Decryption Time Research Articles

Auditable and dynamic access control scheme with behavior and identity tracing

Ciphertext policy attribute-based encryption (CP-ABE) is a potential solution to security sharing issues since it offers one-to-many encryption and fine-grained access control. To prevent users from disclosing their access permissions, traceable CP-ABE schemes are emerging. However, the current traceable CP-ABE schemes still have some security problems, such as impromptu attribute retracting, unreliable tracking, and easy disclosure of access policy privacy, making it essential to implement dynamic data security sharing and malicious user auditing. To address these issues, we propose an auditable and dynamic access control scheme with behavior and identity tracing. The scheme first uses an access tree with hidden leaf nodes to express the access policy, improving the flexibility and security of access control. Secondly, the Blockchain and InterPlanetary File System (IPFS) frameworks perform the audit authentication and store the ciphertext, policy, and public keys to avoid tampering with shared data. Furthermore, to prevent users from leaking their decryption keys, a tracing algorithm based on white-box traceability is developed. On this basis, an authority update algorithm is proposed to revoke the malicious user’s permission immediately and update the decrypted key for unrevoked users. An auditable smart contract based on blockchain is proposed to prevent user denial. Finally, theoretical analyses prove that the proposed scheme not only achieves traceability, revocation, authority updating, policy hiding, and audit, but also guarantees security under a chosen plaintext attack model. Experiments demonstrate that our proposal reduces the key generation time by 22.6%, the encryption and decryption time by 39.2% and 59.8%, and the storage costs of ciphertext and private key are reduced by 12.1% and 5.6% over existing schemes.

Quantum Cryptography for Enhanced Data Security: A Comparative Survey of Data Encryption and Decryption of Error Correction

Data information security is a crucial concern that ought to be managed to help protect vital data. Cryptography is one of the conventional approaches for securing data and is generally considered a fundamental data security component that provides privacy, integrity, confidentiality, and authentication. In this paper, a comparative survey of Rivest-Shamir-Adleman (RSA) data security algorithm is proposed in order to compare their security strength. In doing so, we integrate traditional RSA and Gaussian Interpolation formulas to test their security level. The integration raised the security strength of RSA to the fifth degree,while the Gaussian First Forward Interpolation was used to encrypt the ASCII values of the message after which the traditional RSA was used to encrypt and decrypt the message in the second and third levels. Comparative data security analysis was performed using four different algorithms; RSA, SRNN, 2-Key pair algorithms, and the results showed that when the data size was small, the encryption and decryption times were lower for the proposed algorithm but higher when the data size was big. Thus the two public keys used and some mathematical relations made the eavesdropper not to get much knowledge about the key and therefore, unable to decrypt the message.

FTPE-BC: Fast thumbnail-preserving image encryption using block-churning

With the boom in personal images, cloud services are beginning to take on a significant role in image storage. However, images uploaded to the cloud will face new privacy threats. Image privacy can be effectively protected using traditional encryption methods, but the encrypted images are snowflake-like noisy images, which makes the images unusable. Lately, Thumbnail Preserving Encryption (TPE) is proposed, which relieves this conflict between image availability and privacy in cloud platforms by retaining thumbnails unchanged after encryption. Existing TPE schemes for intra-block grouping usually utilize a lot of pseudo-random functions with high computational complexity, which makes encryption and decryption time too long. We propose a new model, block-churning, and propose a new TPE scheme based on this model. In this scheme, all pixels in the block are churned as a whole. This fundamentally solves the problem of low encryption efficiency caused by intra-block grouping, and greatly improves the encryption speed, while being able to change pixel values more significantly in blocks. The experiment shows that the encrypted image achieves a good balance between availability and privacy.

Enhancing healthcare in the digital era: A secure e-health system for heart disease prediction and cloud security

In the modern world, due to changing lifestyles, more people are infected by deadly diseases, especially heart disorders. An early prognosis is the only possible way of increasing survival rate. One of the key elements of this context is the healthcare digitalization that is carried out through Internet of Things (IoTs) and cloud computing. However, main issue in cloud with IoT is false diagnosis, which leads to cause a major impact on patient’s life. Moreover, data communication through cloud servers can be compromised by attackers due to security issues. Therefore, to overcome these issues, this paper proposes a novel secure e-healthcare system with dual objectives- accurate disease prediction and improved cloud security. A novel diagnosis approach ‘Hybrid Binary Particle Firefly Optimized Extreme Learning Machine classifier (HybBPF-ELM)’ to predict HD is designed, that recognize the subject with HD abnormality from the normal ones. The prediction accuracy and time efficiency of training process are improved by the adoption of a Hybrid Binary Particle Firefly Optimizer (HBPFO) through fine-tuning weight and bias of Extreme Learning Machine (ELM) classifier. Additionally, a new intelligent encryption and decryption framework ‘IEDF’ is introduced for cloud security. It combines four encryption algorithms, including Advanced Encryption Standard (AES), Data Encryption Standard (DES), Rivest- Shamir-Adleman (RSA), and Modified Blow Fish (MBF) to enhance cryptographic strength and key security. Along with this, an Automatic Sequence Encryption (ASC) for data block encryption is employed to ensure strong security and network performance. The integration of HD prediction module and cloud security framework within e-healthcare system assists healthcare experts in securely storing and transferring data for advanced diagnosis. The results show that our proposed system achieves an overall prediction accuracy of 99.36 % and less processing time for encryption and decryption of 127.55 s and 452.01 s at 2.2 GB file size respectively.

PENERAPAN ADVANCED ENCRYPTION STANDARD UNTUK SISTEM LOGIN DAN REGISTRASI PADA SISTEM INFORMASI PENJUALAN (STUDI KASUS:TOKO ILHAM BANJAR)

Data security in sales information systems has become a critical priority in today's digital era. Ilham Banjar Store needs a system that can effectively protect user data, especially in login and registration features, which are vulnerable to security threats. The lack of encryption mechanisms in the current system results in user data, such as passwords, being transmitted in plaintext, increasing the risk of unauthorized access and data modification. Therefore, this study aims to enhance the security of the Ilham Banjar Store's sales information system by implementing the Advanced Encryption Standard (AES) 256-bit Cipher Block Chaining (CBC) method in the login and registration processes. The research method used is Research and Development (RD), consisting of stages such as literature review, needs analysis, system design, implementation, testing, evaluation, and analysis. The implementation of the Advanced Encryption Standard (AES) involves the encryption processes of AddRoundKey, SubBytes, ShiftRows, and MixColumns, and the decryption processes are the inverse of these encryption steps. Testing includes measuring encryption and decryption times as well as verifying data integrity using HMAC (Hash-Based Message Authentication Code). The research results show that the performance testing indicates encryption and decryption times remain within acceptable limits, and the encrypted and decrypted data maintain their integrity. Thus, the new system meets both security and performance requirements.

Blockchain-Enabled Secure Data Sharing with Honey Encryption and DSNN-Based Key Generation

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

Performance Evaluation of Laguerre Transform and Neural Network-based Cryptographic Techniques for Network Security

As the world evolves day by day with new technologies, there is a need to design a secure network in such a way that intruders and unauthorized persons should not have access to the network as well as information regarding the personnel in any firm. In this study, a new cryptographic technique for securing data transmission based on the LaplaceLaguerre polynomial (LLP) is developed and compared to an existing auto-associative neural network technique (AANNT).The performance of the LLPT and AANNT was tested with some selected files in a MATLAB environment and the results obtained provided comparative information (in respect of AANNT versus LLPT) as follows: encryption time (1.67 ms versus 3.9931s), decryption time (1.833 ms versus 2.1172s), throughput (26.2975 Kb/s versus 0.01098 Kb/s), memory consumption (3.349 KB versus 15.958 KB). From the compared results, it shows that AANNT offers a faster processing time, higher throughput, and takes up less memory space than the LLPT. However, cryptanalysis of the AANNT is possible if the network's weight and design are known; hence, the technique is unreliable for ensuring the data integrity and confidentiality of encrypted data. The proposed LLP cryptographic algorithm is designed to provide a higher security level by making the LLP algorithm computationally tedious to invert using the standard Laplace transform inversion method. When compared to the AANN-based cryptographic technique, cracking the algorithm to uncover the encryption key takes time. This shows the strength and robustness of the proposed LLP cryptographic algorithm against attacks, as well as its suitability for solving the problem of data privacy and security when compared to the AANN-based cryptographic algorithm.

Deep Siamese domain adaptation convolutional neural network-based quaternion fractional order Meixner moments fostered big data analytical method for enhancing cloud data security

ABSTRACT In this paper, Quaternion Fractional Order Meixner Moments-based Deep Siamese Domain Adaptation Convolutional Neural Network-based Big Data Analytical Technique is proposed for improving Cloud Data Security (DSDA-CNN-QFOMM-BD-CDS). The proposed methodology comprises six phases: data collection, transmission, pre-processing, storage, analysis, and security of data. Big data analysis methodologies start with the data collection phase. Deep Siamese domain adaptation convolutional Neural Network (DSDA-CNN) is applied to categorize the types of attacks in the cloud database during the data analysis process. During data security phase, Quaternion Fractional Order Meixner Moments (QFOMM) is employed to protect the cloud data for encryption with decryption. The proposed method is implemented in JAVA and assessed using performance metrics, including precision, sensitivity, accuracy, recall, specificity, f-measure, computational complexity information loss, compression ratio, throughput, encryption time, decryption time. The performance of the proposed method offers 23.31%, 15.64%, 18.89% better accuracy and 36.69%, 17.25%, 19.96% less information loss. When compared to existing methods like Fractional order discrete Tchebyshev encryption fostered big data analytical model to maximize the safety of cloud data depend on Enhanced Elman spike neural network (EESNN-FrDTM-BD-CDS), an innovative scheme architecture for safe authentication along data sharing in cloud enabled Big data Environment (LZMA-DBSCAN-BD-CDS).

Auto-metric Graph Neural Network for Attack Detection on IoT-based Smart Environment and Secure Data Transmission using Advanced Wild Horse Standard Encryption Method

Smart cities (SCs) are being constructed with the huge placement of the Internet of Things (IoT). Real-time enhancements to life quality based on comfort and efficiency. The key concerns in most SCs that immediately impact network performance are security and privacy. Numerous approaches are proposed for secure data transmission, but the current methods do not provide high accuracy and it provide high computational time. To resolve these problems, an Auto-metric Graph Neural Network for Attack Detection and Secure Data Transmission using Optimized Enhanced Identity-Based Encryption in IoT (AGNN-AWHSE-ST-IoT) is proposed. Primarily, the input data is taken from the NSL-KDD dataset. The input data is gathered with the aid of NSL-KDD is pre-processed using three steps, crisp data conversion, splitting, and normalization. Then the Pre-processed input is fed into the Colour Harmony Algorithm (CHA) based feature selection to select the important features. After feature selection, the preferred features are given to the AGNN classifier. After classifying, the data is given to Enhanced Identity-Based Encryption (EIBE), and it is optimized using Wild Horse Optimizer (WHO) for transmitting the data more safely. The outcomes of the normal data are displayed using the LCD monitor. The AGNN-AWHSE-ST-IoT method is implemented in PYTHON. The AGNN-AWHSE-ST-IoT method attains 8.888%, 13.953%, 19.512% higher accuracy, 2.105%, 6.593%, 8.988% higher cumulative accuracy, 54.285%, 54.285%, 52.941% lower encryption time, 8.2%, 3.3%, 6.9% lower decryption time, 11.627%, 10.344%, 6.666% higher security level and 60.869%, 70% and 64% lower computational time than the existing approaches such as SBAS-ST-IoT, BDN-GWMNN-ST-IoT and DNN-LSTM-ST-IoT respectively.

Implementation of Hybrid Encryption Algorithm

This paper presents an analysis of a message encryption scheme that harnesses the strengths of both the RSA and AES algorithms. Through a detailed comparison of RSA and AES in terms of encryption and decryption time, security, key management, and key length, we developed a hybrid scheme that leverages the speed advantage of AES for encryption operations and the stability and key management strengths of RSA. Our analysis demonstrates how the integration of these two algorithms can enhance overall security and performance, effectively combining their respective advantages.  Motivation There are various applications using client- server mechanism but the communication channel is not secure that much and existing methods are not so efficient thus there is need to come up with an efficient and better approach.  Objective Our objective through this project is to constructa hybrid algorithm which is capable of overcoming the disadvantages of the existing methods and provide us with muchbetter approach in terms of various parameters.

TRA-PS: Accountable data Pub/Sub service with fast and fine-grained controllable subscription

The Cloud-based data publish/subscribe (Pub/Sub) service presents a selective manner for publishers and subscribers to share and receive data, where confidentiality and authorized access to the data are the fundamental security properties. Therefore, the methodology of attribute-based keyword search (ABKS) was applied that recently considered bilateral access control between the publisher and subscriber. However, existing data Pub/Sub solutions fail to take into account the following features: (i) the efficient revocation of subscribers; (ii) the traceability and auditability of subscriptions; (iii) the excessive time cost of decryption procedure. Therefore, we propose TRA-PS, an accountable data Pub/Sub service that implements efficient revocation, traceability, and public auditability for subscriptions. Technically, we refine the secret key of subscriber in the classic ABKS schemes with associating its identity information and binding accountability module. In addition, we implement TRA-PS and evaluate its practical performance compared to state-of-the-art work in the real cloud environment. Besides achieving accountable property, TRA-PS significantly diminishes the decryption time cost (number of attributes/keywords is 25) by 91.8%, independent of the number of attributes/keywords.

COMPARATIVE OF RIVEST-SHAMIR-ADLEMAN CRYPTOSYSTEM AND ITS FOUR VARIANTS USING RUNNING TIME AND MEMORY CONSUMPTION ANALYSIS

The Rivest-Shamir-Adleman (RSA) algorithm, known for its slow single-precision multiplication (spm) and overall running time, is not commonly employed to encrypt user data directly. As a result, several researchers have developed various RSA-based cryptosystems to enhance the algorithm's performance while maintaining security. This paper presents a comparative analysis of different variants of the RSA cryptosystem, focusing on five specific cryptosystems: RSA, Somsuk-RSA, Modified-RSA (MRSA), Easy Simple Factoring-RSA (ESF-RSA), and Phony-RSA. The methodology involves evaluating the theoretical running time and memory usage through single-precision multiplication (spm) measurements, while the actual running time is estimated using Maple programming. The research has two primary objectives. Firstly, they examined each algorithm of the RSA variants and analysed them according to the proposed methodology. Secondly, to determine which cryptosystem consumes the most time and memory for key generation, encryption, and decryption. The results indicate that ESF-RSA and RSA are the fastest in terms of key generation, ESF-RSA is the quickest for encryption, and Phony-RSA excels in decryption speed. Additionally, ESF-RSA demonstrates the lowest memory usage, whereas MRSA requires the highest memory allocation for all processes.

Large file encryption in a reduced-round permutation-based AES file management system

In the rapid evolving digital landscape, the imperative to ensure data security has never been more crucial. This paper addresses the pressing challenges in data security by introducing a file encryption management system, leveraging a modified advanced encryption standard (AES) algorithm with reduced round iterations and bit permutation. This system aims to comprehensively secure various file types, providing a dependable solution for file exchange. Our findings reveal substantial improvements in both encryption and decryption processes using the reduced-round permutation-based AES (RRPBA). The adapted algorithm demonstrates a significant 38.8% acceleration in encryption time and a remarkable 44.86% improvement in decryption time, positioning it as a pivotal component for efficient file operations within the management system. Moreover, the throughput assessments showcase a remarkable 33.73% improvement in encryption and 23.72% in decryption, outperforming the original AES, emphasizing the algorithm's superior computational effectiveness, signaling positive implications for future high-performance applications. In conclusion, the study not only addresses critical security challenges but also presents a viable solution with tangible speed advantages for file encryption and decryption processes within digital file management systems.

Comparative Analysis of AES, Blowfish, Twofish, Salsa20, and ChaCha20 for Image Encryption

Nowadays, cybersecurity has grown into a more significant and difficult sci-entific issue. The recognition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guar-antees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an answer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryption standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this research is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to compare the outcomes of the techniques evaluated in this research. The as-sessment focused on processing time and speed parameters, examining visual encoding and decoding using Java as the primary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryption and decryption, being over 50% faster than some other algorithms. However, the Twofish algorithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

A secured cloud‐medical data sharing with A‐BRSA and Salp ‐Ant Lion Optimisation Algorithm

AbstractSharing medical data among healthcare providers, researchers, and patients is crucial for efficient healthcare services. Cloud‐assisted smart healthcare (s‐healthcare) systems have made it easier to store EHRs effectively. However, the traditional encryption algorithms used to secure this data can be vulnerable to attacks if the encryption key is compromised, posing a security threat. A secured cloud‐based medical data‐sharing system is proposed using a hybrid encryption model called A‐BRSA, which combines attribute‐based encryption (ABE) and B‐RSA encryption. The system utilises the Salp‐Ant Lion Optimisation Algorithm for optimal key selection. The encrypted data is stored in the cloud and transmitted to the recipient, where it is decrypted using A‐BRSA‐based decryption. The study measures turnaround time, encryption time, decryption time, and restoration efficiency to evaluate the system's performance. The results demonstrate the effectiveness of the A‐BRSA model in ensuring secure medical data sharing in cloud‐based s‐healthcare systems.

Performance Evaluation of Genetic Algorithm-Driven Blockchain Encryption for EHR Management and Validation

In the realm of electronic health record (EHR) management, ensuring robust security and validation mechanisms is paramount due to the sensitive nature of healthcare data. This research focuses on the performance evaluation of a genetic algorithm-driven blockchain encryption approach for enhancing EHR security and validation. The proposed method leverages genetic algorithms to optimize encryption parameters within a blockchain framework, aiming to safeguard patient privacy and prevent unauthorized access. By integrating advanced cryptographic techniques like Elliptic Curve Cryptography (ECC) and Keyed-Hash Message Authentication Code (HMAC)-based authentication, along with machine learning for data classification. The evaluation of the approach holds significant promise in advancing secure EHR management practices, addressing critical challenges in data privacy and integrity within healthcare environments. Finally, as a result, this study presents a comparative analysis of cryptographic systems genetic algorithm-driven blockchain encryption (GADBE)+ECC and GADBE+ Advanced Encryption Standard (AES), focusing on the scaling of encryption and decryption times relative to key sizes and data volumes. Results show that both systems exhibit increasing times with larger key sizes and data sizes. ECC consistently demonstrates superior speed over AES, with decryption times ranging from 0.4 to 3.5 seconds for key sizes from 128 to 512 bits, indicating potential performance advantages of ECC in cryptographic applications.

Authorization of Aadhar data using Diffie Helman key with enhanced security concerns

In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time.

Securing cloud data using secret key 4 optimization algorithm (SK4OA) with a non-linearity run time trend.

Cloud computing alludes to the on-demand availability of personal computer framework resources, primarily information storage and processing power, without the customer's direct personal involvement. Cloud computing has developed dramatically among many organizations due to its benefits such as cost savings, resource pooling, broad network access, and ease of management; nonetheless, security has been a major concern. Researchers have proposed several cryptographic methods to offer cloud data security; however, their execution times are linear and longer. A Security Key 4 Optimization Algorithm (SK4OA) with a non-linear run time is proposed in this paper. The secret key of SK4OA determines the run time rather than the size of the data as such is able to transmit large volumes of data with minimal bandwidth and able to resist security attacks like brute force since its execution timings are unpredictable. A data set from Kaggle was used to determine the algorithm's mean and standard deviation after thirty (30) times of execution. Data sizes of 3KB, 5KB, 8KB, 12KB, and 16 KB were used in this study. There was an empirical analysis done against RC4, Salsa20, and Chacha20 based on encryption time, decryption time, throughput and memory utilization. The analysis showed that SK4OA generated lowest mean non-linear run time of 5.545±2.785 when 16KB of data was executed. Additionally, SK4OA's standard deviation was greater, indicating that the observed data varied far from the mean. However, RC4, Salsa20, and Chacha20 showed smaller standard deviations making them more clustered around the mean resulting in predictable run times.

Integration of Ethereum Blockchain with Cloud Computing for Secure Healthcare Data Management System

Effective management of health data is critical in this age of digital change. In order to solve the issues with safe healthcare data systems, this paper suggests a brand-new Hybrid Healthcare Data Management System (HDMS) that seamlessly combines blockchain and cloud computing technology. The solution guarantees the safe storage and effective administration of health data by utilizing the scalability and flexibility of cloud computing. The Ethereum blockchain, which offers immutability through smart contracts for data integrity assurance, is used to improve security. Important components of the suggested approach include Blockchain Anchoring, which uses distinct hash references on Ethereum to ensure data integrity, Google Cloud Integration for scalable storage and immediate data access, and compliance with Health Level 7 (HL7) formatting criteria for medical data storage. Robust privacy safeguards include cutting-edge methods like decentralized identifiers (DIDs) and homomorphic encryption, which guarantee secure computations on encrypted data and trustworthy identification for allowed access. IPFS file storage is used by the system to improve security by providing redundancy and resilience. By utilizing blockchain's immutability and cloud storage's distributed architecture, HDMS demonstrates resilience against disturbances. Results of the proposed method is implemented in Python Software. The Homomorphic approach that has been suggested continuously beats Optimized Blowfish Algorithm (OBA) in terms of encryption and decryption times. The improvement in encryption is between 3000ms (10 kb) and 1350ms (40 kb). The enhancement demonstrates improved efficiency across data sizes, ranging from 4000ms (10 kb) to 3350ms (40 kb) in decryption. The goal of HDMS is to create a future in which patient outcomes are enhanced by the appropriate handling, storage, analysis, and use of medical data.

DynAuthRoute: Dynamic Security for Wireless Sensor Networks

Objectives: The research aims to design an architecture for secure transmission of data in wireless sensor networks. Methods: The method involves three main pillars: authentication, data encryption, and dynamic routing. Extensive simulations have been conducted to evaluate the suggested method in terms of energy consumption, memory footprint, packet delivery ratio, end-to-end latency, execution time, encryption time, and decryption time. Findings: For authentication, a dynamic key is used to power an improved salt password hashing method. Data encryption is performed using format-preserving encryption (FPE) with the appended salt key. Dynamic routing is implemented using a cluster-based routing technique to enhance network efficiency in terms of power consumption and security. The execution time for MD5 ranges from 15 to 22 milliseconds, while for SHA-1 it ranges from 16 to 23 milliseconds and for the proposed salt key generation it is 1 to 5 milliseconds. Similarly, in terms of energy consumption, memory footprint, packet delivery ratio, end-to-end latency, execution time, encryption time, and decryption time the proposed method shows promising results in ensuring the integrity and security of transmitted encrypted data. Novelty: The presents a novel architecture with enhanced cluster head-based selection algorithm that combines dynamic key-based authentication and secure data routing to establish a safe environment for data transmission in wireless sensor networks. This research works offers a method for encrypting text with a dynamic salt key that is safe, energy-efficient, and lightweight. Keywords: Wireless Sensor Network, Dynamic Key, Authentication, Hash function, Salt algorithm, Dynamic routing, Node clustering, Format-preserving encryption