The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with using two entropy-based approaches (i.e., information gain (IG) and gain ratio (GR)) to select and extract relevant features in various ratios. Then, mathematical set theory (union and intersection) is used to extract the best features. The model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms: Bagging, Multilayer Perception, J48, and IBk. Our approach has resulted in 11 and 28 relevant features (out of 86) using the intersection and union, respectively, on IoTID20 and resulted 15 and 25 relevant features (out of 41) using the intersection and union, respectively, on NSL-KDD. We have further compared our approach with other state-of-the-art studies. The comparison reveals that our model is superior and competent, scoring a very high 99.98% classification accuracy.
Introduction:To address United Nations Millennium Develop ment Goal 4 (MDG 4) on reducing childhood mortality rates by two-thirds by 2015, there is a need for better population-based data on the rates and causes of neonatal death. This study aims to identify the risk factors of neonatal mortality in Bangladesh.
Materials and Methods:The study used data from the nationally representative 2007 Bangladesh Demographic and Health Survey. The survey gathered information regarding socioeconomic, demographic, environmental and maternal and child health care of 10,996 ever married women and 6,058 children. Both bivariate and multivariate statistical analyses were used to assess the relationship between neonatal mortality and contextual factors. Results: The prevalence of neonatal mortality was 37/1,000. The statistical analyses yielded quantitatively important and reliable estimates of neonatal death. The multivariate logistic regression analysis yielded significantly increased risk of neonatal mortality for children with mother who had no formal education, the Muslims, whose mother were adolescents of age 15-19, first ranked birth and twin babies. Conclusion: Emphasis should be given to improve female education in Bangladesh for a better chance of satisfying important factors that can improve infant survival: the quality of infant feeding, general care, household sanitation, and adequate use of preventive and curative health services.
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