Biblio

Smart metering systems have been gaining popularity as a vital part of the general smart grid paradigm. Naturally, as new technologies arise to cover this emerging field, so do security and privacy related issues regarding the energy consumer's personal data. These challenges impose the need for the development of new methods through a better understanding of the state-of-the-art. This paper aims at identifying the main categories of security and privacy techniques utilized in smart metering systems from a three-point perspective: i) a field research survey, ii) EU initiatives and findings towards the same direction and iii) a data-driven analysis of the state-of-the-art and the identification of its main topics (or themes) using topic modeling techniques. Detailed quantitative results of this analysis, such as semantic interpretation of the identified topics and a graph representation of the topic trends over time, are presented.

Capturing the patterns in adversarial movement can present crucial insight into team dynamics and organization of cybercrimes. This information can be used for additional assessment and comparison of decision making approaches during cyberattacks. In this study, we propose a data-driven analysis based on time series analysis and social networks to identify patterns and alterations in time allocated to intrusion stages and adversarial movements. The results of this analysis on two case studies of collegiate cybersecurity exercises is provided as well as an analytical comparison of their behavioral trends and characteristics. This paper presents preliminary insight into complexities of individual and group level adversarial movement and decision-making as cyberattacks unfold.