Regular boundary element method is employed for the variational formulation of Helmholtz equation that governs the waveguiding problems. Like in the Charge simulation method, in this method, the source points associated with the fundamental solutions are allocated outside the domain so that the singular integrals which occur in the standard boundary element procedure can be avoided. First, the formulation is developed for the two-dimensional scalar Helmholtz problem solving for the axial components of either electric or magnetic fields.The application of the formulation is shown for simple hollow rectangular waveguide and dielectric-slab-loaded rectangular waveguide. Then the formulation is extended for the analysis of dielectric waveguides of open type incorporating axial components of both electric and magnetic fields, for the solution of the propagating modes which are generally of hybrid types.To show the validity and quality of the formulation, it is applied to a circular step-index optical waveguide and a dielectric rectangular waveguide. Very close agreements have been found when the solutions are compared with the ones obtained by different methods. One distinct merit of the extended formulation is that it has been fixed to suppress the spurious solutions which are encountered while solved by the conventional boundary element method.
Divergence-free shape functions are proposed for the finite elements, with which inhomogeneously-Ioaded and arbitrarily-shaped waveguides are analysed. The methOd is based on vectorial finite element fonnulation employing edge elements. The shape functions used for the approximation of the fields are shown analytically to be divergence-free and as an evidence, the non-physical solutions that appeared in the longitudinal component finite element formulation have been shown to be absent. To show the validity of the elements, application is made for the analysis of rectangular waveguides loaded with dielectric slab and a waveguide with curved structure. The solutions obtained are compared with the analytical ones or the solutions reported elsewhere. The degree of accuracy has been found satisfactory.
The exponential growth of the Internet of Things (IoT) has led to the rapid expansion of interconnected systems, which has also increased the vulnerability of IoT devices to security threats such as distributed denial-of-service (DDoS) attacks. In this paper, we propose a machine learning pipeline that specifically addresses the issue of DDoS attack detection in IoT networks. Our approach comprises of (i) a processing module to prepare the data for further analysis, (ii) a dynamic attribute selection module that selects the most adaptive and productive features and reduces the training time, and (iii) a classification module to detect DDoS attacks. We evaluate the effectiveness of our approach using the CICI-IDS-2018 dataset and five powerful yet simple machine learning classifiers—Decision Tree (DT), Gaussian Naive Bayes, Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). Our results demonstrate that DT outperforms its counterparts and achieves up to 99.98% accuracy in just 0.18 s of CPU time. Our approach is simple, lightweight, and accurate for detecting DDoS attacks in IoT networks.
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