Search

As technology rapidly advances and internet reliance grows for daily activities, the demand for robust and efficient cryptographic protocols is undeniable. Cryptography, the art of secret writing, uses symmetric mathematical functions to secure data in transit or at rest. Stream ciphers, unlike block ciphers, encrypt data sequentially, eliminating the need for padding with extra bits to complete a block. Rivest Cipher 4 (RC‐4) has been one of the most widely used stream ciphers. However, vulnerabilities exploited by attackers in 2014 led to its widespread replacement. In this paper, we introduce a modified version of RC‐4 designed to address and mitigate the security threats of traditional RC‐4. By incorporating negative numbers into the key generation schedule, our preliminary analysis shows promising results. This adaptation not only enhances RC‐4's security features but also revitalizes its applicability in modern cryptographic scenarios, potentially restoring its viability as a secure cryptographic tool. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANET) help to increase traffic safety, manageability, and efficiency. VANET facilitates effective communication in the vehicular nodes. Here, the network attack is one of the significant consequences of VANET, which may affect network security. The high mobility of the nodes may become difficult to transfer the data through the vehicle. In this article, a novel method of Elliptic Curve Cryptography (ECC) is proposed. The key is generated by Key Generation Unit (KGU), and also the key is fed into the Road Side Unit (RSU). The keys in Enhanced ECC (EECC) are optimized using the Adaptive Horse herd Optimization Algorithm (AHOA). During decryption, the key of source node K1 is to be taken from RSU, where the key is processed by the Digital Signature method to obtain EECC key K2. With the generated K2, the destination node can retrieve the data by the proposed model. Thus, the simulation analysis of the proposed AHOA-EECC method has attained less time complexity which was compared against the high value of 5%, 12.4%, 50%, and 13.7% for GWO-EECC, PSO-EECC, AOA-EECC, and HOA-EECC. Finally, the outcome of the proposed system enhances the security level and performs better data transmission. [ABSTRACT FROM AUTHOR]

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]

Random numbers are the critical components of most security algorithms. Generating random numbers from a natural source or any deterministic algorithm suffers from shorter periods, inefficient energy consumption, smaller keyspace, etc. This work proposes a technique to generate random numbers with reduced complexity while increasing the keyspace and period of the random numbers. This work presents a novel method of cross-over operation on two chaotic maps to design a new hybrid chaotic map. The Pseudorandom Number Generator (PRNG) is constructed from the newly designed chaotic map, and the bit sequences are analyzed to justify their security strength. It is then applied to encrypt the images, and their properties are investigated. The proposed system is measured to have a 21.9% increase in keyspace and stronger key sensitivity. The correlation coefficient and entropy of ciphered images were shown to have a uniform distribution of pixel values. The average number of pixels change rate (NPCR) in ciphered images is calculated to be 99.4694%. The unified average changing intensity (UACI) is averaged at 33.4211%. The experimental analysis validates the competence of the proposed chaotic system for cryptography. [ABSTRACT FROM AUTHOR]

Summary: In this research, we present an innovative cryptographic key generation method utilizing a Generative Adversarial Network (GAN), enhanced by Merkel tree verification, marking a significant advancement in cryptographic security. Our approach successfully generates a large 6272‐bit key, rigorously tested for randomness and reliability using the Dieharder and NIST test suites. This groundbreaking method harmoniously blends cutting‐edge machine learning techniques with traditional cryptographic verification, setting a new standard in data encryption and security. Our findings not only demonstrate the efficacy of GANs in producing highly secure cryptographic keys but also highlight the effectiveness of Merkel tree verification in ensuring the integrity of these keys. The integration of merkel tree in our method provides a means to efficiently verify the authenticity of the large generated key sets. This research has broad implications for the future of secure communications, providing a robust solution in a world increasingly reliant on digital security. The integration of machine learning and cryptographic principles opens up new avenues for research and development, promising to bolster security measures in an era where digital threats are constantly evolving. This work contributes significantly to the field of cryptography, offering a novel perspective and robust solutions to the challenges of digital data protection. [ABSTRACT FROM AUTHOR]

This study introduces a cutting-edge image encryption algorithm aimed at elevating security standards. The Pan-Tompkins Algorithm (PTA) for key generation is proposed for the first time in this study. Additionally, employing steganography through the Least Significant Bit (LSB) method for embedding keys within the encrypted image enhances secure key distribution, thereby fortifying the encryption process. On the other hand, the integration of advanced algorithms, such as Zigzag scanning, the Affine Image Encryption Algorithm (AA), and the Vigenere Image Encryption Algorithm (VA), constitutes the fundamental innovation of the proposed image encryption algorithm. The proposed algorithm is named PanAAVA:Affine Algorithm and Vigenere Algorithm Encryption with PTA-Based Key Generation. The PanAAVA algorithm ensures unparalleled security by encrypting the positions and values of pixels using AA and VA. Notably, using PTA for key generation marks a distinctive and new key generation method feature of the algorithm. To assess the effectiveness of the PanAAVA, a comprehensive comparative analysis is conducted against well-established encryption methodologies, including Lena, Baboon, Airplane, and Pepper. The PanAAVA demonstrates exceptional proficiency in histogram analysis. The PanAAVA demonstrates a Unified Average Changing Intensity (UACI) of 33.4044%. Additionally, the Number of Pixels Change Rate (NPCR) is measured at 99.7442%, showcasing the algorithm's effectiveness in inducing significant pixel changes. The proposed algorithm's Mean Square Error (MSE) is calculated at 3.20679E5%. The proposed algorithm's Peak Signal to Noise Ratio (PSNR) is recorded at 9.512475. The Key Space Size of the proposed algorithm is measured at 2209. Regarding correlation analysis, the PanAAVA achieves a high correlation score of 7.9996. The proposed algorithm successfully passes the National Institute of Standards and Technology (NIST) analysis, demonstrating a remarkably strong correlation close to 0 and a Structural Similarity Index Measure (SSIM) of 0.9977. Furthermore, regarding quantum communication, the proposed algorithm maintains stable key rates of 47.5 ± 0.8 kHz during the day and 50.9 ± 0.7 kHz at night. Additionally, PanAAVA achieves low Quantum Bit Error Rate (QBER) values of 4.77 ± 0.02, ensuring reliable and secure communication. The PanAAVA also demonstrates robust asymmetries at 49.81 ± 0.02 and 50.14 ± 0.03 for a crystal length of 20 mm. highlighting PanAAVA's adaptability and effectiveness in different scenarios. PanAAVA outperforms other encryption algorithms concerning performance measurements and comparisons. In conclusion, the PanAAVA emerges as a beacon of superior security capabilities and innovation in image encryption, showcasing the potential to redefine standards in the field. [ABSTRACT FROM AUTHOR]

Recently, cloud computing has become a growing technology in the information technology industry because of its several smooth delivery services. In cloud computing, multi-tenancy is one of the primary features that affords economic and scalability significance to the service providers and end-users by distributing a similar cloud platform. Due to the increasing demand for cloud computing, cloud usage has increased, so various vulnerabilities and threats have also been enhanced. Hence, data security and privacy are considered the major issues of multi-tenant environments in the cloud. Several existing studies have developed different mechanisms to solve security issues in multi-tenant cloud environments. However, they faced various problems while improving security, and this led to a lack of confidentiality, authenticity, and data integrity. Thus, this research paper intends to propose an efficient encryption approach for securing data delivery in the cloud with reduced time. For secure data delivery, homomorphic encryption is utilized to encode the cloud server's data. In homomorphic encryption, four stages are available for data delivery: key generation, encryption, decryption, and evaluation. The main problems in this homomorphic encryption mechanism are key sharing and key management. Due to these problems, the performance of homomorphic encryption is diminished. Thus, the proposed work introduces an Aquila optimizer for the key generation process. In this, optimal keys are selected, and it provides improved data security and privacy for cloud users. Finally, the selected keys are generated for the encryption and decryption process. The efficiency of the proposed approach is proved by comparing the performance in terms of encryption time, decryption time and throughput over the existing schemes like Rivest, Shamir and Adleman, ElGamal, Algebra Homomorphic Encryption scheme based on ElGamal (AHEE) and modified AHEE. The experimental results reveal that the proposed model achieves reduced encryption and decryption time of 972 ms and 4261 ms for the data size ranges from 5 to 25 mb. [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]

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.

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.

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]

Many real‐world applications, such as smart cities, industrial automation, health care, and so forth, utilize IoT‐enabled devices as a part of wireless networks. IoT devices must have low power and low computation complexity requirements with proper measures of security challenges. Since traditional cryptography techniques are not computationally efficient, other alternatives, such as physical layer key generation (PLKG), is one of the attractive means to achieve security. In this paper, we propose to use a fast independent component analysis (FICA) based KuFaI (Kurtosis and FICA) algorithm for a secured PLKG system. This method reduces data dimensions and improves system performance regarding Bit Disagreement Rate (BDR) and randomness. In KuFaI, we rearrange the received signal strength indicator (RSSI) data set using FICA and then select components based on the kurtosis function. Results demonstrate that the performance of the proposed algorithm is far better than the previously used PCA‐based algorithm. [ABSTRACT FROM AUTHOR]

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]

With increasing data transmission and the escalating risks of data theft, the demand for fast and robust encryption algorithms has intensified. The Rivest Shamir Adleman (RSA) algorithm is a widely used asymmetric cryptographic solution for secure data communication. However, the computational resources required for encryption and decryption processes can become significant, particularly as the volume of data increases. This research paper proposes a dynamic approach to enhance the RSA algorithm tailored explicitly for large-scale data encryption and decryption. The proposed method involves storing the encrypted value of repeated elements, aiming to optimize performance. To evaluate the efficacy of this approach, extensive experiments were conducted on various datasets comprising paragraphs of different sizes. The experiments involved encrypting and decrypting paragraphs containing 10, 50, 100, 200, 500, 1000, and 10,000 words. Comparative analyses were performed against existing RSA, El Gamal, and AES algorithms. Results indicate that the proposed dynamic RSA approach consistently outperforms traditional RSA in terms of encryption time, decryption time, and total execution time across all tested paragraph sizes. The regression analysis revealed stark differences between the two approaches. The regression line for traditional RSA exhibited a significantly steeper slope 1.75 and a substantially higher intercept 1001.42 compared to the proposed dynamic RSA approach, which demonstrated a lower slope 0.0113 and a much smaller intercept 28.58. These findings underscore the superior scalability and efficiency of the proposed dynamic RSA approach over traditional RSA, particularly in handling larger volumes of data while maintaining lower computational overheads. [ABSTRACT FROM AUTHOR]

In recent times, there has been a significant surge in research interest in security, driven by the continuous growth of threats and cyber attacks. Recognizing that existing state-of-the-art security schemes may not adequately address the requirements for lightweight properties and enhanced security measures, the aim of this research is to contribute to the field. The focus is on the development and implementation of lightweight encryption protocols designed to bolster security measures in the face of evolving challenges. The primary objective of this endeavor is to create a secure system utilizing the 3-round Kuznyechik algorithm (SSKA). In this process we introduce key generation algorithm followed by encryption and decryption techniques. The key generation algorithm is mainly based on three-round Kuznyechik algorithm. The Kuznyechik 3-round encryption mechanism has been proven to be effective in producing an imbalanced set at the end of the round and a balanced set before to the third round by employing a set in which the first byte is active and the following bytes are passive. This encourage us to use 3-round Kuznyechik algorithm. Building upon the aforementioned findings, an effective algorithm is suggested for determining the key of the final round in the 3-round Kuznyechik algorithm, employing the integral cryptanalysis method. Based on simulation results, the proposed approach demonstrates noteworthy enhancements in comparison to baseline algorithms concerning time complexity, encryption time, throughput, decryption time, data overhead, and space complexity. [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]

In an era marked by growing concerns about data security and privacy, the need for robust encryption techniques has become a matter of paramount importance. The primary goal of this study is to protect sensitive information during transmission while ensuring efficient and reliable decryption at the receiver's side. To generate robust and unique cryptographic keys, the proposed approach trains an autoencoder neural network based on hashing and optionally generated prime numbers in the MNIST dataset. The key serves as the foundation for secure communication. An additional security layer to the cryptographic algorithm passing through the first ciphertext, was employed utilizing the XORed and Blum-Blum-Shub (BBS) generators to make the system resistant to various types of attacks. This approach offers a robust and innovative solution for secure data transmission, combining the strengths of autoencoder-based key generation and cryptographic encryption. Its effectiveness is demonstrated through testing and simulations. [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.

Nowadays, data storage in a cloud environment plays an important role in managing large scale information. However, the main problem with using a cloud environment is data security. Various encryption approaches are introduced in the existing works to provide security. However, the security issues are not resolved. This paper presented a good security enhancement with decentralized blockchain and enhanced encryption approaches. Initially, the Lamport Merkle Digital Signature Generation based blockchain approach was developed to authenticate the user data to prevent unauthorized access. This presented approach authenticates cloud users by constructing a tree, in which the leaves symbolize the hash function of sensitive user data. Then in the encryption phase, the original data is encrypted as ciphertext utilizing Optimized Elliptic curve cryptography. Here, the Collective Decision optimization approach is utilized for optimal key selection. Furthermore, the selected optimal secret key is exchanged securely utilizing the Improved Diffie-Hellman approach. The presented blockchain-based security scheme enhances data security and ensures confidential data sharing between users. The experimental results of the presented approach are examined in terms of different performance metrics. The performance analysis of the presented approach outperforms the different existing approaches. [ABSTRACT FROM AUTHOR]

Maximizing the security of the cloud system is most important need of this world. Similarly, the data controllers are also increased stealing the sensitive and personal data from the cloud computing system. A repeated data violation occurs due to a large amount of outsourced and unsecured sensitive data. At present, numerous research works have been performed to secure the data in the cloud, but sometimes they do not succeed in securing the sensitive data. A Multi-Objective Privacy Preservation Model for Cloud Security utilizing Hunter Prey Optimization approach is proposed in this paper. Initially, the data is taken from 5 types of dataset like, concrete, Heart disease, Super Conductivity, Air Quality and wholesale customer datasets. The input data is given to the sanitization of data and restoration stage. In sanitization of data and restoration phase, SMA is utilized. After preventing the leakage in the data sanitization and restoration stage, the input data is applied to the key generation phase. Multi-objective functions such as the preservation of information ratio, the ratio of hiding, and the modification degree are performed at the key generation stage with the help of the hunter prey optimization algorithm to improve cloud data security. The proposed MOPP-CS-HPOA method is evaluated under some performances metrics, like modification degree, ratio of hiding, information preservation ratio, key sensitivity and computational time. Then the proposed MOPP-CS-HPOA method attains 35.69%, 38.504% and 31.805% higher information preservation ratio; 39.52%, 30.28% and 38.14% higher hiding ratio; analysed with MOPP-CS-JSSO, MOPP-CS-PS-BMFO and MOPP-CS-SVM-KNN-RF-NB-ANN methods. [ABSTRACT FROM AUTHOR]

Audio cryptography is the practice of encrypting audio data to prevent illegal access to and listening to it. This paper presents an innovative technique of audio cryptography based on the Python computer language. To ensure secrecy and integrity, the suggested system encrypts and decrypts audio signals using advanced cryptographic techniques. A crucial component of AES, the cryptographic key is dynamically created to improve security. Python's broad library support and ease of use make it an ideal platform for implementing the AES algorithm, which ensures dependable and effective audio data encryption. The system utilizes Python's cryptography library for seamless integration and ease of implementation. Simulation results demonstrate the efficacy of the AES algorithm in securely encrypting and decrypting audio data with reduced noise compared to traditional methods. [ABSTRACT FROM AUTHOR]

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.

Key generation for data cryptography is vital in wireless communications security. This key must be generated in a random way so that can not be regenerated by a third party other than the intended receiver. The random nature of the wireless channel is utilized to generate the encryption key. However, the randomness of wireless channels deteriorated over time due to channel aging which casing security threats, particularly for spatially correlated channels. In this paper, the effect of channel aging on the ciphering key generations is addressed. A proposed method to randomize the encryption key each coherence time is developed which decreases the correlation between keys generated at consecutive coherence times. When compared to the conventional method, the randomness improvement is significant at each time interval. The simulation results show that the proposed method improves the randomness of the encrypting keys. [ABSTRACT FROM AUTHOR]

Vehicular Ad hoc Network (VANET) allows transmission, amid moving or stationary vehicles via wireless technology. Amongst several problems, safe transmission is the most important one in smart VANETs in 5G networks. Smart vehicles require integration with advanced road systems encompassing smart payment and traffic control systems. Numerous security mechanisms are used in VANETs to ensure safe communication. One such mechanism is cryptographic digital signatures based on encryption. This study introduces the new seagull optimization algorithm involving share creation with an image encryption scheme (SGOA-SCIES) for secure VANET transmissions. The goal of the SGOA-SCIES technique is to create a considerable number of shares and encrypt them to accomplish security. In the SGOA-SCIES technique, a Multiple Share Creation (MSC) scheme is employed to generate numerous share sets. For the share encryption process, the SGOA-SCIES technique engages the Fractional-Order Chaotic System (FOCS) approach to encrypt the generated shares. The optimal keys of the FOCS method can be chosen by the SGOA usage, which ameliorates the security level. The performance evaluation of the SGOA-SCIES method is examined on benchmark data. The simulations demonstrate the enhanced SGOA-SCIES methodology outcome and compare it with the ones of other existing systems and under the implementation of various measures. [ABSTRACT FROM AUTHOR]

Cloud computing is a rapidly advancing paradigm that enables users to access various services and resources anytime, anywhere. With this advancement, security has become a major concern for business organisations and individuals, and hence, it is essential to ensure that the services are provided with high data security. Numerous researches have focused on devising effective techniques to enhance data security. However, with the increasing connectivity, security still remains to be a major challenge. This paper devises a novel data protection scheme in the cloud by using the Twofish encryption algorithm and a key generation scheme with the Bald Eagle Pelican Optimization (BEPO) Algorithm. The proposed Twofish+BEPO_KeyGen is implemented in various phases, like initialization, registration, key generation, data encryption, authentication, validation and data sharing, and data decryption. Here, the Twofish algorithm is used to encrypt the data that has to be outsourced to the cloud, and for encryption, the security key required is generated by the BEPO algorithm. The efficacy of the Twofish+BEPO_KeyGen approach is examined by considering metrics, like memory usage, validation time, normalized variance, and conditional privacy, and is detected to have achieved values of 76.3 MB, 37.278 s, 1.665, and 0.926, correspondingly. [ABSTRACT FROM AUTHOR]

Nowadays the cloud computing environment is widely utilized for transmitting and receiving data securely. Inorder to secure the data the encryption method is used but still due to some limitations the security process is diminished. Therefore, this paper proposes a new algorithm to provide better security while transmitting data through the network. At first, the sensitivity of data is determined using a lightweight convolutional neural network (LWCNN) model which is used to categorize the unclassified data into two categories normal sensitive data and highly sensitive data. After determining the level of data sensitivity, the encryption process is performed further. The efficient hash function-based duplication detection approach is employed to maintain confidential information before outsourcing it to a cloud server. Subsequently, the ideal keys are generated for each data based on its sensitivity level using the proposed fuzzy tuna swarm (FTS) algorithm. Finally, the data is encrypted by converting plain text into ciphertext which is only visible to authorized users. The experimental results show that the LWCNN model utilized for data sensitivity classification achieved 94% accuracy and the FTS algorithm proposed for optimal key generation took much less communication time of about 1800 µs than other compared techniques. [ABSTRACT FROM AUTHOR]

Biometrics refers to the technology for personal recognition or authentication based on our physiological and/or behavioural characteristics. Distinctiveness, Permanence, Collectability, Performance, Acceptability, and Circumvention are the properties of biometric Using only one biometric trait in Authentication systems may not provide all the properties mentioned above. We use more than one biometric trait (similar / different) simultaneously which is called as multi biometric, provides more robust. There are a number of other sources that can be combined to enhance security in the case of combining different biometric modalities. In this paper shows the basic biocrypto system technique used in most of the research. This shows the cryptosystem which stores a key and will be released once the verification is done. Cryptography methods is combined with multibiometric which enhances the security of privacy data. [ABSTRACT FROM AUTHOR]

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]

Solar cells have been widely used for offering energy for Internet of Things (IoT) devices. Recently, solar cells have also been used as sensors for context awareness sensing due to their sensitivity to varying lighting conditions. In this article, we are the first to use solar cells for symmetric key generation. To generate symmetric keys, we take advantage of photovoltage measurements generated from solar cells equipped with a pair of IoT devices. Symmetric keys are essential for pairing IoT devices and further securing wireless communication. Despite the sensitivity to varying lighting conditions, challenges still remain for the use of solar cells for key generation, such as time unsynchronisation and noisy measurements. To solve these challenges, we design a novel key generation framework, SolarKey, which includes the starting point detection and a compressed sensing-based two-tier key reconciliation method. Extensive experiments have been conducted to evaluate the performance of our proposed key generation method in various environments, which shows the proposed method can improve the key matching rate by up to 25%.We also conduct security analysis and the randomness test, which shows that SolarKey is resilient to common attacks such as the eavesdropping attack and the imitating attack and sufficiently random. [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).

Before users store data in the cloud, many security issues must be addressed, as they will have no direct control over the data that has been outsourced to the cloud, particularly personal and sensitive data (health, finance, military, etc.). This article proposes a system based on chaotic maps for private key generation. A hybrid encryption for fast and secure cryptography. In addition to a multi-cloud storage with Pseudonymized file names to preserve user data privacy on the cloud while minimizing data loss. As well as a hash approach to check data integrity. AES in combination with RSA and fragmenting the file is used for the encryption. Integrity is cheeked using SHA-3. The experiments demonstrated that the key generation strategy may provide efficient keys in a shorter amount of time. Furthermore, the keys for the proposed approach passed NIST randomness testing. When cipher text fragmentation and encryption are combined, the average entropy value was (7.99). In addition, as file sizes grew, the total system's execution time increased only slightly. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing is considered as the most fabulous paradigm to accommodate various kinds of user information. However, the privacy conflicts integrated with computational complexities need more hybridized resource efficiency for hassle‐free accession. For this purpose, this article presents a cloud assisted, secured and privacy preserved protocol based on the elliptical curve cryptography (ECC) and blockchain consortium. Blockchain provides an innovative approach for storing information, establishing trust, and various other transactions in an open platform. There exists no single technology as a panacea to obtain optimum privacy and security in the complex cloud environment that needs several desired characteristics. Hence, an elite integration of multiple cryptographic methods by carefully analyzing the potential harms and pitfalls has to be framed to balance the trade‐off between privacy and security. The proposed technique highlights the user privacy preservation through guaranteeing that user information is safeguarded from unauthorized use or access. In accordance with that the present study enrolled the advantages of the ECC algorithm, vectorization, and blockchain methods to rule out the limitations of state of art methods. This article attempts to provide the most required privacy with optimum key generation, encryption, and decryption time. The present study has obtained optimal outcomes and the maximum percentage reduction w.r.t the existing method in key generation time, encryption time, and decryption time is given as 14.89%, 16.67%, and 12.5%, respectively. [ABSTRACT FROM AUTHOR]

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]

Summary: Improvement of privacy and security in data centers is challenging with proficient safety key managing. So as to resolve this issue, data centers require proficient quantum cryptographic techniques with authentication methods that are suitable to improve the privacy and security with lesser intricacy. In addition, quantum cryptography (QC) offers maximal security with lesser complication, which raises the security strength and storing capability of big data. This work intends to introduce a QC oriented data security model in cloud via electing the optimal private key. Here, attribute based encryption (ABE) is performed for ensuring better data transmission among the nodes in cloud. For key generation in ABE, improved QKD is adopted in this work. Depending upon this, the decryption and encryption is done. Moreover, for optimal secret key selection in ABE, self‐modified Aquila optimization (SM‐AO) scheme is deployed. Further, analysis is done depending upon varied metrics. [ABSTRACT FROM AUTHOR]

In wireless body area network health care applications, energy-constraint wearable devices are used to monitor patient physiological parameters. The security of the private health information of a person plays a significant role because if it is captured and read by an unauthorized person, the confidentiality of the patient data is lost. Therefore, there is a requirement to secure the data by performing encryption to transfer it into an unreadable form. Since the resources used for encryption should be kept to a minimum as the devices are attached to the human body, a lightweight encryption algorithm has to be used. Therefore, the generation of a unique key used for encryption plays a significant role. In work, generating a unique key uses the ECG values taken from MIT-BIH Arrhythmia database. Four unique keys are generated, which can be used for encryption. The uniqueness and randomness of the keys generated are proved using the runs test and frequency test within the block. Also, the average hamming distance calculated between the ECG keys generated from two different ECG signals is 62.5% (≈ 80 bits), which proves the distinctiveness of the keys generated. Implementation of the work is performed using Matlab. [ABSTRACT FROM AUTHOR]

• Development of a universal key generation mechanism with EEG signals using autoencoders to extract the biometric features. • A result of 0% of False Acceptance Rate in the proposed mechanism (an attacker will not be able to generate any key). • A key generation period of half a second, without needing to store the key or biometric information related to the user. • The creation of a database with real-world EEG signals of 39 volunteers to test the key generation mechanism. Nowadays, a rapid, easy, and convenient access to our private information is essential to carry out both personal and professional activities. In most cases, this information is sensitive and can be stolen due to its importance and the lack of security protocols. In this study we propose a time–invariant cryptographic key generation mechanism based on electroencephalogram (EEG) signals. We employed Discrete Wavelet Transform and autoencoders to extract the biometric features from the EEG signals. Using these features, we construct a scheme to generate secure seeds that can be used as inputs for secure hash functions and obtain cryptographic keys. The mechanism proposed preserves the privacy of the user, as the cryptographic key is generated for each new EEG signal received, avoiding the need of storing the key, previous EEG signals or any other information. Results show that the proposed mechanism is secure against random attacks, as a 0% of False Acceptance Rate is reported, while generating seeds with an entropy of 0.968 in less than 500 ms. [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]

Cyber Physical System (CPS) plays an important role in industry 4.0 applications such as smart factories, smart energy, smart transportation, smart buildings, smart healthcare, etc. Similarly, Cyber Physical Sensor System (CPSS) has gained popularity in recent times and is composed of a computing platform linked to an actuator, sensor, and wireless access point. In real-time scenarios, CPSS continuously gathers data from physical objects and conducts real-time control events based on the process algorithm. Then, the gathered data is transferred to the control centre or cloud services via network layer for further processing. In this scenario, there exists a need to identify the way of utilizing the intellect correctly, by designing effective data sensing and fusion schemes for CPSS. With this background, the current paper presents a Deep Learning with Metaheuristics based Data Sensing and Encoding (DLMB-DSE) scheme for CPSS. The aim of the proposed DLMB-DSE technique is to present a prediction-based data sensing and fusion approach to reduce the quantity of data communication and maintain maximum coverage by ensuring security. DLMB-DSE technique involves the design of Optimal Deep Belief Network (DBN) with Adagrad optimizer to primarily predict the data of the succeeding period with minimum number of data items. It also helps in making the primary predicted value, estimate the actual value, with maximum accuracy. Besides, Multi-Key Homomorphic Encryption (MKHE) technique is also applied for useful data encoding and decoding processes, thereby accomplishing security. Moreover, the novelty of the study lies in optimal key generation process, followed in MKHE technique, using Equilibrium Optimizer (EO). This helps in improving the security. A wide range of experiments was implemented to validate the better performance of the proposed DLMB-DSE technique. The experimental results exhibit the promising performance of DLMB-DSE approach over other methods under different measures. [ABSTRACT FROM AUTHOR]