In this paper, we present a novel method to construct cryptographically strong bijective substitution-boxes based on the complicated dynamics of a new hyperchaotic system. The new hyperchaotic system was found to have good characteristics when compared with other systems utilized for S-box construction. The performance assessment of the proposed S-box method was carried out based on criteria, such as high nonlinearity, a good avalanche effect, bit-independent criteria, and low differential uniformity. The proposed method was also analyzed for the batch-generation of 8 × 8 S-boxes. The analyses found that through a proposed purely chaos-based method, an 8 × 8 S-box with a maximum average high nonlinearity of 108.5, or S-boxes with differential uniformity as low as 8, can be retrieved. Moreover, small-sized S-boxes with high nonlinearity and low differential uniformity are also obtainable. A performance comparison of the anticipated method with recent S-box proposals proved its dominance and effectiveness for a strong bijective S-box construction.

In modern-day block ciphers, the role of substitution-boxes is to transform the plaintext data nonlinearly to generate ciphertext data with sufficient confusion. It has been well-confirmed that the robustness and security of such block ciphers heavily based on the cryptographic strength of the underlying substitution-boxes. Reason being, they are the only components that are held responsible to bring required nonlinearity and complexity into the security system which can frustrate the attackers. Accordingly, a number of different concepts have been explored to construct strong S-boxes. To move forward with the same aim, a novel simple modular approach, the very first time, is investigated to construct nonlinear S-box in this paper. The proposed new modular approach consists of three operations such as new transformation, modular inverses, and permutation. A number of highly nonlinear S-boxes can be easily constructed with slight changes in the novel transformation parameters. An example S-box is presented whose critical performance assessment against some benchmarking criterions such as high nonlinearity, absence of fixed points, fulfillment of SAC and BIC properties, low differential uniformity and linear approximation probability and comparison with recent S-boxes demonstrate its upright cryptographic potentiality. In addition, an image encryption algorithm is also proposed wherein the generated S-box is applied to perform the pixels shuffling and substitution for strong statistical and differential encryption performance.

This paper presents a hybrid method for the construction of cryptographically strong bijective substitution-boxes by utilizing the merits of chaotic map and algebraic groups. The hybrid method first generates the key-dependent dynamic S-boxes using chaotic heuristic search strategy and then the S-boxes are evolved with the help of potent proposed algebraic group structures. This paper proposes a new improved combination chaotic map to operate initial search strategy. To augment the strength of generated S-boxes, the algebraic group structures are discovered which have the power to improve their cryptographic strength. The performance assessments using standard criterions are rendered to quantify the strengths of proposed bijective S-boxes. The experimental results and comparisons with recent S-box research findings justify the effectiveness and competence of the proposed bijective S-boxes and anticipated hybrid generation method.

Static substitution-boxes in fixed structured block ciphers may make the system vulnerable to cryptanalysis. However, key-dependent dynamic substitution-boxes (S-boxes) assume to improve the security and robustness of the whole cryptosystem. This paper proposes to present the construction of key-dependent dynamic S-boxes having high nonlinearity. The proposed scheme involves the evolution of initially generated S-box for improved nonlinearity based on the fractional-order time-delayed Hopfield neural network. The cryptographic performance of the evolved S-box is assessed by using standard security parameters, including nonlinearity, strict avalanche criterion, bits independence criterion, differential uniformity, linear approximation probability, etc. The proposed scheme is able to evolve an S-box having mean nonlinearity of 111.25, strict avalanche criteria value of 0.5007, and differential uniformity of 10. The performance assessments demonstrate that the proposed scheme and S-box have excellent features, and are thus capable of offering high nonlinearity in the cryptosystem. The comparison analysis further confirms the improved security features of anticipated scheme and S-box, as compared to many existing chaos-based and other S-boxes.

The issues of identity authentication and privacy protection of individuals in body area network (BAN) systems have raised much concern in past few years. To address the challenges of privacy protection in wireless BAN, an image encryption algorithm has been proposed recently by Wang et al. The encryption algorithm utilized two 1D chaotic maps to generate sub-chaotic matrices which are combined to perform encryption. The algorithm has good statistical encryption performance. However, a cautious inquiry finds that it has some underlying security defects. This paper evaluates the security of the Wang et al. encryption algorithm to show that it is totally breakable under proposed cryptanalysis and hence infeasible for privacy protection in BAN. It has been shown that the plain-image data can be recovered without any prior knowledge of secret key and plain-text. Furthermore, this paper also suggests an improved encryption scheme using secure hash algorithm SHA-512 for one-time keys and a 4D hyperchaotic system to subdue the security insufficiencies of the algorithm under study. The simulation results and analysis demonstrate that the improved image encryption scheme has excellent encryption quality, plain-image sensitivity, and resistance to possible cryptanalytic attacks.

This research delves into the obstacles and difficulties associated with predicting cryptocurrency movements in the volatile global financial market. This study develops and evaluates an advanced Deep Learning-Enhanced Temporal Fusion Transformer (ADE-TFT) model to estimate Bitcoin values more accurately. This research employs cutting-edge artificial intelligence (AI) and machine learning (ML) techniques to comprehensively examine various aspects of cryptocurrency forecasting, including geopolitical implications, market sentiment analysis, and pattern detection in transactional datasets. The study demonstrates that the ADE-TFT model outperforms its lower-layer counterparts in terms of forecasting accuracy, with reduced Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE) values, particularly when using a higher hidden layer configuration (h=8). The study emphasizes the importance of experimenting with different normalization strategies and utilizing various market-related data to enhance the model's performance. The results suggest that improving forecasting accuracy may require addressing these limitations and incorporating additional factors, such as market sentiment. By providing investors with more precise market predictions, the techniques and information presented in this research have the potential to significantly increase investor power in an unpredictable digital currency market, enabling wise investment choices.