Detecting the Security Level of Various Cryptosystems Using Machine Learning Models

被引:31
作者
Shafique, Arslan [1 ]
Ahmed, Jameel [1 ]
Boulila, Wadii [2 ,3 ]
Ghandorh, Hamzah [3 ]
Ahmad, Jawad [4 ]
Rehman, Mujeeb Ur [1 ]
机构
[1] Riphah Int Univ, Dept Elect Engn, Islamabad 44000, Pakistan
[2] Univ Manouba, RIADI Lab, Manouba 2010, Tunisia
[3] Taibah Univ, Coll Comp Sci & Engn, Medina 42353, Saudi Arabia
[4] Edinburgh Napier Univ, Sch Comp, Edinburgh EH10 5DT, Midlothian, Scotland
关键词
Support vector machine (SVM); security analysis; image encryption; cryptosystem; IMAGE; TRANSFORM; MAPS;
D O I
10.1109/ACCESS.2020.3046528
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:9383 / 9393
页数:11
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