be secured. Generally, the cryptography mechanism is done in the authentication section only. We implemented our new idea of registration with selected images and pins for processing RSA. By valid authentication approval earned by the proposed mechanism, the user is allowed to use the cloud database, encryption, decryption, etc. To prove the efficiency level of our proposed system, a comparison work is conducted between DSSE (Digital Signature Standard Encryption) and EFSSA (Efficient framework for securely sharing a file using asymmetric key distribution management). The experimental work is carried out and the performance evaluation is done using encryption time and decryption time analysis, throughput, and processing time. On this observation, the security level attained by ERSA is far better in comparison to DSSE and EFSSA with the maximum throughput attained by the proposed E-RSA being 500 Mb/Min and encryption time of 3.2 s, thus ensuring the user trust in using the cloud environment. [ABSTRACT FROM AUTHOR]

The proliferation of devices for the Internet of Things (IoT) and their implication in many activities of our lives have led to a considerable increase in concern about the security of these devices, posing a double challenge for designers and developers of products. On the one hand, the design of new security primitives, suitable for resource-limited devices, can facilitate the inclusion of mechanisms and protocols to ensure the integrity and privacy of the data exchanged over the Internet. On the other hand, the development of techniques and tools to evaluate the quality of the proposed solutions as a step prior to their deployment, as well as to monitor their behavior once in operation against possible changes in operating conditions arising naturally or as a consequence of a stress situation forced by an attacker. To address these challenges, this paper first describes the design of a security primitive that plays an important role as a component of a hardware-based root of trust, as it can act as a source of entropy for True Random Number Generation (TRNG) or as a Physical Unclonable Function (PUF) to facilitate the generation of identifiers linked to the device on which it is implemented. The work also illustrates different software components that allow carrying out a self-assessment strategy to characterize and validate the performance of this primitive in its dual functionality, as well as to monitor possible changes in security levels that may occur during operation as a result of device aging and variations in power supply or operating temperature. The designed PUF/TRNG is provided as a configurable IP module, which takes advantage of the internal architecture of the Xilinx Series-7 and Zynq-7000 programmable devices and incorporates an AXI4-based standard interface to facilitate its interaction with soft- and hard-core processing systems. Several test systems that contain different instances of the IP have been implemented and subjected to an exhaustive set of on-line tests to obtain the metrics that determine its quality in terms of uniqueness, reliability, and entropy characteristics. The results obtained prove that the proposed module is a suitable candidate for various security applications. As an example, an implementation that uses less than 5% of the resources of a low-cost programmable device is capable of obfuscating and recovering 512-bit cryptographic keys with virtually zero error rate. [ABSTRACT FROM AUTHOR]

The fierce competition in international markets and the rapid advancements in information technology result in shorter lead times, lower transportation capacity, and higher demand. The supply chain network is one of the most crucial areas of concentration in the majority of business circumstances. Blockchain technology is a promising option for safe information exchange in the supply chain network. Although preserving security at every level of the blockchain is somewhat important, cryptographic methodologies are frequently used in the existing works. The novel perceptive craving game search (PCGS) optimization algorithm is used to optimally generate the key for data sanitization, which assures the privacy of logistics data. Here, the original logistics data obtained from the manufacturer is sanitized with an optimal key generated by using the PCGS optimization algorithm, avoiding the risk of unauthorized access and data swarm that causes the system to lag. Moreover, the sanitized data obtained from the manufacturer is transmitted to the allowed parties via different sub-chains. The same generated key is used on the receiving customer side for reconstructing the original information from the sanitized data. The performance and results of the proposed blockchain-based privacy preservation model are validated using various parameters. [ABSTRACT FROM AUTHOR]

In the recent past, the storage of images and data in the cloud has shown rapid growth due to the tremendous usage of multimedia applications. In this paper, a modulated version of the Ikeda map and key generation algorithm are proposed, which can be used as a chaotic key for securely storing images in the cloud. The distinctive feature of the proposed map is that it is hyperchaotic, highly sensitive to initial conditions, and depicts chaos over a wide range of control parameter variations. These properties prevent the attacker from detecting and extracting the keys easily. The key generation algorithm generates a set of sequences using a designed chaos map and uses the harmonic mean of the generated sequences as the seed key. Furthermore, the control parameters are modified after each iteration. This change in the control parameters after each iteration makes it difficult for an attacker to predict the key. The designed map was tested mathematically and through simulations. The performance evaluation of the map shows that it outperforms other chaotic maps in terms of its parameter space, Lyapunov exponent, bifurcation entropy. Comparing the designed chaotic map with existing chaotic maps in terms of average cycle length, maximum Lyapunov exponent, approximate entropy, and a number of iterations, it is found to be very effective. The existence of chaos is also proved mathematically using Schwartz's derivative theorem. The proposed key generation algorithm was tested using the National Institute of Standards and Technology (NIST) randomness test with excellent results. [ABSTRACT FROM AUTHOR]

The real world is bounded by people, hospitals, industries, buildings, businesses, vehicles, cognitive cities, and billions of devices that offer various services and interact with the world. Recent technologies, including AR, VR, XR, and the digital twin concept, provide advanced solutions to create a new virtual world. Due to the ongoing development of information communication technologies and broadcast channels, data security has become a major concern. Blockchain (BC) technology is an open, decentralized, and transparent distributed database that can be maintained by the group. BC's major features are high credibility, decentralization, transparency, versatility, autonomy, traceability, anonymity, intelligence, reward mechanisms, and irreversibility. This study presents a blockchain-driven image encryption technique using arithmetic optimization with a fractional-order Lorenz system (BDIE-AOFOLS). The BDIE-AOFOLS technique uses the FOLS method, which integrates the Arnold map, tent map, and fractional Lorenz system. Besides this, an arithmetic optimization algorithm (AOA) was carried out for the optimum key generation process to achieve the maximum PSNR value. The design of an AOA-based optimal generation of keys for the FOLS technique determines the novelty of the current work. Moreover, the cryptographical pixel values of the images can be stored securely in the BC, guaranteeing image security. We compared the outcomes of the proposed BDIE-AOFOLS technique against benchmark color images. The comparative analysis demonstrated the improved security efficiency of the BDIE-AOFOLS technique over other approaches, with a mean square error of 0.0430 and a peak signal-to-noise ratio of 61.80 dB. [ABSTRACT FROM AUTHOR]

Sensitive Health Information (SHI) is a developing patient-centric model of medical data exchange, which is frequently outsourced to be stored at third party servers. Though, there have been various privacy issues as SHI could be disclosed to the unauthorised and third parties. This is a promising method to encrypt the SHI before outsourcing to assure the patients’ control over access to their own SHI. However, challenges like scalability in key management and flexible access have remained the most significant issues toward achieving fine-grained, cryptographically data access control. In this paper, the authors proposed a novel patient-centric system model for access control to SHIs stored in semi-honest servers. For fine-grained and scalable access control for SHIs, authors have proposed an encryption technique which is an improvement over RSA techniques to encrypt every patient’s SHI file. To different from previous works in secure data transmission, the authors focus on the data owner and divide users into several domains in SHI, which greatly decrease the key management complexity for data owners and users. Comprehensive analytical and experimental results are presented which reflect the efficiency of the proposed approach.

Physical layer security (PLS) is considered one of the most promising solutions to solve the security problems of massive Internet of Things (IoTs) devices because of its lightweight and high efficiency. Significantly, the recent physical layer key generation (PLKG) scheme based on transmission delay proposed by Huang et al. (2021) does not have any restrictions on communication methods and can extend the traditional physical layer security based on wireless channels to the whole Internet scene. However, the secret-sharing strategy adopted in this scheme has hidden dangers of collusion attack, which may lead to security problems such as information tampering and privacy disclosure. By establishing a probability model, this article quantitatively analyzes the relationship between the number of malicious collusion nodes and the probability of key exposure, which proves the existence of this security problem. In order to solve the problem of collusion attack in Huang et al.’s scheme, this article proposes an anti-collusion attack defense method, which minimizes the influence of collusion attack on key security by optimizing parameters including the number of the middle forwarding nodes, the random forwarding times, the time delay measurement times and the out-of-control rate of forwarding nodes. Finally, based on the game model, we prove that the defense method proposed in this article can reduce the risk of key leakage to zero under the scenario of the “Careless Defender” and “Cautious Defender” respectively.

Cloud Computing, employed in various applications and services, refers to using computational resources as a service depending on customer needs via the Internet. The computing paradigm is built on data outsourcing to third-party-controlled data centers. Despite the significant developments in Cloud Services and Applications, various security vulnerabilities remain. This research proposes the EBBKG Model for Efficient Data Sharing in Cloud. For secure data sharing in the cloud, the approach combines BBKG with ABS. The method offers good data management that efficiently specifies the subsequent processing processes. The paradigm imposes encrypted access control, along with specific enhanced access capabilities. Secondly, the user's privacy may be adequately protected with a secure authentication paradigm that employs ABS to safeguard the user's private data. The key is optimized using BOA to enhance security and cloud providers and limit dangerous user threats using these implementations. Criteria like security, time complexity, and accountability govern the suggested method's effectivenes [ABSTRACT FROM AUTHOR]

Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption (MKHE) technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models. [ABSTRACT FROM AUTHOR]

Cyber security is a term utilized for describing a collection of technologies, procedures, and practices that try protecting an online environment of a user or an organization. For medical images among most important and delicate data kinds in computer systems, the medical reasons require that all patient data, including images, be encrypted before being transferred over computer networks by healthcare companies. This paper presents a new direction of the encryption method research by encrypting the image based on the domain of the feature extracted to generate a key for the encryption process. The encryption process is started by applying edges detection. After dividing the bits of the edge image into (3x3) windows, the diffusions on bits are applied to create a key used for encrypting the edge image. Four randomness tests are passed through NIST randomness tests to ensure whether the generated key is accepted as true. This process is reversible in the state of decryption to retrieve the original image. The encryption image that will be gained can be used in any cyber security field such as healthcare organization. The comparative experiments prove that the proposed algorithm improves the encryption efficiency has a good security performance, and the encryption algorithm has a higher information entropy 7.42 as well as a lower correlation coefficient 0.653. [ABSTRACT FROM AUTHOR]

The generation of unique identifiers extracted from the physical characteristics of the underlying hardware ensures the protection of electronic devices against counterfeiting and provides security to the data they store and process. This work describes the design of an efficient Physical Unclonable Function (PUF) based on the differences in the frequency of Ring Oscillators (ROs) with identical layout due to variations in the technological processes involved in the manufacture of the integrated circuit. The logic resources available in the Xilinx Series-7 programmable devices are exploited in the design to make it more compact and achieve an optimal bit-per-area rate. On the other hand, the design parameters can also be adjusted to provide a high bit-per-time rate for a particular target device. The PUF has been encapsulated as a configurable Intellectual Property (IP) module, providing it with an AXI4-Lite interface to ease its incorporation into embedded systems in combination with soft- or hard-core implementations of general-purpose processors. The capability of the proposed RO-PUF to generate implementation-dependent identifiers has been extensively tested, using a series of metrics to evaluate its reliability and robustness for different configuration options. Finally, in order to demonstrate its utility to improve system security, the identifiers provided by RO-PUFs implemented on different devices have been used in a Helper Data Algorithm (HDA) to obfuscate and retrieve a secret key. [ABSTRACT FROM AUTHOR]

Today, with the progression in ICT, information sharing (IS) has turned outto be more possible. IS in supply chains (SC) has grown up more proficient by the global preamble of long-term coordination and cooperation that leads to enhancement of aggressive advantages of companies. However, lack of IS in companies results in incompetence of coordinating activities in organizations. Here, this work aims to enhance the security and privacy of the proposed block-chain-aided SCM, wherein the modified "Data Sanitization and Data Restoration" is done with an optimal key creation procedure to protect the receptive data in every block. Here, the optimal key is chosen via Sine Map estimation-based BSO (SME-BSO) algorithm. Finally, parametric analysis varies ra 2 from 0-1, 0.2, 0.4, 0.6 and 0.8. [ABSTRACT FROM AUTHOR]

Cloud computing is being utilized in an assortment of ventures, including money, business, and diversion and the client's information is stored in a remote system given by the cloud supplier. One of the essential issues in the cloud is guaranteeing safe correspondence. The proposed security framework is deals with improving the degree of safety of information traded by cloud clients. This paper utilizes fuzzy investigation to evaluate cloud clients' trust levels, as well as dissemination and renouncement in a cloud and to keep up with data security during access in mobile cloud computing. It guarantees information security in versatile cloud and gets shielded from undesirable access and change. [ABSTRACT FROM AUTHOR]

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population. [ABSTRACT FROM AUTHOR]

Virtual machines (VMs) are preferred by the majority of organizations due to their high performance. VMs allow for reduced overhead with multiple systems running from the same console at the same time. A physical server is a bare-metal system whose hardware is controlled by the host operating system. A physical server runs on a single instance of OS and application. A virtual server or virtual machine encapsulates the underlying hardware and networking resources. With the existing physical server, it is difficult to migrate the tasks from one platform to another platform or to a datacentre. Centralized security is difficult to setup. But with Hypervisor the virtual machine can be deployed, for instance, with automation. Virtualization cost increases as well as a decrease in hardware and infrastructure space costs. We propose an efficient Azure cloud framework for the utilization of physical server resources at remote VM servers. The proposed framework is implemented in two phases first by integrating physical servers into virtual ones by creating virtual machines, and then by integrating virtual servers into cloud service providers in a cost-effective manner. We create a virtual network in the Azure datacenter using the local host physical server to set up the various virtual machines. Two virtual machine instances, VM1 and VM2, are created using Microsoft Hyper-V with the server Windows 2016 R. The desktop application is deployed and VM performance is monitored using the PowerShell script. Tableau is used to evaluate the physical server functionality of the worksheet for the deployed application. The proposed Physical to Virtual to Cloud model (P2V2C) model is being tested, and the performance result shows that P2V2C migration is more successful in dynamic provisioning than direct migration to cloud platform infrastructure. The research work was carried out in a secure way through the migration process from P2V2C. [ABSTRACT FROM AUTHOR]

• Here, revenue distribution model depend on Multiple Services (MS). • Key generation is performed depend on the SpinalNet. • Data protection phase, includes encryption, hashing functions, passwords. • Authentication scheme done by cloud security on blockchain and smart contracts. Cloud computing is currently emerging as a developing technology in which a Cloud Service Provider (CSP) is a third-party organization that provides effective storage of data and facilities to a large client base. Saving information in a cloud offers users the satisfaction of accessing it without the need for direct knowledge of the distribution and management of an infrastructure. The primary objective is to develop a novel, secure, and privacy-preserving data-sharing model that utilizes deep-based key generation on blockchain in the cloud. Data communication is done using multiple entities. The research aims to develop a collaborative data-sharing method in the cloud for the authentication scheme for cloud security on blockchain and smart contracts. Initialization, registration, key generation, authentication of data sharing, and validation are carried out here. The proposed data-sharing model involves a revenue distribution model that depends on Multiple Services (MS) models to improve multiple cloud services. The security parameters namely passwords, hashing functions, key interpolation, and encryption are used for preserving the Data privacy and here the SpinalNet is used for generating keys. Furthermore, the devised SpinalNet_Genkey obtained a value of 45.001 MB, 0.002, and 0.003 sec for memory usage, revenue, and computation cost. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging field that encompasses several heterogeneous devices and smart objects that are integrated with the network. In open platforms, these objects are deployed to present advanced services in numerous applications. Innumerable security-sensitive data is generated by the IoT device and therefore, the security of these devices is an important task. This work formulates a secure data transfer technique in IoT, named Authentication enabled Privacy Protection (APPS) scheme for resource-constrained IoT devices. The proposed scheme demonstrates resilience against various attacks; such as resisting reply attacks, device anonymity, untracebility, session key establishment, quantum attacks and resisting MITM attacks. For the privacy protection scheme, the secured data transfer is initiated between the entities, like IoT devices, servers, and registration centers, by using various phases, namely registration phase, key generation phase, data encryption, authentication, verification, and data retrieval phase. Here, a mathematical model is designed for protecting data privacy using hashing, encryption, secret keys, etc. Finally, performance of proposed APPS model is analyzed; wherein the outcomes reveal that the proposed APPS model attained the maximum detection rate of 0.85, minimal memory usage of 0.497MB, and minimal computational time of 112. 79 sec and minimal turnaround time 131.91 sec. [ABSTRACT FROM AUTHOR]

Eliminating the risk of bugs and external decryption in cryptographic keys has always been a challenge for researchers. The current research is based on a new design that uses an Omega network-based pseudorandom DNA key generation method to produce cryptographic keys for symmetric key systems. The designed algorithm initially takes two random binary numbers as inputs to the Omega network design, generating an output of 256 symmetric keys. The Omega network uses the concept of the central dogma of molecular biology (DNA and RNA properties), including DNA replication (for DNA) and the transcription process (for RNA). The NIST test suite is applied to test the security properties of the proposed design. According to the study's findings, the suggested design is significantly suited to achieve the NIST test security properties and passes all of the NIST recommended tests. [ABSTRACT FROM AUTHOR]

Physical-layer key generation technology requires information reconciliation to correct channel estimation errors between two legitimate users. However, sending the reconciliation signals over the public channel increases the communication overhead and the risk of information leakage. Aiming at the problem, integrated secure communication schemes using non-reconciled keys have attracted extensive attention. These schemes exploit channel coding to correct both inconsistent keys and transmission error bits. Meanwhile, more redundant code bits must be added to correct errors, which results in a lower secure transmission rate. To address the problem, we analyze the merit of channel correlation between non-reconciled key generation and secure transmission. Inspired by this, we propose a non-reconciled physical-layer keys-assisted secure communication scheme based on channel correlation. First of all, the signal frame is designed to make use of channel correlation between non-reconciled key generation and secure transmission. Based on the channel correlation, non-reconciled keys are then generated from the wireless channel to encrypt transmitted data. Moreover, an adaptive coding algorithm based on the equivalent channel is presented to encode the data bits before encryption, to guarantee reliable transmission. Finally, theoretical analysis and simulations demonstrate the significant performance of the proposed scheme in terms of low bit error ratio and high secure transmission rate. [ABSTRACT FROM AUTHOR]

Group communication is widely used by most of the emerging network applications like telecommunication, video conferencing, simulation applications, distributed and other interactive systems. Secured group communication plays a vital role in case of providing the integrity, authenticity, confidentiality, and availability of the message delivered among the group members with respect to communicate securely between the inter group or else within the group. In secure group communications, the time cost associated with the key updating in the proceedings of the member join and departure is an important aspect of the quality of service, particularly in the large groups with highly active membership. Hence, the paper is aimed to achieve better cost and time efficiency through an improved DC multicast routing protocol which is used to expose the path between the nodes participating in the group communication. During this process, each node constructs an adaptive Ptolemy decision tree for the purpose of generating the contributory key. Each of the node is comprised of three keys which will be exchanged between the nodes for considering the group key for the purpose of secure and cost-efficient group communication. The rekeying process is performed when a member leaves or adds into the group. The performance metrics of novel approach is measured depending on the important factors such as computational and communicational cost, rekeying process and formation of the group. It is concluded from the study that the technique has reduced the computational and communicational cost of the secure group communication when compared to the other existing methods. [ABSTRACT FROM AUTHOR]

The Healthcare system is an organization that consists of important requirements corresponding to security and privacy, for example, protecting patients’ medical information from unauthorized access, communication with transport like ambulance and smart e-health monitoring. Due to lack of expert design of security protocols, the healthcare system is facing many security threats such as authenticity, data sharing, the conveying of medical data. In such situation, block chain protocol is used. In this manuscript, Efficient Block chain Network for securing Healthcare data using Multi-Objective Squirrel Search Optimization Algorithm (MOSSA) is proposed to generate smart and secure Healthcare system. In this the block chain is a decentralized and the distributed ledger device that consists of various blocks linked with digital signature schemes, consensus mechanisms and chain of hashing, offers highly reliable storage capabilities. Further the block chain parameters, such as block size, transaction size and number of block chain channels are optimized with the help of MOSSA. With the evolution of the MOSSA provide new features for enhancing security and scalability. The simulation process is executed in the JAVA platform. The experimental result of the proposed method shows higher throughput of 26.87%, higher efficiency of 34.67%, lowest delay of 22.97%, lesser computational overhead of 37.03%, higher storage cost of 34.29% when compared to the existing method such as Block chain-ECIES-HSO, Block chain-hybrid GO-FFO, Block chain-SDN-HSO algorithm for healthcare technologies. [ABSTRACT FROM AUTHOR]

Every organization in this digital age is expected to exponentially increase its digital data due to generations from machines. The advanced computations of Big Data are now showing various opportunities for the researchers who work on security enhancements to ensure the efficient accessibility of the data stores. Our research work aims to derive a Fusion-based Advanced Encryption Algorithm (FAEA) for a cost-optimized satisfiable security model toward the usage of Big Data in the cloud. The FAEA method is evaluated for its performance toward efficiency, scalability, and security and proved to be 98% ahead of the existing methods of Security Hadoop Distributed File System Sec (HDFS) and Map Reduce Encryption Scheme (MRE). On the other hand, this work aims to address the problems of usage of Big Data in the cloud toward the sole solution, cost-effective solutioning, and proof of ownership. The outcome analysis of FAEA revolves around addressing these three major problems. This research work would be much helpful for the IT industries to manage Big Data in Cloud with security aspects for the decade. [ABSTRACT FROM AUTHOR]

Aiming at the problems of slow channel changes and low key generation rate in IoT quasi-static scenarios, a key generation method based on reconfigurable intelligent surface (RIS) was proposed. First, the agility characteristics of RIS was used to construct a fast-changing channel. Then, the base station and legitimate users extracted a consistent key from the channel information through channel estimation, conversion, and information negotiation. Finally, optimal time allocation for data transmission and key generation in the coherent time to achieve the maximum transmission rate of one-time pad. The simulation results show that the key generation rate of proposed method is higher than that of the existing relay-assisted method, random signal flow method and random number method, and as the number of RIS reflection units and frequency of channel estimation in the coherence time increase, the key generation rate will be further improve.