In order to improve the security and efficiency of image encryption, many researchers have continuously proposed new image encryption schemes in recent years. However, these image encryption schemes have not been fully analyzed and evaluated. In this paper, a newly reported image encryption scheme based on Feistel network and dynamic Deoxyribonucleic Acid (DNA) encoding is deeply and comprehensively investigated. This encryption scheme mainly adopts four encryption steps to encrypt the plain image, which are Generation of chaotic sequences, Hill encryption, Feistel network, and Pixel diffusion. Our analyses show that there are some problems in the secret key design and encryption process of this encryption scheme. After pointing out and analyzing these problems, we have made several necessary improvements to this encryption scheme and proposed the corresponding chosen-plaintext attack algorithm. The subsequent simulation tests and analyses have confirmed the effectiveness and feasibility of the proposed attack algorithm. Finally, for the problems in this encryption scheme and some current image encryption schemes, improvement suggestions are presented to provide references for the designers of future image encryption schemes.

A symmetric block cipher employing a substitution–permutation duo is an effective technique for the provision of information security. For substitution, modern block ciphers use one or more substitution boxes (S-Boxes). Certain criteria and design principles are fulfilled and followed for the construction of a good S-Box. In this paper, an innovative technique to construct substitution-boxes using our cubic fractional transformation (CFT) is presented. The cryptographic strength of the proposed S-box is critically evaluated against the state of the art performance criteria of strong S-boxes, including bijection, nonlinearity, bit independence criterion, strict avalanche effect, and linear and differential approximation probabilities. The performance results of the proposed S-Box are compared with recently investigated S-Boxes to prove its cryptographic strength. The simulation and comparison analyses validate that the proposed S-Box construction method has adequate efficacy to generate efficient candidate S-Boxes for usage in block ciphers.

Parkinson’s disease (PD) is one of the chronic neurological diseases whose progression is slow and symptoms have similarities with other diseases. Early detection and diagnosis of PD is crucial to prescribe proper treatment for patient’s productive and healthy lives. The disease’s symptoms are characterized by tremors, muscle rigidity, slowness in movements, balancing along with other psychiatric symptoms. The dynamics of handwritten records served as one of the dominant mechanisms which support PD detection and assessment. Several machine learning methods have been investigated for the early detection of this disease. But, most of these handcrafted feature extraction techniques predominantly suffer from low performance accuracy issues. This cannot be tolerable for dealing with detection of such a chronic ailment. To this end, an efficient deep learning model is proposed which can assist to have early detection of Parkinson’s disease. The significant contribution of the proposed model is to select the most optimum features which have the effect of getting the high performance accuracies. The feature optimization is done through genetic algorithm wherein K -Nearest Neighbour technique. The proposed novel model results into detection accuracy higher than 95%, precision of 98%, area under curve of 0.90 with a loss of 0.12 only. The performance of proposed model is compared with some state of the art machine learning and deep learning based PD detection approaches to demonstrate the better detection ability of our model.

Efficient substitution boxes have crucial importance in the design of cryptographically strong modern block encryption algorithms. They are the only nonlinear blocks that decide the security strength of entire cryptosystem. In this paper, a meta-heuristic approach based on Ant Colony Optimization and chaos is put forward to retrieve a suitable configuration of strong 8×8 substitution box. The optimization is carried out by transforming initial S-box to travelling salesman problem through edge matrix. The cryptographic strength of optimized S-box is investigated against standard tests such as bijectivity, nonlinearity, strict avalanche criterion, output bits independence criterion and differential approximation probability. The performance comparison of generated S-box with some recent chaos-based S-boxes evidently proves that the proposed scheme is proficient to discover strong nonlinear component of block encryption systems