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Global networking, growing computer infrastructure complexity and the ongoing migration of many private and business aspects to the electronic domain commonly mandate using cutting-edge technologies based on data analysis, machine learning, and artificial intelligence to ensure high levels of network and information system security. Transparency is a major barrier to the deployment of black box intelligent systems in high-risk domains, such as the cybersecurity domain, with the problem getting worse as machine learning models increase in complexity. In this research, explainable machine learning is used to extract information from the CIC-IDS2017 dataset and to critically contrast the knowledge attained by analyzing if–then decision tree rules with the knowledge attained by the SHAP approach. The paper compares the challenges of the knowledge extraction using the SHAP method and the if–then decision tree rules, providing guidelines regarding different approaches suited to specific situations.

The prediction of sport event results has always drawn attention from a vast variety of different groups of people, such as club managers, coaches, betting companies, and the general population. The specific nature of each sport has an important role in the adaption of various predictive techniques founded on different mathematical and statistical models. In this paper, a common approach of modeling sports with a strongly defined structure and a rigid scoring system that relies on an assumption of independent and identical point distributions is challenged. It is demonstrated that such models can be improved by introducing dynamics into the match models in the form of sport momentums. Formal mathematical models for implementing these momentums based on conditional probability and empirical Bayes estimation are proposed, which are ultimately combined through a unifying hybrid approach based on the Monte Carlo simulation. Finally, the method is applied to real-life volleyball data demonstrating noticeable improvements over the previous approaches when it comes to predicting match outcomes. The method can be implemented into an expert system to obtain insight into the performance of players at different stages of the match or to study field scenarios that may arise under different circumstances.

Tennis, as one of the most popular individual sports in the world, holds an important role in the betting world. There are two main categories of bets: pre-match betting, which is conducted before the match starts, and live betting, which allows placing bets during the sporting event. Betting systems rely on setting sports odds, something historically done by domain experts. Setting odds for live betting represents a challenge due to the need to follow events in real-time and react accordingly. In tennis, hierarchical models often stand out as a popular choice when trying to predict the outcome of the match. These models commonly leverage a recursive approach that aims to predict the winner or the final score starting at any point in the match. However, recursive expressions inherently contain computational complexity which hinders the efficiency of methods relying on them. This paper proposes a more resource-effective alternative in the form of a combinatorial approach based on a binomial distribution. The resulting accuracy of the combinatorial approach is identical to that of the recursive approach while being vastly more efficient when considering the execution time, making it a superior choice for live betting in this domain.

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