One of the most significant issues in wireless sensor networks (WSNs) is security, which must be addressed to keep WSNs safe from malicious attacks. An intrusion detection system (IDS) is essential in analyzing network traffic and detecting abnormal events. However, these IDSs suffer from several drawbacks that affect their effectiveness and flexibility in accuracy, so they must overcome these drawbacks to improve the performance of IDS. These drawbacks include difficulties in determining the appropriate dataset, the problem of feature selection, and the issue of the imbalanced dataset and choosing the appropriate algorithms for the classification process in WSN. In this paper, a model for an anomaly‐based IDS in WSNs is proposed. This model applied mutual information (MI) for feature selection and the synthetic minority oversampling technique (SMOTE) for solving the imbalanced dataset problem. It used different machine learning (ML) algorithms, random forest (RF), decision tree (DT), support vector machine (SVM), and K‐nearest neighbors (KNNs) to analyze network traffic and binary classification or multiclass classification. To implement and evaluate the performance of the proposed model, the standard dataset NSL‐KDD is used. Python language is used to implement the proposed model in the Anaconda platform, and many evaluation metrics are also utilized to evaluate the performance of the proposed method. Experimental results show that the proposed model can detect intrusions using different ML algorithms with high accuracy. The results of the proposed model for different ML algorithms outperform the state‐of‐the‐art algorithms, and the maximum enhancement reached 15% in the accuracy metric.

Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature’s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%.

Secure transmission of images over wireless communications systems can be done using RSA, the most known and efficient cryptographic algorithm, and OFDMA, the most preferred signal processing choice in wireless communications. This paper aims to investigate the performance of OFDMA system for wireless transmission of RSA-based encrypted images. In fact, the performance of OFDMA systems; based on different signal processing techniques, such as, discrete sine transforms (DST) and discrete cosine transforms (DCT), as well as the conventional discrete Fourier transforms (DFT) are tested for wireless transmission of gray-scale images with/without RSA encryption. The progress of transmitting the image is carried by firstly, encrypting the image with RSA algorithm. Then, the encrypted image is modulated with DFT-based, DCT-based, and DST-based OFDMA systems. After that, the modulated images are transmitted over a wireless multipath fading channel. The reverse operations will be carried at the receiver, in addition to the frequency domain equalization to overcome the channel effect. Exhaustive numbers of scenarios are performed for study and investigation of the performance of the different OFDMA systems in terms of PSNR and MSE, with different subcarriers mapping and modulation techniques, is done. Results indicate that the ability of different OFDMA systems for wireless secure transmission of images. However, the DCT-OFDMA system showed superiority over the DST-OFDMA and the conventional DFT-OFDMA systems.

A single carrier frequency division multiple access (SC-FDMA), is a technique thathas a similar performance and essentially the same overall complexity as those of orthogonalfrequency division multiple access (OFDMA). The main advantage of SC-FDMA overOFDMA is its lower peak-to-average power ratio (PAPR). In this paper, an enhancement of aSC-FDMA by decreasing the PAPR is addressed. An efficient repeated clipping and filtering(RCF) algorithm is applied to a SC-FDMA system, which results in a significant PAPRreduction. System performance remains the same after applying the RCF algorithm.Simulations show the proposed scheme provides a bit error as that of the conventional system.That is, due to the selection of the optimum clipping ratio (CR). For more accurate validation,simulations of the conventional system and the proposed system have been performed usingdifferent types of channels. The proposed scheme provides a PAPR reduction by a gain ofapproximately 4 dB with a slight increase of complexity.

Partial transmit sequence (PTS) and selective mapping (SLM), are two algorithmsused for peak-to-average power ratio (PAPR) reduction in multicarrier transmissions. In thispaper, these techniques are applied for single carrier transmission system enhancement. Theadvantage of these algorithms, that they have no effect on the bit error (BER) of the system.But it will increase the complexity of the system due to the need for additional inversediscrete fourier transform IDFT calculations, search complexity for optimum phase factor.These algorithms are implemented on a single carrier frequency division multiple access (SCFDMA)system. SC-FDMA is used in Long Term Evaluation (LTE) of 3rd GenerationPartnership Project (3GPP) as a strong candidate for uplink multiple access scheme.Simulations show that the proposed system with these two algorithms introduces a significantPAPR reduction compared to conventional system. There is a tradeoff between the PAPRreduction and the transmitter complexity. Due to the need for additional IDFT blocks, thecomplexity increased and due to side information, data rate has small decrease. Differentsimulation parameters are used for these algorithms which provide a good indication forselection of optimum technique with optimum parameters.