Blowfish Encryption Algorithm Research Articles

Adoption of Blockchain Technology for Data Management of Human Resource Demands in Organizational Enterprises

Human resource data accuracy has become a significant factor in assessing the effectiveness and performance of human resource management (HRM) in businesses. Numerous human resource hazards originating from asymmetric information continue to cost firms money and even put companies out of business despite the rapid progress of mobile technology and Internet technology. Blockchain is a transaction platform that makes use of the immutability qualities of immutable data records. Because of the dispersed nature of this technology, it has a broad variety of applications in numerous industries. Seeing the potential of this new technology, we picked the HRM sector since this data must be kept private and secret while still having great research value. This research presents a unique blockchain-based encryption method for establishing an HRM data method that decreases the danger of HRM data integrity. The data is authenticated using smart contracts, and data is encrypted using the proposed Improved Identity-based blowfish encryption algorithm (IIBEA) with Particle Swarm Optimization (PSO). New blocks are verified using the Proof of Work (PoW) consensus process. The suggested model's metrics are examined and compared to traditional encryption approaches. This model solves the lack of difference in the authenticity of HRM information, and it will give real and effective information to the HRM of organizational companies.

A secured energy aware resource allocation and task scheduling based on improved cuckoo search algorithm and deep reinforcement learning for e-healthcare applications

Healthcare industries have begun using modern technologies and tools like cloud computing these days. When cloud computing and assisted technologies are used in the healthcare domain the storage cost improves along with the quality and time availability. The present work makes use of cryptography based architecture for improving the overall security aspects. However, the tasks of the user take a longer duration for the execution to complete when the needed resources are unavailable on the main server. ICSA (Improved Cuckoo Search Algorithm) with DRL (Deep Reinforcement Learning) to solve this issue. The main goal of this work is to increase overall security and reduce execution times of jobs. The objective also includes using underutilized resources. Scheduling jobs, Binary In-order Traversal Trees with weights are used. Subsequently, DRL algorithm has been used to decrease the space complexity by splitting the individual resources. There will be idle resources in a state space that are used in allocation of tasks. The scheduled tasks will then do a search on the resources based on the ICSA algorithm. The server will then distribute the resources to the action area after an optimal resource has been chosen and assigned to the job. DRL for resource allocation so that the inactive resource usage is minimized and the execution time is reduced. Finally, MTAC-IBEA (multi tenant authentication control with improved blowfish encryption algorithm) for decrypt health data. The outcomes of simulation demonstrate that the suggested method offers optimum work schedules, resource allocations, and security in e-healthcare systems.

Stochastic Gradient Descent long short-term memory based secure encryption algorithm for cloud data storage and retrieval in cloud computing environment

With the increasing rise of distributed system technologies, one of the most pressing problems facing the digital world is ensuring the security of sensitive and confidential data during transport and storage, which is also regarded as one of the most critical difficulties facing cloud computing. Numerous techniques exist for enhancing data security in the cloud computing storage environment. Encryption is the most important method of data protection. Consequently, several accessible encryption strategies are utilized to provide security, integrity, and authorized access by employing modern cryptographic algorithms. Cloud computing is an innovative paradigm widely accepted as a platform for storing and analysing user data. The cloud is accessible via the internet, exposing the data to external and internal threats. Cloud Service Providers (CSPs) must now implement a secure architecture to detect cloud intrusions and safeguard client data from hackers and attackers. This paper combines Stochastic Gradient Descent long short-term memory (SGD-LSTM) and Blow Fish encryption to detect and prevent unauthorized cloud access. User registration, intrusion detection, and intrusion prevention are the three phases of the planned system. The SGD-LSTM classifier predicts cloud data access and prevents unauthorized cloud access. In the data access phase, cloud data access is managed by authenticating the authorized user with the Blowfish encryption algorithm. Comparing the proposed classifier to existing classifiers demonstrates that it detects abnormal access accurately. The experimental outcomes enhanced data security, which can be utilized to protect cloud computing applications. The experimental results of the suggested SGD-LSTM algorithm indicated a high level of protection, as well as a considerable improvement in security and execution speed when compared to algorithms that are often used in cloud computing.

A Concentrated Energy Consumption Wireless Sensor Network by Symmetric Encryption and Attribute Based Encryption Technique

Wireless sensor networks (WSNs) are increasingly used in a wide variety of settings, including defence, industry, healthcare, and education. Hundreds or even thousands of sensor nodes are spread out across a given area and linked to a central Base Station (BS) in order to keep tabs on the environment. The BS then sends the data out to the users over the internet. The sensor network's adaptability, portability, dependability, and quickness are driving its widespread use across industries. The suggested SHS evaluates the efficiency of well-established symmetric algorithms to see where it stands in the spectrum of security. The Blowfish encryption algorithm was proven to require the least amount of processing power after extensive benchmarking. Therefore, the Blowfish algorithm is selected to protect sensitive medical information. The medical database receives the encrypted health records. Only those with proper permissions should be able to access them. Therefore, the CP-ABE is implemented to regulate access to patient records. The SHS's results on the dataset are compared to those of other existing systems. With SHS, health data may be transmitted to doctors rapidly and securely because it requires less computing time and energy. In addition to these benefits, SHS also offers privacy, authentication, and authorization.

SDA-RDOS: A New Secure Data Aggregation Protocol for Wireless Sensor Networks in IoT Resistant to DOS Attacks

In a typical Wireless Sensor Network (WSN), thousands of sensor nodes can be distributed in the environment. Then, each sensor node transmits its detected data to the base station with the help of cooperation. In this type of network, data aggregation protocols are used to increase the network’s lifetime and reduce each sensor node’s communication load and energy consumption. With Data Clustering, the density of data circulating in the network is reduced, thus increasing the network’s life. Energy, delay, and efficiency are essential criteria in Data Clustering; however, security is another crucial aspect to be considered. A comprehensive solution for secure data clustering has yet to be seen when the literature is examined. In the solutions developed, data availability, which means that the WSN is resistant to Denial of Service (DOS) attacks, has been neglected too much, even though confidentiality, integrity, and authentication are met with different algorithms. This study developed a comprehensive, secure clustering protocol by considering all security requirements, especially data availability. The developed protocol uses the blowfish encryption algorithm, EAX mode, and RSA algorithm. The proposed protocol was theoretically analyzed, empirically evaluated, and simulated from many perspectives. Comparisons were made with LSDAR, SUCID, and OOP-MDCRP protocols. As a result of the study, a comprehensive security solution is provided and more successful results were obtained according to Energy Efficiency, Network Lifetime, Average Delay, and Packet delivery ratio criteria.

A Review of the Blowfish Algorithm Modifications in Terms of Execution Time and Security

Data has become increasingly popular for advanced digital content transmission. Researchers are concerned about the protection of data. The transmission of digital data over a network has made multimedia data vulnerable to various threats, including unauthorized access and network hacking. As a result, the data must be protected with encryption methods based on symmetric encryption algorithms, which will ensure the data security. The Blowfish encryption algorithm is one of the most well-known cryptographic algorithms. However, each of the current algorithms has its own set of advantages and disadvantages. However, there are several drawbacks to using this algorithm, including complex computational operations, fixed (S-Box) and pattern issues, which can arise while dealing with more complex data, including texts. Many academics have sought to increase the algorithm's efficiency. The modifications to the Blowfish algorithms provided by researchers in prior works are summarized in this publication.

Medical data quality management using butterfly optimization with adaptive threshold sensitive energy‐efficient routing protocol and multidimensional chaotic blowfish encryption in wireless body networks

AbstractNowadays, medical applications require much medical information to analyze various diseases. To collect various medical data, wearable devices collect patient health and medical details using a wireless body area network. The collected details consist of several critical details: oxygen level, heart attack information, blood pressure, airflow and so forth. These details are broadcast to the healthcare centers using wireless technologies to make clinical decisions. During medical information transfer, the data quality and critical events are difficult to maintain because it reduces the packet delivery, transmission delay, and high energy. So, this article introduces bacterial optimization and adaptive threshold‐sensitive energy‐efficient routing protocol (BOATSEE) to transmit the medical data. The method aggregates the sensitive data by selecting the cluster head and effectively broadcasts the critical data. In addition, the optimized method manages the network lifetime and energy using an energy‐efficient method. Also, we have proposed a multidimensional chaotic blowfish encryption (MCBE) algorithm to enhance the system's security. Then the system's efficiency is computed based on metrics like energy consumption, packet delivery ratio, end‐to‐end delay, and QoS metric‐associated restraints. The results reveal that the proposed system is efficient in managing the medical data quality with minimum energy utilization, packet loss rate, delay, and maximum packet delivery ratio when compared with the conventional approaches. Our method also proved to be more secure than the traditional systems.

Application of Digital Twin Combined with Artificial Intelligence and 5G Technology in the Art Design of Digital Museums

More and more museums are being established or restored as people’s cultural levels grow. A digital twin is a computerized representation of the physical object or phenomenon, which acts as its real-time digital equivalent. Artificial intelligence (AI) is becoming increasingly widespread, and museums and art shows will need to change their old working and thinking processes to fully fulfill their potential. In an increasingly digital world, combining AI technologies and wearable devices into digital museums could assist in boosting involvement. It is necessary to investigate AI’s strategic function and application in affecting tourist experiences at art galleries and museums, as well as its potential to improve education. Also, digital twin (DT) paves the way to alleviate difficulties or might improve access to digital museums via digital experiences. The historical report recommends that digital museum items be represented in a way that poses the fewest threats to its legitimacy. Owing to its uniqueness and vulnerability to substantial environmental dangers, the safeguarding of art and cultural heritage in digital museums is a serious issue. There are many approaches for assessing the state of artistic heritage and protecting it from extreme acts. The current study offers a framework for digitized museum art design based on the utilization of digital twin (DT), artificial intelligence (AI), and fifth-generation (5G) technologies. Further to enhance the security of the system, we employ the improved blowfish encryption algorithm (IBEA). Also, the authentication is done using the Diffie-Hellman protocol. The proposed system is simulated using MATLAB, and its performance is analyzed and compared with conventional systems. From the result obtained, it was revealed that the suggested methodology express high range of security level (95%) than the existing mechanisms. This study is very important in two directions: (1) it presents an analytical structure for having to adapt digital information to complicated systems in museums and (2) it explains the artistic obstacles for heritage properties, such as accessibility, execution time, security level, and the efficiency of 3-dimensional designs.

Implementation of modified blowfish using random number generator for quantum computers

Classical computers are quite safe from attacks by implementing traditional cryptographic techniques and algorithms but in future as quantum computers will come in full fledge, our current cryptographic algorithms will become vulnerable. In this paper authors have presented a modified blowfish scheme named as pseudo random generated blowfish encryption algorithm which can provide security to our data quantum computers also. Analysis of algorithm is done on classical computer as well as on quantum simulators (IBM qx) and result of comparison is also presented. The theoretical security measures are also discussed in this paper.

Health monitoring jeopardy prophylaxis model based on machine learning in fog computing

AbstractCloud‐based fog computing was developed in recent years to make it easier for patients to monitor distant health and discover mosquito‐borne syndromes at an initial period, allowing to track the mosquito‐borne illnesses. Many obstacles have been confronted by the technology, including high latency, mobility, overhead connectivity, and location consciousness. Due to the security issue and wrong prediction of the diseases, users are affected by getting wrong alert messages from fog which increase the disease spreading. Hence a novel Prevention Technique based on Fog Computing has been introduced to prevent the epidemic syndrome at an initial period and safeguard the people around the world. Initially, each user register their personal information in Pristine Database through IoT device. These information are encrypted with 16 round of key‐dependent operation via new Blowfish Encryption Algorithm. Subsequently, the mosquito spawned disease is diagnosed by measuring the resemblance factor between the user and disease through the Hybrid Endemic Halsen Classifier and the Resemblance Coefficient are used to diagnose and categorize users as infected or unaffected. The mosquito‐borne disease is detected using this method by assessing the similarity factor between the user and the disease with less computing time. As a result, if a new symptom is found rather than one from the given medical dataset, the information will be saved in the Pristine Database, and the classification process will be faster with less computing time in the future if the same symptom is identified. Finally, the novel Temporal Social Network Analysis was developed to assess the likelihood of a disease breakout also analyze the infected users and send the awareness message to take the precautionary measures in the cloud processing layer. Thus, the proposed work effectively predicts and prevents the spreading of epidemic outbreaks with accuracy of 97% which is 7.2% higher than the existing approaches, and reduced latency of 0.18 second.

Improving the security of the organization from the shadow IoT using Blow-fish encryption algorithm

Smart cities, traffic congestion, waste management, structural health, security, emergency services, logistics, retail, industrial control, and health care are just a few of the applications that the Internet of Things (IoT) can help with. The Internet of Things (IoT) is a mega-technology that can connect to anything, anybody, at any time, place, platform, and network. It has a significant impact on the entire blockchain of enterprises, smart objects, and devices, systems, and services provided by heterogeneous network connection (HNC) and is being developed as a smart pervasive framework for smart devices. Because IoT devices link to complicated equipment, interact with hostile surroundings and are deployed on a variety of unregulated platforms, they confront several security risks and challenges. Because the Internet of Things (IoT) has the capacity to integrate any sort of network or sophisticated system, it may be vulnerable to vulnerabilities inherent in the separate systems that make up the integrated network. The purpose of this research paper is to investigate the security issues that individual systems responsible for IoT interconnection face, as well as their impact on the overall IoT system.

Data Storage Security in Mobile Cloud Computing (MCC) using Improved Blowfish Algorithm

Mobile cloud computing (MCC) combines cloud computing and mobile computing to deliver vast computational resources to mobile consumers, network operators, and cloud computing providers. You may access your data from anywhere in the globe using any mobile device that is linked to the Internet. Cloud computing provides access to data in real-time whenever and wherever want. Any conventional mobile device can benefit from MCC's infrastructure, computational capacity, software, and platform services. Network security, web application security, data access, authentication, authorization, data confidentiality, and data breach are all concerns of MCC's security. Because mobile devices lack sufficient storage and processing power, their data storage capacity is limited. Users of mobile devices may inadvertently provide sensitive information over the network or through the application. Therefore, data security is the main concern for mobile device users. The objective of this paper is to find a solution that can enhance technical requirements with relation to user’s data security and privacy in mobile cloud computing. To achieve this improved blowfish encryption algorithm is used to encrypt each user’s data security and where the shared secret key is hash down using message digest called secured hash function. Hashing can increase the integrity and privacy of user data. The proposed algorithm is evaluated with a normal blowfish algorithm and 3DES with different parameters. Improved blowfish algorithm shows better performance than normal blowfish algorithm and 3DES. In this work, we have developed web-based application where the Amazon MySQL RDS database is used for data storage.

A hybrid chaotic blowfish encryption for high-resolution satellite imagery

In this article, we have designed a new information confidentiality mechanism based on the combination of Blowfish encryption algorithm along with Henon and Chen chaotic dynamical systems. We have authenticated our proposed encryption algorithm over satellite and other standard digital images of image processing. Our proposed encryption algorithm is equally deployed as an encrtpter and decrypter while at the sending and receiving ends in satellite communication. In satellite communication, typical wireless mediums are used for transferring heavy payloads and usual telemetry. The priciple aim of this article is to design a new mechanism for the privacy of digital contents mainly satellite images. We have authenticated our anticipated mechanism with security performace analyses.

Secured Message Broadcasting in VANET using Blowfish Algorithm with Oppositional Deer Hunting Optimization

Emergency Message broadcasting is an important process in VANET. Security and reliable transmission are the two major concerns in message broadcasting. VANET is open to unauthorized nodes, hackers, misbehaving vehicles, malicious attackers etc without security. Without valid confirmation of authorized vehicles, these types of attacks may occur. To enhance the reliability in message broadcasting, some existing techniques are used. They transmit the data without much delay but they didn’t provide any trusted authentication. So hackers, malicious nodes, unauthorized vehicles may easily interrupt the emergency messages. Also Brute force attack, Man in Middle attack are not identified and eliminated. In this research, a trust based secured broadcasting mechanism is developed which ensures the metrics such as security, privacy, integrity, trust etc. The major intension of this research is to reduce latency and provide high reliable, secure and efficient communication over the vehicles. The data such as vehicle position, location, speed, and other traffic information’s are generated and stored in a separate table. A network is created with varying densities. A path is generated for message broadcasting between source and destination based on the specific gateway estimated. Here, Optimal Wireless Access in Vanet (OWAV) Protocol is employed to gather vehicle related information to reduce the delay. Blowfish encryption algorithm along with Oppositional Deer Hunting Optimization (ODHO) is used to store the trusted vehicles location to avoid unauthorized tracking. The performance of the proposed research is analyzed with various metrics such as Packet delivery ratio (PDR), transmission delay, encryption time, hroughput, computational overhead etc. The efficiency of the research is compared with other existing methods.

A Secure Method for Managing Data in Cloud Storage using Deduplication and Enhanced Fuzzy Based Intrusion Detection Framework

Cloud services increase data availability so as to offer flawless service to the client. Because of increasing data availability, more redundancies and more memory space are required to store such data. Cloud computing requires essential storage and efficient protection for all types of data. With the amount of data produced seeing an exponential increase with time, storing the replicated data contents is inevitable. Hence, using storage optimization approaches becomes an important pre-requisite for enormous storage domains like cloud storage. Data deduplication is the technique which compresses the data by eliminating the replicated copies of similar data and it is widely utilized in cloud storage to conserve bandwidth and minimize the storage space. Despite the data deduplication eliminates data redundancy and data replication; it likewise presents significant data privacy and security problems for the end-user. Considering this, in this work, a novel security-based deduplication model is proposed to reduce a hash value of a given file size and provide additional security for cloud storage. In proposed method the hash value of a given file is reduced employing Distributed Storage Hash Algorithm (DSHA) and to provide security the file is encrypted by using an Improved Blowfish Encryption Algorithm (IBEA). This framework also proposes the enhanced fuzzy based intrusion detection system (EFIDS) by defining rules for the major attacks, thereby alert the system automatically. Finally the combination of data exclusion and security encryption technique allows cloud users to effectively manage their cloud storage by avoiding repeated data encroachment. It also saves bandwidth and alerts the system from attackers. The results of experiments reveal that the discussed algorithm yields improved throughput and bytes saved per second in comparison with other chunking algorithms.

Secure Radio Frequency Transmission for Paperless Voting System

In any democracy, elections play an important role. If the traditional Electronic Voting Machine (EVM) is secured by encryption, it could be made more reliable. Traditional voting process provides security through the use of a paper audit trail which is not environment friendly making it unfit for use in the long run. This paper proposes the use of Blowfish algorithm for encryption along with secure transmission using radio frequency and verification of the cast vote. In this approach, the cast vote is encrypted using Blowfish encryption algorithm and transmitted to the server through radio frequency. At the server, the data is decrypted and sent back to be displayed on the screen of the EVM, eliminating the paper audit trail. This approach will account for a considerable amount of cost reduction without compromising on the security and sanctity of the election process.

Secure Data Hiding using Reversible Image Transformation

In today’s era, the large amount of data’s are stored in cloud. But nowadays securely storing data to the cloud is an important task. But the unauthorized parties are tries to decrypt the data from the cloud. So, the data stored in cloud should be secure from any malicious activities. Based on these requests, we propose a framework named reversible data hiding (RDH). It mainly works on encrypted images based on reversible image transformation (RIT). The server insert data into the cloud. On that time, he will add some additional information for the protection of data. In the given framework, the original data can be embedded in another image named carrier image with equal size. So, the hacker tries to pullout the data, he will only get the carrier image. So we can accommodate more data’s into a single storage space. The method picks the data to be hide and a carrier image with equal size. The cover image is embedded into the original image based on the LSB insertion algorithm. i.e., the original data’s are dissolved into the carrier image using a secret hiding key. The cover image is encrypted using blow fish encryption algorithm. It includes normal RDH method and RDH with RIT. Using RIT, it gives high visual quality and security. The technique is mainly used in medical imagery, military imagery for reliable data storage.

Blowfish Implementation on SoloEncrypt Security Model for User Data Protection Prior to Cloud Storage

Smartphones' data security has become increasingly important in the advancement of mobile technologies. Users today use smartphones as means of communication, sending and receiving messages, browsing the internet as well as storing sensitive information. Due to these functionalities smartphones today are preferred targets of attacks. Hence, the foremost goal of this work is to provide security for user data on smartphones by the implementation of Blowfish encryption algorithm prior to cloud storage. Blowfish encryption algorithm is a popular and well tested cryptographic algorithm to provide security for user data in mobile cloud computing. Further, there is the need for the development of a mobile application to provide the needed security on smartphones using this existing algorithm. This paper therefore proposed a Secure App using java + xml which was implemented on SoloEncrypt security model. Three Android devices were used to carry out the experiment; Samsung Galaxy On7 Prime, Samsung Galaxy J2 Pro and Samsung Galaxy J7 Nxt. Encryption time and decryption time were parameters used for evaluating the encryption algorithm which are among the standardized metrics approved by the National Institute of Standards and Technology (NIST). The experimental results revealed that the algorithm encrypts with better speed and it takes less time to decrypt when compared with related work.

Generating Cipher Text using BLOWFISH Algorithm for Secured Data Communications

Cryptography plays a major role in the network security. In order to secure the data one must do encryption of the original message. In this paper, the design and analysis of high speed and high performance BLOWFISH algorithm is implemented in VHDL coding and compared with AES (Advanced Encryption Standard) algorithm. The BLOWFISH algorithm involves the process of giving the data and key as input to the encryption block. BLOWFISH encryption algorithm is designed and programmed in VHDL coding. Then it is implemented in Xilinx 10.1. This research is carried in the following steps: designing of encryption algorithm, writing VHDL code, simulating the code on “ModelSim altera 6.5e”, synthesizing and implementing the code using Xilinx’s ISE 10.1.This research aims in developing flexible and technology independent architectures in the areas of VPN software, file compression, public domain software such as smart cards, etc. Also presents the comparison of BLOWFISH and AES algorithms. Experimental results show that BLOWFISH algorithm runs faster than AES algorithm while both of them consume almost the same

Double Layer Image Security System using Encryption and Steganography

The image security on internet transfers is the concern of the hour as the breaching attacks into the image databases are rising every year. The hackers take advantage of the stolen personal and important images to fulfill their dangerous and unethical intentions. The image data theft can be used to defame a person on the internet by posting the illegal and unacceptable images of that person (internet user). Hence the images transfers have to be secure to ensure the privacy of the user’s image data. In this research, a number of image security systems have been studied to evaluate the research gap. Majority of the existing image security systems are not up to date to protect against the latest breaching attacks. So, we have proposed an effective and robust image security framework particularly designed for the images. The proposed has been designed and implemented using MATLAB. In this research, a hybrid image security framework has been proposed to overcome the problem stated earlier, which will be implemented by combining various techniques together to achieve the image security goal. The techniques included in the combination would beimage compression, cryptography and steganography. DWT compression has been used, because it is a stronger compression algorithm. The steganographed image would be compressed to reduce its size. Blowfish encryption algorithm would be used for the encryption purposes. It offers maximum throughput (faster) and also energy efficient. Compressed image would be encrypted to enhance the image security. Real image will be hidden into another image. A cluster based steganographic technique will be used. Real image and face image would be analyzed, and the real image would be embedded in those areas of face image, where color schemes of the real image and face image would be most similar. Kmeans or Hierarchical clustering would be used as a clustering technique. An all new comparative analysis technique would be applied to make the comparison between real image and base image on the basis of color patterns.