Detecting the Security Level of Various Cryptosystems Using Machine Learning Models

Shafique, Arslan; Ahmed, Jameel; Boulila, Wadii; Ghandorh, Hamzah; Ahmad, Jawad; Rehman, Mujeeb Ur

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.

Citation

Shafique, A., Ahmed, J., Boulila, W., Ghandorh, H., Ahmad, J., & Rehman, M. U. (2021). Detecting the Security Level of Various Cryptosystems Using Machine Learning Models. IEEE Access, 9, 9383-9393. https://doi.org/10.1109/access.2020.3046528

• Journal Article Type: Article
• Acceptance Date: Dec 18, 2020
• Online Publication Date: Dec 22, 2020
• Publication Date: 2021
• Deposit Date: Jan 25, 2021
• Publicly Available Date: Jan 25, 2021
• Journal: IEEE Access
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Pages: 9383-9393
• DOI: https://doi.org/10.1109/access.2020.3046528
• Keywords: Support vector machine (SVM), security analysis, image encryption, cryptosystem
• Public URL: http://researchrepository.napier.ac.uk/Output/2717192