Network security plays a critical role in our lives based on the threats and attacks to which we are exposed and that increase daily; these attacks result in the need to develop different protection methods and techniques. Network intrusion detection systems (NIDSs) are a way to detect several malicious network attacks. Many researchers have focused on developing NIDSs based on machine learning (ML) approaches to detect variants of attacks. ML approaches can automatically discover the essential variances between normal and abnormal data by analysing the features of a large dataset. Indeed, many features are extracted without discrimination, increasing the computational complexity. By applying a feature selection method, a subset of features is selected from the whole feature set with the aim of improving the performance of MLbased detection methods. The salp swarm algorithm (SSA) is a nature-based optimization algorithm that has demonstrated efficiency in minimizing processing challenges to perform optimization for feature selection problems. This research investigates the impact of the SSA on improving ML-based network anomaly detection using different ML classifiers, including XGBoost and Naïve Bayes algorithms. Experiments were conducted on standard datasets for comparison purposes; two datasets are used explicitly for network intrusion attacks: UNSW-NB15 and NSL-KDD. The experimental results show that the proposed method performs better in improving anomaly NIDSs in terms of the f-measure, recall, detection rate, and false alarm rate on both datasets. It outperforms the state-of-the-art techniques recently proposed in the literature.

DNA methylation acts as a major epigenetic modification in mammals, characterized by the transfer of a methyl group to a cytosine. DNA methylation plays a pivotal role in regulating normal development, and misregulation in cells leads to an abnormal phenotype as is seen in several cancers. Any mutations or expression anomalies of genes encoding regulators of DNA methylation may lead to abnormal expression of critical molecules. A comprehensive genomic study encompassing all the genes related to DNA methylation regulation in relation to breast cancer is lacking. We used genomic and transcriptomic datasets from the Cancer Genome Atlas (TGCA) Pan-Cancer Atlas, Genotype-Tissue Expression (GTEx) and microarray platforms and conducted in silico analysis of all the genes related to DNA methylation with respect to writing, reading and erasing this epigenetic mark. Analysis of mutations was conducted using cBioportal, while Xena and KMPlot were utilized for expression changes and patient survival, respectively. Our study identified multiple mutations in the genes encoding regulators of DNA methylation. The expression profiling of these showed significant differences between normal and disease tissues. Moreover, deregulated expression of some of the genes, namely DNMT3B, MBD1, MBD6, BAZ2B, ZBTB38, KLF4, TET2 and TDG, was correlated with patient prognosis. The current study, to our best knowledge, is the first to provide a comprehensive molecular and genetic profile of DNA methylation machinery genes in breast cancer and identifies DNA methylation machinery as an important determinant of the disease progression. The findings of this study will advance our understanding of the etiology of the disease and may serve to identify alternative targets for novel therapeutic strategies in cancer.