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The current research work proposed a novel optimization-based 2DSIMM (Two-Dimensional Sine Iterative chaotic map with infinite collapse Modulation Map) model for image encryption. The proposed 2D-SIMM model is derived out of sine map and Iterative Chaotic Map with Infinite Collapse (ICMIC). In this technique, scrambling effect is achieved with the help of Chaotic Shift Transform (CST). Chaotic Shift Transform is used to change the value of pixels in the input image while the substituted value is cyclically shifted according to the chaotic sequence generated by 2D-SIMM model. These chaotic sequences, generated using 2D-SIMM model, are sensitive to initial conditions. In the proposed algorithm, these initial conditions are optimized using JAYA optimization algorithm. Correlation coefficient and entropy are considered as fitness functions in this study to evaluate the best solution for initial conditions. The simulation results clearly shows that the proposed algorithm achieved a better performance over existing algorithms. In addition, the VLSI implementation of the proposed algorithm was also carried out using Xilinx system generator. With optimization, the correlation coefficient was -0.014096 and without optimization, it was 0.002585. [ABSTRACT FROM AUTHOR]

Cyber Physical Systems (CPSs) are network systems containing cyber (computation, communication) and physical (sensors, actuators) components that interact with each other through feedback loop with the help of human intervention. The dynamic and disseminated characteristics of CPS environment makes it vulnerable to threats that exist in virtualization process. Due to this, several security issues are presented in CPS. In order to address the challenges, there is a need exists to extend the conventional security solutions such as Intrusion Detection Systems (IDS) to handle high speed network data traffic and adaptive network pattern in cloud. Additionally, the identification of feasible network traffic characteristics is the main issue in precise detection of attacks in the network. With this motivation, the current research paper presents an Optimal Deep Belief Networkbased distributed Intrusion Detection System (ODBN-IDS) for secure CPS environment. The proposed model pre-process the cloud network traffic data to improve its quality to next level. Here, a Binary Flower Pollination Algorithm (BFPA) is employed for feature selection process. The attained characteristics are used in optimal Deep Belief Networks (DBN) to detect the presence of intrusion in cloud data and produce alarms, in case of presence of intrusions. Equilibrium Optimizer Algorithm (EOA) is used to fine tune the hyperparameters in DBN model. A detailed set of simulations was conducted on benchmark datasets and the analysis results were compared. A detailed comparison was conducted for various models to satisfy the security requirements of cloud network and the results established the supremacy of the proposed ODBN-IDS model. [ABSTRACT FROM AUTHOR]

Early detection of preterm labor is important to avoid neonatal death and mortality. Uterine electromyography (UEMG) or electrohysterography is a non-invasive method of extracting electrical activity signal from the abdominal part during pregnancy, which helps in early detection. This signal can be used to classify term and preterm labors. Herein, the performances of four classifiers have been evaluated using seven nonlinear features extracted from UEMG signals. They were then compared with four features analyzed from different literature. The results show that with the Elman neural network classifier, the bi-spectrum feature, which has phase information, outperforms other features with 99.8875% accuracy, 100% sensitivity, and 99.77% specificity. [ABSTRACT FROM AUTHOR]