No abstract

This paper focuses on training machine learning models using the XGBoost and extremely randomized trees algorithms on two datasets obtained using static and dynamic analysis of real malicious and benign samples. We then compare their success rates—both mutually and with other algorithms, such as the random forest, the decision tree, the support vector machine, and the naïve Bayes algorithms, which we compared in our previous work on the same datasets. The best performing classification models, using the XGBoost algorithm, achieved 91.9% detection accuracy and 98.2% sensitivity, 0.853 AUC, and 0.949 F1 score on the static analysis dataset, and 96.4% accuracy and 98.5% sensitivity, 0.940 AUC, and 0.977 F1 score on the dynamic analysis dataset. Then, we exported the best performing machine learning models and used them in our proposed MLMD program, automating the process of static and dynamic analysis and allowing the trained models to be used for classification on new samples.

No abstract

No abstract

This paper deals with the issue of the brain-computer interface (BCI)-the human-machine interface (HMI) based on acquisition, analysis and transformation of signals generated by the central nervous system (CNS) as the manifestation of its normal function. Brain-computer interface can be seen as the bridge that is building up direct one-way or two-way communication pathway between the brain and the external technical device. Paper introduces techniques based on non-invasive functional imaging of the brain used for data acquisition in non-invasive brain computer interfaces, and is focused on the technique that is reading neural activity of the brain with use of multi-channel electroencephalograph (EEG). As the part of this paper we are introducing our experience with the low-cost commercially available equipment Emotiv EPOC Neuroheadset based on this technology.