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Mobile botnets are a growing threat to the internet security field. These botnets target less secure devices with lower computational power, while sometimes taking advantage of features specific to them, e.g., SMS messages. We propose a host-based approach using machine learning techniques to detect mobile botnets with features derived from system calls. Patterns created tend to be shared among applications with similar actions. Therefore, different botnets are likely to share similar system call patterns. To measure the effectiveness of our approach, a dataset containing multiple botnets and legitimate applications was created. We carried out three experiments, namely finding out the best time-window, and performing feature selection and hyperparameter tuning. A high performance (over 84%) was achieved in multiple metrics across multiple machine learning algorithms. An in-depth analysis of the features is also presented to help future work with a solid discussion about system call-based features.

Abstract: This paper introduces a new tool for pattern recognition. Called the Discriminative Paraconsistent Machine (DPM), it is based on a supervised discriminative model training that incorporates paraconsistency criteria and allows an intelligent treatment of contradictions and uncertainties. DPMs can be applied to solve problems in many fields of science, using the tests and discussions presented here, which demonstrate their efficacy and usefulness. Major difficulties and challenges that were overcome consisted basically in establishing the proper model with which to represent the concept of paraconsistency. [Copyright &y& Elsevier]

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.