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.

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The ambition to achieve covering of secret messages into another file is implemented in many steganographic solutions, but usually the message is covered only into one file. In this paper the mechanism based on the method of steganography, with use of many cover files, is described. Proposal of the algorithm, based on Last Significant Bit method of steganography, is also described. The main contribution of this work is in the proposal of multi-carrier steganographic solution along with its implementation in the software application.