Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron neural network, decision tree, random forest, and K‐nearest neighbors. The principal component analysis (PCA) and logistic regression (LR) methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F‐measure, accuracy, and area under the curve (AUC‐ROC) were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC‐ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC‐ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications.

Fetal alcohol spectrum disorder (FASD) is an umbrella term for children’s conditions due to their mother having consumed alcohol during pregnancy. These conditions can be mild to severe, affecting the subject’s quality of life. An earlier diagnosis of FASD is crucial for an improved quality of life of children by allowing a better inclusion in the educational system. New trends in computer-based diagnosis to detect FASD include using Machine Learning (ML) tools to detect this syndrome. However, most of these studies rely on children’s images that can be invasive and costly. Therefore, this paper presents a study that focuses on evaluating an ANN to classify children with FASD using non-invasive and more accessible data. This data used comes from a battery of tests obtained from children, including psychometric, saccade eye movement, and diffusion tensor imaging (DTI). We study the different configurations of ANN with dense layers being the psychometric data that correctly perform the best with 75% of the outcome. The other models include a feature layer, and we used it to predict FASD using every test individually. Model obtained obtained an accuracy of 88.46% (psychometric, 74.07% (Antisaccadic), 72.24% (Prosaccadic), 88% (Memory guide saccade), and 75% (DTI). These results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. These results are an improvement on Zhang’s 2019 model, which used the same data with less accuracy level.

Pattern matching algorithms have been studied on numerous occasions, mainly focusing on performance because of the large amount of data used in a matching process. However, a strong focus on performance can entail particular issues like the lack of flexibility to match patterns. As a consequence, programming developers need to tweak matching algorithms in contortive ways or create new specialized ones altogether if their specific needs are not supported. Inspired by the self-replication behavior of cells in biology, we explore and evaluate the design and implementation of an algorithm to flexibly match patterns, named Matcher Cells. Through the composition of simple rules applied to cells, developers can adjust the matching semantics of this algorithm to different needs. We describe this algorithm using a pure functional language as a recipe for any Turing-complete programming language and then offer an object-oriented architecture for languages like Java. To show the flexibility of our proposal, we use a concrete implementation in TypeScript to describe two applications, from different domains, that use pattern matching in a stream of tokens. Additionally, we carry out performance and developer experience empirical evaluations with undergraduate students using Matcher Cells. Finally, we discuss the pros and cons of using a biological-based algorithm, exploiting the compositions of rules, to match patterns.