Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the “classical” crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim’s machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird’s eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection.

In this article, the problem of detecting JPEG images, which have been steganographically manipulated, is discussed. The performance of employing various shallow and deep learning algorithms in image steganography detection is analyzed. The data, images from the BOSS database, were used with information hidden using three popular steganographic algorithms: JPEG universal wavelet relative distortion (J-Uniward), nsF5, and uniform embedding revisited distortion (UERD) at two density levels. Various feature spaces were verified, with the discrete cosine transform residuals (DCTR) and the Gabor filter residuals (GFR) yielding best results. Almost perfect detection was achieved for the nsF5 algorithm at 0.4 bpnzac density (99.9% accuracy), while the detection of J-Uniward at 0.1 bpnzac density turned out to be hardly possible (max. 56.3% accuracy). The ensemble classifiers turned out to be an encouraging alternative to deep learning-based detection methods.

In this article, we address the problem of detecting anomalies in system log files. Computer systems generate huge numbers of events, which are noted in event log files. While most of them report normal actions, an unusual entry may inform about a failure or malware infection. A human operator may easily miss such an entry; therefore, anomaly detection methods are used for this purpose. In our work, we used an approach known from the natural language processing (NLP) domain, which operates on so-called embeddings, that is vector representations of words or phrases. We describe an improved version of the LogEvent2Vec algorithm, proposed in 2020. In contrast to the original version, we propose a significant shortening of the analysis window, which both increased the accuracy of anomaly detection and made further analysis of suspicious sequences much easier. We experimented with various binary classifiers, such as decision trees or multilayer perceptrons (MLPs), and the Blue Gene/L dataset. We showed that selecting an optimal classifier (in this case, MLP) and a short log sequence gave very good results. The improved version of the algorithm yielded the best F1-score of 0.997, compared to 0.886 in the original version of the algorithm.

This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected classifiers, the k-Nearest Neighbor (k-NN), the Artificial Neural Network (ANN) and Support Vector Machines (SVMs), were used in experiments. SVMs turned out to provide the best classification accuracy of 75.44% in the speaker dependent mode, that is, when speech samples from the same speaker were included in the training corpus. Various speaker dependent and speaker independent configurations were analyzed and compared. Emotion recognition in speaker dependent conditions usually yielded higher accuracy results than a similar but speaker independent configuration. The improvement was especially well observed if the base recognition ratio of a given speaker was low. Happiness and anger, as well as boredom and neutrality, proved to be the pairs of emotions most often confused.

In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corpus. Next, using neural machine translation, we developed a Polish version of the English database. A bilingual, parallel corpus created in this way, named CORTEX (CORpus of Translated Emotional teXts), labeled with three sentiment polarity classes and nine emotion classes, was used for experiments on classification. We employed various classifiers: Naïve Bayes, Support Vector Machines, fastText, and BERT. The results obtained were satisfactory: we achieved the best scores for the BERT-based models, which yielded accuracy of over $90\%$ for sentiment (3-class) classification and almost $80\%$ for emotion (9-class) classification. We compared the results for both languages and discussed the differences. Both the accuracy and the F1-scores for Polish turned out to be slightly inferior to those for English, with the highest difference visible for BERT.