While artificial intelligence (AI) has undeniably ushered numerous solutions across various fields, the growing belief that AI can solve all problems overshadows their lack of transparency that comes along. Understanding how decisions are made and what has led to the output is crucial in critical systems to ensure accountability and trust.

This research proposes a complementary method leveraging inter-host distances that localise the outlying hosts, logs and the time frames, which require more advanced analysis. By relying on a variant of a prominent statistical method in the field of authorship attribution - Burrows Delta - the approach enhances transparency in identifying deviating hosts, logs and time frames. Hence, the proposed solution offers an understandable complementary method that preserves integrability by being a log-based method while enabling understandable pinpointing of the specific hosts, logs and time frames that warrant further advanced analysis. By providing insights into the behaviour of the hosts over time, a temporal summarisation for security analysts is provided, relaxing their need to go through all the log files to understand the hosts' behaviour.

The results show that a complementary method based on the textual content of the metadata of the logs provides alternative insight into the activities of the hosts than the attributes. Moreover, the behaviour defined by the proposed method requires less extensive lookup than the behaviour defined by attributes. The inter-host distances based on the textual content allow understandable localisation of the host behaviour over time. Hence, this research provides an understandable method that will summarise the behaviour of the hosts over time, which enables the localisation of the logs requiring more advanced, in-depth analysis, and thereby reducing the amount of logs security analists need to consider during a compromise.

Seeing the virtual coach as a friend is beneficial as it increases the motivation, confidence, and perseverance of the accompanying. Here, an investigation has taken place to what characteristics the virtual coach must possess to establish this friendly relationship. Thus, the main research question is: What are the reasons for seeing the virtual coach as a stranger or friend? This research made use of pre-gathered data. Here, five hundred participants interacted with the text-based virtual coach Sam in five separate sessions. Afterwards, each participant rated the relationship with the virtual coach, followed by an explanatory free-text response to which thematic analysis was applied. This resulted in five main themes: Relation, Positive Characteristics, Perception, Impersonal, and Chat Opinions. These were used to determine the characteristics the virtual coach must have to be considered a friend. Furthermore, these themes were used to obtain correlations. With these, it has been concluded that the user is more likely to develop a closer relationship with the virtual coach when: perceiving it as a human, pleasantly conversing, and being positive about its character. Whereas the user's age has a negligible influence on the relationship.