- Volume 12
- Issue 1
- (6) Articles
Anita Soni
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.
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Doi: https://doi.org/10.54216/JCIM.120101
Vol. 12
Issue. 1
PP. 08-18, (2023)
Raju Ranjan
,
Vinay Kumar Ahlawat
Wireless sensor networks (WSN) have been implemented in nearly every field of use because they offer a solution to practical problems that can also be affordably implemented. The sensor nodes have limited computing resources, weak batteries, and limited storage space. The environmental or physical data collected by these nodes is transmitted straight to the BS. The data transfer cost is raised due to the direct data transmission. In addition, the lifetime of sensor networks is shortened because of the rise in energy required for data exchange. As a result, data aggregation is utilized in WSN to lessen the burden of transmission costs and lengthen the useful life of the sensor networks. Each sensor node's transmission is encrypted with cipher text generated by the Paillier homomorphic cryptosystem. In addition, the Bilinear aggregate signature method is used to create a digital signature at each sensor node. The cluster head / BS is where the aggregation takes place once the cipher text and signature have been combined. Before deciding whether to accept or reject the message, the BS checks the aggregate signature. The homomorphic cryptosystem saves power because it does not perform intermediate-level or cluster-head decryption. Data integrity, authenticity, and confidentiality are all maintained while using less power with this technology. The Intel laboratory dataset is used in the implementation. When compared to current systems, the proposed SDA method requires less time and energy to calculate.
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Doi: https://doi.org/10.54216/JCIM.120102
Vol. 12
Issue. 1
PP. 19-29, (2023)
Vikas Goel
,
Amit Kumar Goyal
The transmission and storage of digital data raises serious security concerns as information technology evolves at a breakneck pace. To ensure the safety of the transferred data, security methods must be put in place. Encrypting an image is a method of protecting sensitive data by converting it into an unrecognizable format. The procedure includes access control, privacy, validation, and copyright protection. Cryptography, steganography, and watermarking are three distinct methods to prevent unauthorized access to digital data. Of these three methods, cryptography has emerged as one of the most important ways to ensure complete safety. Therefore, a secure and efficient cipher algorithm is required for trustworthy communication. In this work, we offer a practical Secured Asymmetric Image Cipher (SAIC) Algorithm for encrypting images with a secret key of arbitrary length. At first, the KG algorithm creates two unique keys. Both the encryption and decryption processes require a key. The experimental results reveal that the encrypted image lacks the original image's independence (NPCR > 99.89%, UACI > 36.89%). The suggested approach has a high encryption rate, can be implemented easily, and is computationally secure. The reproduced data validates the safety and practicability of the proposed architecture.
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Doi: https://doi.org/10.54216/JCIM.120103
Vol. 12
Issue. 1
PP. 30-40, (2023)
Mustafa El-Taie
,
Aaras Y.Kraidi
The use of machine learning methods in cybersecurity is only one of many examples of how this once-emerging innovation has entered the mainstream. Anomaly-based identification of common assaults on vital infrastructures is only one instance of the various applications of malware analysis. Scholars are using machine learning-based identification in numerous cybersecurity solutions since signature-based approaches are inadequate at identifying zero-day threats or even modest modifications of established assaults. In this work, we introduce the machine-learning models-based security framework to detect cyber-attacks. This paper used three machine learning models Logistic Regression, Random Forest, and K-Nearest Neighbor This framework not only reduces the computational difficulty of the framework by minimizing the feature parameters, but it also performs well in terms of accuracy in forecasting unknown scenarios in the tests. Finally, we ran trials using cybersecurity datasets to measure the machine learning model's performance using metrics including precision, recall, and accuracy.
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Doi: https://doi.org/10.54216/JCIM.120104
Vol. 12
Issue. 1
PP. 41-49, (2023)
Hasan Sankari
,
Mohammad Abobala
The objective of this paper is to present a novel application of 2-cyclic refined integers to build a novel crypto scheme for the encryption and decryption of data and information based on the algebraic properties of 2-cyclic refined integers combined with RSA algorithm, where an improved version of RSA crypto-scheme will be established and applied to the security of information and data. On the other hand, we illustrate some examples and figures to show the validity and complexity of the algorithm.
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Doi: https://doi.org/10.54216/JCIM.120106
Vol. 12
Issue. 1
PP. 67-73, (2023)
Mustafa El-Taie
,
Aaras Y.Kraidi
The convergence of blockchain technology and edge intelligence has emerged as a transformative force in the realm of smart cities, offering unprecedented opportunities to enhance sustainability. This paper presents a comprehensive review and prospective analysis of this convergence, shedding light on its potential to revolutionize urban environments. We commence with an exploration of the fundamental components of blockchain technology, emphasizing its core principles of decentralization, immutability, and consensus mechanisms. Simultaneously, we trace the historical evolution of blockchain from its origins with Bitcoin to its broad applications in diverse domains. In the context of edge intelligence, we examine how this paradigm shift decentralizes data processing, enabling real-time decision-making and enhancing data security. We elucidate its key components, such as edge devices and analytics algorithms, while highlighting its critical role in reshaping the urban landscape. The crux of this paper lies in the convergence of blockchain and edge intelligence, where we explore its profound implications for the sustainability of smart cities. From efficient energy management and waste reduction to improved transportation and green infrastructure, this convergence empowers smart cities to optimize resource usage, reduce environmental impact, and enhance the quality of life for their residents. Through a prospective analysis, we anticipate emerging trends and innovations that will shape the future of smart city sustainability, including AI integration, 5G connectivity, and circular economy initiatives. As smart cities continue to evolve, they stand at the forefront of addressing urbanization challenges while fostering sustainable, inclusive, and prosperous urban environments.
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Doi: https://doi.org/10.54216/JCIM.120105
Vol. 12
Issue. 1
PP. 50-61, (2023)
