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.
Highlights
Due to the exponential increase in transmissions of multimedia data over insecure channels, security has become a much-in-demand area of research
For the security level detection, we have considered all types of image encryption algorithms whether it is based on the frequency domain, transform-based or chaotic maps based schemes The main objective of the proposed work is to find the security level of the encryption algorithms
We have developed a new model using a support vector machine (SVM) to identify the security level of various cryptosystems
Summary
Due to the exponential increase in transmissions of multimedia data over insecure channels (mostly the Internet), security has become a much-in-demand area of research. To enhance the security of the encryption algorithm, Kaur et al proposed a new optical image encryption scheme based on a chaotic in [19] which proved capable of generating the vectors of multiple orders using a piece-wise linear chaotic map (PWLCM) [20]. Selective encryption schemes work well for real-time applications where fast encryption is required, they are not suitable for text encryption, where every individual single bit must be encrypted in order for the data to be properly concealed These algorithms achieved efficient encryption, as demonstrated by the statistical analysis; these results were not enough to show the security level of the proposed work. SVM is used to test various algorithms and determine whether each one has a security level of strong, acceptable, or weak This purpose requires several inputs that can be treated as features or feature vectors.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
