This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

Recently, various artificial intelligence (AI)-based methods have been proposed to support humans in detecting disinformation and fake news. The goal of this article is to provide a meta-analysis, and formally evaluate, compare, and benchmark various classes of fake news detection approaches. To this end, the following paper performs a comprehensive analysis of the performance-related results of different models using a range of benchmark datasets. The performed and disclosed meta-analysis compares the statistical significance of differences in a range of performance metrics, including precision, $F1$ -score, recall, and balanced accuracy (BACC). The utilized approach features the $5$ $\times$ $2$ cross-validation methodology. The models undergoing the formal evaluation constitute state-of-the-art (SOTA) solutions meeting acceptance criteria. The evaluated approaches draw from the most recent advancements in natural language processing (NLP). The outcome of this work is the formal benchmarking and meta-analysis of fake news detection methods that can be further utilized by the research community, but more importantly by the practitioners and decision-makers that counter fake news on a daily basis, e.g., in press agencies, homeland security agencies, fact-checkers, and so on. This work is the natural extension of the authors’ previous systematic analysis of fake news detection methods and authors’ own fake news detection methods based on machine learning (ML)/artificial intelligence (AI) techniques.

At present, many researchers see hope that artificial intelligence, machine learning in particular, will improve several aspects of the everyday life for individuals, cities and whole nations alike. For example, it has been speculated that the so-called machine learning could soon relieve employees of part of the duties, which may improve processes or help to find the most effective ways of performing tasks. Consequently, in the long run, it would help to enhance employees’ work-life balance. Thus, workers’ overall quality of life would improve, too. However, what would happen if machine learning as such were employed to try and find the ways of achieving work-life balance? This is why the authors of the paper decided to utilize a machine learning tool to search for the factors that influence the subjective feeling of one’s work-life balance. The possible results could help to predict and prevent the occurrence of work-life imbalance in the future. In order to do so, the data provided by an exceptionally sizeable group of 800 employees was utilised; it was one of the largest sample groups in similar studies in Poland so far. Additionally, this was one of the first studies where so many employees had been analysed using an artificial neural network. In order to enable replicability of the study, the specific setup of the study and the description of the dataset are provided. Having analysed the data and having conducted several experiments, the correlations between some factors and work-life balance have indeed been identified: it has been found that the most significant was the relation between the feeling of balance and the actual working hours; shifting it resulted in the tool predicting the switch from balance to imbalance, and vice versa. Other factors that proved significant for the predicted WLB are the amount of free time a week the employee has for themselves, working at weekends only, being self-employed and the subjective assessment of one’s financial status. In the study the dataset gets balanced, the most important features are selected with the selectKbest algorithm, an artificial neural network of 2 hidden layers with 50 and 25 neurons, ReLU and ADAM is constructed and trained on 90% of the dataset. In tests, it predicts WLB based on the prepared dataset and selected features with 81% accuracy.