Abstract Open Web Applications Security Project (OWASP), an open-source community committed to serve application developers and security professionals has always accentuated on the dire consequences of web application vulnerabilities like SQLI, XSS, LDAP, and Buffer overflow attacks frequently occurring on the web application threat landscape. Since these attacks are difficult to comprehend, machine learning algorithms are often applied to this problem context for decoding anomalous patterns. This work explores the performance of algorithms like decision forest, neural networks, support vector machine, and logistic regression. Their performance has been evaluated using standard performance metrics. HTTP CSIC 2021, a web intrusion detection dataset is used in this study. Experimental results indicate that SVM and LR have been superior in their performance than their counterparts. Predictive workflows have been created using Microsoft Azure Machine Learning Studio (MAMLS), a scalable machine learning platform which facilitates an integrated development environment to data scientists.
The authors utilized numerous documents created by advisory groups, expert panels and multicultural focus groups to develop performance measures for assessing the cultural competency of mental health systems. To provide a national perspective, the focus groups–a total of 134 consumers, family members, advocates and providers–met in locations across the country: New York, Florida, South Carolina, South Dakota, and California. Competency was measured within three levels of organizational structure: administrative, provider network, and individual caregiver. Indicators, measures and data sources for needs assessment, information exchange, services, human resources, plans and policies, and outcomes were identified. Procedures for selection and implementation of the most critical measures are suggested. The products of this project are broadly applicable to the concerns of all cultural groups.