Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%.
The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic routing information communication and an unprotected public media make them easy targets for a wide variety of security threats. IDSs are helpful tools that can detect and prevent system vulnerabilities in a network. Unfortunately, there is no possibility to construct advanced protective measures within the basic infrastructure of the WSN. There seem to be a variety of machine learning (ML) approaches that are used to combat the infiltration issues plaguing WSNs. The Slime Mould Algorithm (SMA) is a recently suggested ML approach for optimization problems. Therefore, in this paper, SMA will be integrated into an IDS for WSN for anomaly detection. The SMA’s role is to reduce the number of features in the dataset from 41 to five features. The classification was accomplished by two methods, Support Vector Machine with polynomial core and decision tree. The SMA showed comparable results based on the NSL-KDD dataset, where 99.39%, 0.61%, 99.36%, 99.42%, 99.33%, 0.58%, and 99.34%, corresponding to accuracy, error rate, sensitivity, specificity, precision, false positive rate, and F-measure, respectively, are obtained, which are significantly improved values when compared to other works.
In line fiber Mach–Zehnder inferometer (MZI) pulse compression was designed three different lengths of single mode-polarization maintaining fiber with (8, 16, 24) cm after splicing them between two single mode fibers (SMF-28e) with (23 and 13) cm and applying different weights on splicing region and the cross sectional area of SM-PM fiber, the designed performance of the in line fiber compressor system was studies in terms of compressor factor. Two minima pulse compression factor were obtained, one is 1.13 with FWHM 251.584 pm, centered wavelength 1547.394 nm, 52 cm interferometer length and 5 g was applied on the micro-cavity splicing region, and the second is equal 1.10 with FWHM 259.730 pm, centered wavelength 1547.120 pm and, 68 cm interferometer length and 10 g was applied on the cross sectional area of the second PMFs, in the case of single and cascaded interferometers, respectively. The input of the all interferometers was pulsed laser source with peak power 1.2297 mW, 286 pm spatial FWHM, 10 ns temporal FWHM, 3 kHz repetition rate and centered at 1546.7 nm.
To create a new transmission phenomenon, the availability of good data rates, the development of the internet of things (IoT), and different machine type communications (MTC) developed the ability to converse without synchronization, or the need for routing overhead in the form of synchronization overhead, while using mixed legacy fourth generation (4G) systems, based on orthogonal frequency division multiplexing, cannot meet these requirements (OFDM). Many waveform alternatives for OFDM have been proposed, including FBMC, GFDM, UFMC, and filtering-OFDM, in order to meet the criteria of the next generation system F-OFDM. As a consequence, a revolutionary filter based on a flat top weighted window is described in this work, where simulation results indicate that the suggested method outperforms prior designs in regard to spectral adeptness increased substantially acrros the deminish in the required side-information as overhead of the synchronization process. The trasitional F-OFDM uses root-raised-cosine filter. However, in this paper, novel filter design was proposed, whis is simple and real-time construction capability. This new filter is built around legacy filters, where the new filter is a combination of different filters to make use of various advantages of the old filters and get rid of the disadvangages. The power spectral density (PSD), -157.2 dBW/Hz, which is outstanding CP-OFDM-PSD, -50 dBW/Hz.
In this study, an optical frequency comb source (OFCS) based on a dual-drive Mach–Zehnder modulator (MZM) is constructed and theoretically demonstrated. A mathematical model of the constructed OFCS is then built to investigate the effect of the peak-to-peak radio frequency (RF) signals applied to the MZM arms on the generated optical frequency comb (OFC) lines at the MZM output. A dual-drive MZM, a continuous wave laser source, and an RF signal source are included in the OFCS. The chirp parameter can be controlled and 64 comb lines generated at a comb spacing of 25 GHz by regulating voltages applied to the MZM arms. The developed OFCS is relatively simple but valuable. The generated OFC lines can be used for high data-rate transmission.
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