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.

Recently proposed lattice-based cryptography algorithms can be used to protect the IoT communication against the threat from quantum computers, but they are computationally heavy. In particular, polynomial convolution is one of the most time-consuming operations in lattice-based cryptography. To achieve efficient implementation, the Number Theoretic Transform (NTT) algorithm is an ideal choice, but it has certain limitations on the parameters, which not all lattice-based schemes can employ directly. Hence, alternative techniques are proposed to accelerate polynomial convolution on lattice-based schemes that cannot utilize the NTT directly. In this paper, we propose a parallel Toeplitz matrix-vector product (TMVP) version to accelerate the polynomial convolution in PQC algorithms implemented it on a graphics processing unit (GPU). This is the first time a TMVP parallel version has been proposed and experimented on different GPU cores (i.e., CUDA-cores and Tensor-cores). The effectiveness of the proposed solution is validated on Saber (the NIST post-quantum standardization finalist) and Sable (an improved version of Saber) schemes. Experimental results show that TMVP-based polynomial convolution using CUDA-cores fails to exhibit a significant enhancement compared to the schoolbook CUDA-core method already proposed by Hafeez et al. 2023. However, when the TMVP technique is applied to Tensor-cores, it outperformed state-of-the-art implementations. The proposed Tensor-core approach outperformed the schoolbook Tensor-core method by up to 1.21×, and outperformed the dot-product-instructions method (Lee et al. 2022) by up to 3.63×. The proposed TMVP Tensor-cores is also faster than the TMVP CUDA-cores method by 13.76×.

High throughput key encapsulations and decapsulations are needed by Internet of Things (IoT) applications in order to simultaneously process a multitude of small data in secure communication. In this article, we present two novel techniques for accelerating the implementation of polynomial convolution on a graphics processing unit (GPU), utilizing advanced Tensor cores, which benefit the performance of key encapsulations. First, a polynomial restructuring technique is proposed to allow several polynomials with distinct public keys to be processed in a single communication cycle. This is an improvement compared to the previous work by Lee et al. Next, we observe that polynomial convolution in some key encapsulation mechanisms contains reduction patterns that are not friendly to parallel implementation. We propose separating the multiplication and reduction processes so they can be parallelized independently. To verify the effectiveness of our proposed techniques, we applied it to two key-encapsulation mechanisms from the Scabbard post-quantum key-encapsulation mechanism suite and evaluate their performance. Experimental results show that polynomial convolution using Tensor cores is $1.05\times $ faster (for the Florete scheme) and $3.6\times $ faster (for the Sable scheme) than using compute unified device architecture core-based multiplication with conventional cores on a GPU. The Tensor cores-based encapsulations and decapsulations are faster than a reference implementation on a CPU supporting AVX2 by more than $5.6\times $ and $6.4\times $ , respectively, for the Florete scheme and $8.3\times $ and $13.3\times $ faster, respectively, for the Sable scheme. This shows that the proposed techniques can achieve significantly higher throughput for key exchange and encapsulation mechanisms, which are important for securing IoT applications.

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.

This study presents a novel approach to cryptographic algorithm design that harnesses the power of recurrent neural networks. Unlike traditional mathematical-based methods, neural networks offer nonlinear models that excel at capturing chaotic behavior within systems. We employ a recurrent neural network trained on Monte Carlo estimation to predict future states and generate confusion components. The resulting highly nonlinear substitution boxes exhibit exceptional characteristics, with a maximum nonlinearity of 114 and low linear and differential probabilities. To evaluate the efficacy of our methodology, we employ a comprehensive range of traditional and advanced metrics for assessing randomness and cryptanalytics. Comparative analysis against state-of-the-art methods demonstrates that our developed nonlinear confusion component offers remarkable efficiency for block-cipher applications.

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.