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In order to improve the security and efficiency of image encryption systems comprehensively, a novel chaotic S-box based image encryption scheme is proposed. Firstly, a new compound chaotic system, Sine-Tent map, is proposed to widen the chaotic range and improve the chaotic performance of 1D discrete chaotic maps. As a result, the new compound chaotic system is more suitable for cryptosystem. Secondly, an efficient and simple method for generating S-boxes is proposed, which can greatly improve the efficiency of S-box production. Thirdly, a novel double S-box based image encryption algorithm is proposed. By introducing equivalent key sequences r, t related with image ciphertext, the proposed cryptosystem can resist the four classical types of attacks, which is an advantage over other S-box based encryption schemes. Furthermore, it enhanced the resistance of the system to differential analysis attack by two rounds of forward and backward confusion-diffusion operation with double S-boxes. The simulation results and security analysis verify the effectiveness of the proposed scheme. The new scheme has obvious efficiency advantages, which means that it has better application potential in real-time image encryption.

In the paper, a new one-dimensional (1-D) compound Sine chaotic system (CSCS) is first proposed. Then new chaotic maps are generated by the CSCS. And four novel generated maps are used for the illustration about the CSCS. Secondly, the results of performance analysis show that the four maps have large Lyapunov exponents and high complexity. Thirdly, a high-efficiency image encryption scheme is proposed by employing two of the four new produced chaotic maps. In the proposed encryption scheme, the simplest diffusion operation is used. And we use a variety of scrambling operations, such as Zigzag transform, Magic confusion and the row confusion. In addition, to increase key space and in order to improve the ability to resist two kinds of attacks, namely the known plaintext attack and the selected plaintext attack, the control parameters and the initial values of the two new chaotic systems are generated based on the SHA-256 function. Finally, compared to other schemes, simulation tests show that our scheme not only has higher security but also faster encryption speed.

For many years, chaotic maps have been widely used in the design of various algorithms in cryptographic systems. In this paper, a new model (compound chaotic system) of quantum random walks controlled by a hyper-chaotic map is constructed and a novel scheme for constructing a dynamic S-Box based on the new model is proposed. Through aperiodic evaluation and statistical complexity measurement, it is shown that the compound chaotic system has features such as complex structure and stronger randomness than classical chaotic systems. Based on the chaotic sequence generated by the composite system, we design a dynamic S-Box generation mechanism. The mechanism can quickly generate high-security S-Boxes. Then, an example of randomly generating S-Boxes is given alongside an analytical evaluation of S-Box standard performance criteria such as bijection, boomerang uniformity, bit independence, nonlinearity, linear approximate probability, strict avalanche effect, differential uniformity, the and generalized majority logic criterion. The evaluation results confirm that the performance of the S-Box is excellent. Thus, the proposed dynamic S-Box construction technique can be used to generate cryptographically strong substitution-boxes in practical information security systems.

Most of existing optical color image encryption schemes have born security risks due to the adoption of linear transform, and data redundancy for the generation of complex image. To settle these problems effectively, a hybrid scheme for self-adaptive double color-image encryption is proposed in this paper. In this scheme, each RGB color component of two secret color images is first compressed and encrypted by 2-D compressive sensing (CS) in which measurement matrices are generated by 1-D compound chaotic systems and further are optimized by steepest descent algorithm to improve image reconstruction effect. Then, the two measured images are regarded as the real part and imaginary part to constitute a complex image, respectively. In the end, the complex image is reencrypted by self-adaptive random phase encoding and discrete fractional random transform (DFrRT) to obtain the final encrypted data. In the process of DFrRT and random phase encoding, the correlations between R, G, B components are adequately utilized. The production of key streams not only depends on the initial values of chaotic systems but also on plaintexts, and the three color components affect each other to enhance the ability against the known plaintext attack. The projection neural network algorithm is adopted to obtain the decryption images. Simulation results also verify the validity and security of the proposed method.

Soil salinization is very complex and its evolution is affected by numerous interacting factors produce strong non-linear characteristics. This is the first time fractional order chaos theory has been applied to soil salinization-level classification to decrease uncertainty in salinization assessment, solve fuzzy problems, and analyze the spectrum chaotic features in soil with different levels of salinization. In this study, typical saline soil spectrum data from different human interference areas in Fukang City (Xinjiang) and salt index test data from an indoor chemical analysis laboratory are used as the base information source. First, we explored the correlation between the spectrum reflectance features of soil with different levels of salinization and chaotic dynamic error and chaotic attractor. We discovered that the chaotic status error in the 0.6 order has the greatest change. The 0.6 order chaotic attractors are used to establish the extension matter-element model. The determination equation is built according to the correspondence between section domain and classic domain range to salinization level. Finally, the salt content from the chemical analysis is substituted into the discriminant equation in the extension matter-element model. Analysis found that the accuracy of the discriminant equation is higher. For areas with no human interference, the extension classification can successfully identify nine out of 10 prediction data, which is a 90% identification accuracy rate. For areas with human interference, the extension classification can successfully identify 10 out of 10 prediction data, which is a success rate of 100%. The innovation in this study is the building of a smart classification model that uses a fractional order chaotic system to inversely calculate soil salinization level. This model can accurately classify salinization level and its predictive results can be used to rapidly calculate the temporal and spatial distribution of salinization in arid area/desert soil.

This manuscript proposes a novel technique of synchronization to synchronize four non-identical chaotic systems together. The newly introduced scheme is named as anti-difference quadratic compound synchronization. We have also used the multi-switching of signals to increase the complexity of the proposed scheme. Lorenz, Rössler, and the modified financial systems are taken as drive systems, and the modified Shimizu–Morioko system is taken as the response system to illustrate the obtained results. Lyapunov stability is used to design suitable controllers. Numerical simulations and graphs are presented using MATLAB to verify the theoretical results.

Information can be shared across the Internet using cloud computing, a powerful paradigm for meeting the needs of individuals and organizations. To minimize access time and maximize load balancing for data nodes (DNs), a dynamic data replication algorithm is necessary. Even so, few of the existing algorithms consider each objective holistically during replication. An improved ant lion optimizer (ALO) algorithm and a fuzzy system are used in this paper to determine dynamically the number of replicas and the DNs for replication. Further, it balances the trade-offs among different objectives (e.g., service time, system availability, load, and monetary cost). The ALO algorithm has been widely applied to solve complex optimization problems due to its simplicity in implementation. However, ALO has premature convergence and can thus easily get trapped into the local optimum solution. In this paper, to overcome the shortcomings of ALO by balancing exploration and exploitation, a hybrid ant lion optimizer with Tabu search algorithm (ALO-Tabu) is proposed. There are several improvements of the ALO, in which the appropriate solutions are selected for the initial population based on chaotic maps (CMs) and opposition-based learning (OBL) strategies. On the other hand, there are many CMs, OBLs, and random walk strategies that make it difficult to select the best one for optimization. Generally, they are selected manually, which is time-consuming. As a result, this paper presents a hyper-heuristic ALO (HH-ALO-Tabu) that automatically chooses CMs, OBLs, and random walk strategies depending on the differential evolution (DE) algorithm. Based on 20 well-known test functions, the experiment results and statistical tests show that HH-ALO-Tabu can solve optimization problems effectively.

Chaos has been widely used in many different kinds of scientific fields, especially in the field of cryptography. While most of the original chaotic systems are not complex and secure enough for practical uses, therefore, in this paper, a novel compounding coupling technique is firstly proposed to enhance the complexity of chaotic systems. The provided compound-coupled chaotic model is universal for all different chaotic maps. Here, to prove the validity of the proposed model, 1D Logistic map, the most widely used chaotic map, is taken as the example. Then a series of numerical experiments were conducted to compare and analyze the maps before and after improvement. From the results, it’s obvious that the improved map has higher dynamical complexity than the original one. Furthermore, a novel image encryption algorithm based on the compound-coupled Logistic map is proposed to prove the practicability of the improvement model. Sufficient experimental tests indicate that this encryption algorithm has a high security level, which can be competitive to other chaos-based image encryption algorithms.

Cryptography is required while interacting through an insecure media, such as internet. Unauthorised individuals have the ability to view and alter data as it is being transferred from one location to other. Cryptography is important in the realm of security of data. Cryptographic methods are being utilized to protect information against attackers in various areas. Symmetric key encryption employs a single key for encryption and decryption, and is a famously used cryptosystem. In this manuscript, improved elliptical curve cryptography and chaotic mapping with fruitfly optimization algorithm is proposed for Secure Data Transmission (IECC-CMFOA-SDT). Here, cryptography and steganography are consolidated to enhance the data security. Initially, input is the plain text from the sender and compression is done to decrease the size of input data. Then data obfuscation is carried out over the compressed data file with the aid of binary conversion, 2’s complement, decimal conversion, and ASCII conversion. By utilizing the improved elliptical curve cryptography (IECC) algorithm, obfuscated data is encrypted. Encrypted data is embedded with the support of Chaotic mapping based fruitfly optimization algorithm (CMFOA), which conceals the data by jumbling the pixels in the image. Hence the secret data is diversified. CMFOA picks the superior values of pixels to root the encrypted data. The receiver acquires the stego image with embedded concealed data, which chooses the optimum pixels points to extract the concealed data from the image. Original data is acquired by decrypting, deobfuscating and decompressing the extracted data. The proposed approach is executed in CloudSim Plus. The performance metrics, like obfuscation time, deobfuscation time, encryption time, decryption time, MSE and PSNR are analysed. Performance of IECC-CMFOA-SDT model gives 39.01%, 28.34%, and 37.45% lower encryption time and 17.12%, 24.12% and 32.07 higher PSNR compared with the existing methods, like optimal users based secure data transmission with lightweight block ciphers (CRT-HTLBO-SDT), Secure data transmission employing a hybrid of a bit mask oriented genetic algorithm, encryption and steganography (BMOGA-DWT-SDT) and cryptanalysis of an encryption method based on a compound coupled logistic map and an anti-codifying technique for secure data transmission (CCLM-ACT-SDT).

The Honey Badger Algorithm (HBA) is a novel meta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers. The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA, which has been applied in photovoltaic systems and optimization problems effectively. However, HBA tends to suffer from the local optimum and low convergence. To alleviate these challenges, an improved HBA (IHBA) through fusing multi-strategies is presented in the paper. It introduces Tent chaotic mapping and composite mutation factors to HBA, meanwhile, the random control parameter is improved, moreover, a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation. IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems. The Wilcoxon Rank-sum Test, Friedman Test and Mann-Whitney U Test are conducted after emulation. The results indicate the competitiveness and merits of the IHBA, which has better solution quality and convergence traits. The source code is currently available from: .