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This paper presents the employment of a DPA attack on the NIST (National Institute of Standards and Technology) standardized AES (advance encryption standard) protocol for key retrieval and prevention. Towards key retrieval, we applied the DPA attack on AES to obtain a 128-bit secret key by measuring the power traces of the computations involved in the algorithm. In resistance to the DPA attack, we proposed a countermeasure, or a new modified masking scheme, comprising (i) Boolean and (ii) multiplicative masking, for linear and non-linear operations of AES, respectively. Furthermore, we improved the complexity involved in Boolean masking by introducing Rebecca’s approximation. Moreover, we provide a novel solution to tackle the zero mask problem in multiplicative masking. To evaluate the power traces, we propose our custom correlation technique, which results in a decrease in the calculation time. The synthesis results for original implementation (without countermeasure) and inclusion of countermeasure are given on a Zynq 7020 FPGA (Artix-7 device). It takes 424 FPGA slices when implemented without considering the countermeasure, whereas 714 slices are required to implement AES with the inclusion of the proposed countermeasure. Consequently, the implementation results provide the acceptability of this work for area-constrained applications that require prevention against DPA attacks.

An unmanned free-swimming submersible (UFSS) is designed to perform certain tasks in water without interposing humans. The vehicle’s control is achieved by integrating mathematical (analog) and non-mathematical (embedded) controllers. The main goal of integrated controllers is to overcome the environmental disturbances and noise of the sensor data. These disturbances, as well as the noise data, are generated during steering, diving, and speed control. The amplitude of disturbances and noise varies with the depth and intensity of water waves. This article presents a robust hybrid linear quadratic regulator (HLQR) controller for UFSS. The presented controller targets the desired state of the UFSS in the presence of a disturbing environment. The hybrid approach is achieved by employing: (1) two linear quadratic regulators or controllers and (2) a mathematical structure of the Riccati equation. Consequently, the proposed HLQR controller is integrated into the UFSS system to evaluate the response in terms of settling time, rise time, overshoot, and steady-state error. Furthermore, the robustness of the HLQR is investigated by considering the feedback to step response and hydrodynamic disturbances. The implementation results reveal that the proposed controller outperforms state of the art controllers, such as proportional-integral-derivative and lead-compensator controllers.

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.