In spectrum sensing issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature selection. This paper proposes a new hybrid optimization algorithm to optimize feature selection for a Deep Neural Network (DNN) classifier. To surpass the premature convergence problem and improve the exploitation ability of the original Black Widow Optimization Algorithm (BWO), we mix a modified version of BWO and Particle Swarm Optimization (PSO), called MBWPSO. The aim is to enhance the performance of a blind spectrum sensing approach in the context of cognitive radio (CR) for wireless communications. Computer simulations show that the MBWPSO algorithm outperforms the original one and a set of state-of-the-art algorithms (i.e., HS, BBO, PSO, and SA) algorithms. The MBWPSO also exhibits the best performance once applied for feature selection in the above context

This paper proposes blind spectrum sensing (SS) in a narrowband context called Beamforming Fusion assisted Spectrum Sensing (BFSS). Considering a channel with angles of arrival (AoA), we jointly exploit beamforming algorithms to make decisions about the detection of users on frequency resources. The proposed method is totally blind and does not require knowledge of the noise power, the channel estimation, and the source signal. A state-of-the-art comparison of SS methods using beamforming is provided to validate our contribution in a shallow SNR region.

A machine learning algorithm aims at designing a mathematical model based on a given training data set. Generally, the built model has a set of parameters that need to be adjusted. Since the performance of a given model depends on its settings, the parameters have to be carefully chosen through a fine-tuning step. A good model selection not only boosts performance but also allows a well-generalized model, i.e., a model that works sound on unseen data. In this paper, we assess the effectiveness of some metaheuristic optimization algorithms for support-vector machines (SVM) model selection. Computer simulations show that optimization algorithms that overall outperforms other algorithms using benchmark functions can be, further, definitely used for an efficient SVM model selection for classification. Thus, we show that Teaching–Learning-Based Optimization algorithm is faster and also enables the most accurate classification, even against other proposed methods in the literature for SVM model selection.

In this paper, a new selective encryption scheme based on DWT, AES S-box and chaotic permutation is proposed. The new scheme is composed of six steps: Image decomposition, Block permutation, DWT decomposition, substitution phase, chaotic permutation phase and reconstruction phase. Firstly, it generates four subbands, namely cAP, cVP, cHP and cDP, and encrypts only cAP subband, which contains the meaningful part of data. The proposed cryptosystem is evaluated using various security and statistical analysis. The performance tests show that the proposed scheme is secure against statistical and differential attacks.

The substitution box is an essential element in cryptographic algorithms. Indeed, the S-box guarantees the confusion and nonlinearity properties required by secure block ciphers. In this study, we choose the Rossler system to generate a chaotic S-box. Proposed approach is tested for six criteria, which are bijectivity, nonlinearity, strict avalanche criterion (SAC), input/output XOR distribution and linear approximation probability (LP). In order to test the robustness and the suitability of the proposed S-box in real time application, especially in image encryption, we evaluate the statistical analysis of the substituted image such as, correlation, histogram and information entropy. Comparative studies with some selected S-boxes have been performed to prove the effectiveness of the proposed S-box. Experimental results and simulations show that the proposed Sbox is powerful against attacks.