Detecting Malicious DNS over HTTPs (DoH) Connections via Machine Learning Techniques

DoH is a modern protocol used as an alternative to the existing DNS protocol, which provides confidentiality and integrity to DNS functions by using protected channels. Since this kind of connection can pass through the current protection systems, it can be used for spreading malicious software. There is a need to find defense mechanisms that can detect and prevent these forms of malicious behaviors. In this study, we propose a method to classify malicious DoH connections using machine learning techniques, and we propose a feature selection process which reduced the number of used features till 27% of the total 33 features, and resulted improved the detection level of the malicious DoH connections. The study involves employing twelve different supervised machine learning classifiers, and the designed feature selection process used 8 different feature selection methods based on machine learning techniques for counting the importance of the features. The reached results were promising since the accuracy scores were about 100% in detecting malicious DoH connections.

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Predicting the Political Sentiment of Web Log Posts Using Supervised Machine Learning Techniques Coupled with Feature Selection

Advances in Web Mining and Web Usage Analysis - Lecture Notes in Computer Science ◽

10.1007/978-3-540-77485-3_11 ◽

2007 ◽

pp. 187-206 ◽

Cited By ~ 18

Author(s):

Kathleen T. Durant ◽

Michael D. Smith

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

The Political ◽

Web Log ◽

Learning Techniques

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Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

Current Medicinal Chemistry ◽

10.2174/2213275912666191102162959 ◽

2020 ◽

Vol 28 (2) ◽

pp. 253-265 ◽

Cited By ~ 3

Author(s):

Gabriela Bitencourt-Ferreira ◽

Amauri Duarte da Silva ◽

Walter Filgueira de Azevedo

Keyword(s):

Machine Learning ◽

Binding Affinity ◽

Predictive Performance ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Scoring Functions ◽

Cyclin Dependent Kinase ◽

Learning Models ◽

Learning Techniques ◽

Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

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Local mortality estimates during the COVID-19 pandemic in Italy

Journal of Population Economics ◽

10.1007/s00148-021-00857-y ◽

2021 ◽

Author(s):

Augusto Cerqua ◽

Roberta Di Stefano ◽

Marco Letta ◽

Sara Miccoli

Keyword(s):

Machine Learning ◽

Excess Mortality ◽

Control Method ◽

Local Level ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Mortality Data ◽

Official Method ◽

Learning Techniques ◽

Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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