Detecting the Security Level of Various Cryptosystems Using Machine Learning Models

Arslan, shafique (2021) Detecting the Security Level of Various Cryptosystems Using Machine Learning Models. IEEE Access, 09. pp. 1-5. ISSN 2169-3536

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Abstract

With recent advancements in multimedia technologies, the security of digital data has become a critical issue. To overcome the vulnerabilities of current security protocols, researchers tend to focus their efforts on modifying existing protocols. Over the last few decades, though, several proposed encryption algorithms have been proven insecure, leading to major threats against important data. Using the most appropriate encryption algorithm is a very important means of protection against such attacks, but which algorithm is most appropriate in any particular situation will also be dependent on what sort of data is being secured. However, testing potential cryptosystems one by one to find the best option can take up an important processing time. For a fast and accurate selection of appropriate encryption algorithms, we propose a security level detection approach for image encryption algorithms by incorporating a support vector machine (SVM). In this work, we also create a dataset using standard encryption security parameters, such as entropy, contrast, homogeneity, peak signal to noise ratio, mean square error, energy, and correlation. These parameters are taken as features extracted from different cipher images. Dataset labels are divided into three categories based on their security level: strong, acceptable, and weak. To evaluate the performance of our proposed model, we have performed different analyses (f1-score, recall, precision, and accuracy), and our results demonstrate the effectiveness of this SVM-supported system.

Item Type:	Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering and Applied Sciences (FEAS) > Department of Electrical Engineering Islamabad
Depositing User:	Mr. Arslan Shafique
Date Deposited:	27 Apr 2021 17:40
Last Modified:	27 Apr 2021 17:40
URI:	http://research.riphah.edu.pk/id/eprint/1372

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