This paper uses a hybrid feature selection process and classification techniques to classify cyber‐attacks in the UNSW‐NB15 dataset. A combination of k‐means clustering, and a correlation‐based feature selection, were used to come up with an optimum subset of features and then two classification techniques, one probabilistic, Naïve Bayes (NB), and a second, based on decision trees (J48), were employed. Our results show that this hybrid feature selection method in combination with the NB model was able to improve the classification accuracy of most attacks, especially the rare attacks. The false alarm rates were lower for most of the attacks, and particularly the rare attacks, with this combination of feature selection and the NB model. The J48 decision tree model, however, did not perform any better with the feature selection, but its classification rate for all attack families was already very high, with or without feature selection.

Background We determined how the vaginal and penile microbiomes contribute to HSV-2 serostatus within sexual partnerships. Methods Microbiomes were characterized in cervicovaginal lavage and penile meatal swab through high-throughput 16s ribosomal RNA gene amplicon sequencing. HSV-2 antibody was detected in serum. We modeled vaginal and penile taxa and covariates contributing to HSV-2 status in women and men using bivariate probit. Results Among 231 couples, HSV-2 was detected in: 78 (33.8%) both the man and woman, 52 (22.5%) woman only, 27 (11.7%) man only, 74 (32.0%) neither. Women were median age 22 years, 10.9% with HIV, 21.4% with Bacterial vaginosis (BV). Men were median age 26 years, 11.8% with HIV, 55.0% circumcised. In analysis adjusted for sociodemographics and BV, enrichment of vaginal Gardnerella vaginalis and Lactobacillus iners was associated with increased likelihood of HSV-2 in women and HSV-2 in male partners. Penile taxa (including Ureaplasma and Aerococcus) were associated with HSV-2 in women. Conclusions We demonstrate that penile taxa are associated with HSV-2 in female partners, and vaginal taxa are associated with HSV-2 in male partners. Our findings suggest that couples-level joint consideration of genital microbiome and STI or related outcomes could lead to new avenues for prevention.

BackgroundPenile microbiome composition has been associated with HSV-2 and HIV in men and with bacterial vaginosis (BV) and HSV-2 in female sex partners. This study sought to 1) characterize penile microbiome composition over a 1-year period and 2) identify factors associated with penile microbiome composition over time.MethodsThis prospective study of community-recruited heterosexual couples in Kenya measured penile and vaginal microbiomes via 16S ribosomal RNA gene amplicon sequencing at 4 time points over 1 year (1, 6, and 12 months after baseline). We used longitudinal mixed-effects modeling to assess associated demographic, behavioral, and disease factors and changes in community type, meatal taxa with the highest mean relative abundance, and alpha and beta diversity measures. We estimated group-based trajectories to elucidate compositional trends.ResultsAmong 218 men with 740 observations, men had a median age of 26 years, 11.6% were living with HIV, and 46.1% were HSV-2 seropositive. We identified 7 penile community types that varied with circumcision status, female partner vaginal microbiome community state type (CST), condom use, and penile washing. Across varying analytic approaches, 50%–60% of men had stable penile microbiome compositions. Alpha diversity measures were lower for circumcised men and those who reported condom use; they were stable over time but higher if female partners had diverse CSTs or BV. BV was positively associated with the relative abundance of numerous individual penile taxa. The decreased Bray–Curtis similarity was more common for men with HSV-2, and HSV-2 was also associated with a lower relative abundance of Corynebacterium and Staphylococcus.ConclusionsOver a 1-year period, penile microbiome composition was stable for a substantial proportion of men and was influenced by men’s circumcision status, sexual practices, female partner’s vaginal CST and BV status, and men’s HSV-2 status. In the female genital tract, a diverse CST is often associated with poorer health outcomes. Our results contribute toward understanding whether this framework extends to the penile microbiome and whether diversity and the associated penile microbiome compositions influence susceptibility or resilience to poorer health outcomes in men. Focusing on understanding how these factors influence the penile microbiome may lead to therapeutic avenues for reduced HSV-2 and BV infections in men and their female sex partners.

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Representation of text is a significant task in Natural Language Processing (NLP) and in recent years Deep Learning (DL) and Machine Learning (ML) have been widely used in various NLP tasks like topic classification, sentiment analysis and language translation. Until very recently, little work has been devoted to semantic analysis in phishing detection or phishing email detection. The novelty of this study is in using deep semantic analysis to capture inherent characteristics of the text body. One-hot encoding was used with DL and ML techniques to classify emails as phishing or nonphishing. A comparison of various parameters and hyperparameters was performed for DL. The results of various ML models, Naïve Bayes, SVM, Decision Tree, as well as DL models, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM), were presented. The DL models performed better than the ML models in terms of accuracy, but the ML models performed better than the DL models in terms of computation time. CNN with Word Embedding performed the best in terms of accuracy (96.34%), demonstrating the effectiveness of semantic analysis in phishing email detection.