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This paper presents an improvement in the recognition of faulty signals, encountered in the case of the Gazelle helicopter's main rotor, using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. The main focus is on the distinction between faulty and healthy signals and then between the three subclasses of faulty signals, i.e. faulty bearings, joints problem and mechanical loosening. This research work is divided into three parts. The first part approaches the two above-mentioned classes of signals at the same time, and, to this purpose, the Linear Discriminant Analysis (LDA), Non Linear Discriminant Analysis (NLDA) and Back-propagation Neural Network (BPNN) are used. In the second and third part of the paper, GA and PSO are employed for optimizing the hyperplanes and hypersurfaces which separate the above-mentioned classes of signals, as well as the architecture and connection weights of a neural network (NN). Real data are used, which correspond to the vibration signals measured during periodic technical inspections, and are characterized by amplitudes and frequencies typical of the eight highest peaks of the Welch spectrum. The results obtained confirm the validity of the above-mentioned approaches and comparable favorably with those of other multivariate methods. The GA-or PSO-based neural networks` diagnosis can therefore be established for helicopter computers so that faults can be detected.

Denial of Service (DoS/DDoS) intrusions are damaging cyberattacks, and their identification is of great interest to the Intrusion Detection System (IDS). Existing IDS are mainly based on Machine Learning (ML) methods including Deep Neural Networks (DNN), but which are rarely hybridized with other techniques. The intrusion data used are generally imbalanced and contain multiple features. Thus, the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017, CSE-CICIDS2018 and CICDDoS 2019 datasets, according to the following key points. a) Three imbalanced CICIDS2017-2018-2019 datasets, including Benign and DoS/DDoS attack classes, are used. b) A new technique based on K-means is developed to obtain semi-balanced datasets. c) As a feature selection method, LDA (Linear Discriminant Analysis) performance measure is chosen. d) Four metaheuristic algorithms, counting Artificial Immune System (AIS), Firefly Algorithm (FA), Invasive Weeds Optimization (IWO) and Cuckoo Search (CS) are used, for the first time together, to increase the performance of the suggested DNN-based DoS attacks detection. The experimental results, based on semi-balanced training and test datasets, indicated that AIS, FA, IWO and CS-based DNNs can achieve promising results, even when cross-validated. AIS-DNN yields a tested accuracy of 99.97%, 99.98% and 99.99%, for the three considered datasets, respectively, outperforming performance established in several related works.