Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation.

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have applied our previously proposed CC-based FS with random feature grouping (CCFSRFG) to a benchmark cybersecurity dataset as the preprocessing step. The dataset with original features and the dataset with a reduced number of features were used for infrequent pattern detection. Experimental analysis was performed and evaluated using 10 unsupervised anomaly detection techniques. Therefore, the proposed infrequent pattern detection is termed Unsupervised Infrequent Pattern Detection (UIPD). Then, we compared the experimental results with and without FS in terms of true positive rate (TPR). Experimental analysis indicates that the highest rate of TPR improvement was by cluster-based local outlier factor (CBLOF) of the backdoor infrequent pattern detection, and it was 385.91% when using FS. Furthermore, the highest overall infrequent pattern detection TPR was improved by 61.47% for all infrequent patterns using clustering-based multivariate Gaussian outlier score (CMGOS) with FS.

Diabetes and impaired fasting glycemia in the tribes of Khagrachari hill tracts of Bangladesh.

OBJECTIVE: To determine the prevalence of type 2 diabetes and impaired fasting glycemia (IFG) in a tribal population of Bangladesh. RESEARCH DESIGN AND METHODS: A cluster sampling of 1,287 tribal subjects of age > or =20 years was investigated. They live in a hilly area of Khagrachari in the far northeast of Bangladesh. Fasting plasma glucose, blood pressure, height, weight, waist girth, and hip girth were measured. Lipid fractions were also estimated. We used the 1997 American Diabetes Association diagnostic criteria. RESULTS: The crude prevalence of type 2 diabetes was 6.6% and IFG was 8.5%. The age-standardized (20-70 years) prevalence of type 2 diabetes (95% CI) was 6.4% (4.96-7.87) and of IFG was 8.4% (6.48-10.37). Both tribesmen and women had equal risk for diabetes and IFG. Compared with the lower-income group, the participants with higher income had a significantly higher prevalence of type 2 diabetes (18.8 vs. 3.1%, P < 0.001) and IFG (17.2 vs. 4.3%, P < 0.001). Using logistic regression, we found that increased age, high-income group, and increased central obesity were the important risk factors of diabetes. CONCLUSIONS: The prevalence of diabetes in the tribal population was higher than that of the nontribal population of Bangladesh. Older age, higher central obesity, and higher income were proven significant risk factors of diabetes. High prevalence of diabetes among these tribes indicates that the prevalence of diabetes and its complications will continue to increase. Evidently, health professionals and planners should initiate diabetes care in these tribal communities.