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Cyber attacks are one of the world's most serious challenges nowadays. A Distributed Denial of Service (DDoS) attack is one of the most common cyberattacks that has affected availability, which is one of the most important principles of information security. It leads to so many negative consequences in terms of business, production, reputation, data theft, etc. It shows the importance of effective DDoS detection mechanisms to reduce losses. In order to detect DDoS attacks, statistical and data mining methods have not been given good accuracy values. Researchers get good accuracy values while detecting DDoS attacks by using classification algorithms. But researchers, use individual classification algorithms on generalized DDoS attacks. This study used six machine learning classification algorithms to detect eleven different DDoS attacks on different DDoS attack datasets. We used the CICDDoS2019 dataset which is collected from the Canadian Institute of Cyber security in this study. It contains eleven different DDoS attack datasets in CSV file format. On each DDoS attack, we evaluated the effectiveness of the classification methods Logistic regression, Decision tree, Random Forest, Ada boost, KNN, and Naive Bayes, and determined the best classification algorithms for detection.

Trace element concentrations play an important role in grouping and provenance studies of archaeological artifacts. Particle‐inducedX‐ray emission (PIXE) using low energy proton beams (1.5 and 3 MeV) from a tandem particle accelerator was used to analyze the ancient clay potteries collected from an ancient Buddhist site in India. Concentrations of 13 elements, including eight transition elements, were determined. The concentrations of transition elements were used for the grouping study by statistical cluster analysis according to the similar and dissimilar elemental distribution within the samples. Two International Atomic Energy Agency Reference Materials (RMs) were analyzed to validate the PIXE method. Two samples were analyzed by both PIXE and instrumental neutron activation analysis (INAA) as a part of quality assurance. Grouping results were used for preliminary provenance study. Copyright © 2017 John Wiley & Sons, Ltd.

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