Understanding the current threat landscape as well as timely detection of imminent attacks are primary objectives of cyber security. Through time-series modeling of security data, such as event logs, alerts, or incidents, analysts take a step towards these goals. On the one hand, extrapolating time-series to predict future occurrences of attacks and vulnerabilities is able to support decision-making and preparation against threats. On the other hand, detection of model deviations as anomalies can point to suspicious outliers and thereby disclose cyber attacks. However, since the set of available techniques for time-series analysis is just as diverse as the research domains in the area of cyber security analytics, it can be difficult for analysts to understand which approaches fit the properties of security data at hand. This paper therefore conducts a broad literature review in research domains that leverage time-series analysis for cyber security analytics, with focus on available techniques, data sets, and challenges imposed by applications or feature properties. The results of our study indicate that relevant approaches range from detective systems ingesting short-term and low-level events to models that produce long-term forecasts of high-level attack cases.

Background: Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade. Objective: This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020. Methods: Search terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann–Whitney U tests. Results: Although the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, P<.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions. Conclusions: As internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups.

The autoregressive model is a tool used in time series analysis to describe and model time series data. Its main structure is a linear equation using the previous values to compute the next time step; i.e., the short time relationship is the core component of the autoregressive model. Therefore, short-term effects can be modeled in an easy way, but the global structure of the model is not obvious. However, this global structure is a crucial aid in the model selection process in data analysis. If the global properties are not reflected in the data, a corresponding model is not compatible. This helpful knowledge avoids unsuccessful modeling attempts. This article analyzes the global structure of the autoregressive model through the derivation of a closed form. In detail, the closed form of an autoregressive model consists of the basis functions of a fundamental system of an ordinary differential equation with constant coefficients; i.e., it consists of a combination of polynomial factors with sinusoidal, cosinusoidal, and exponential functions. This new insight supports the model selection process.

The problem of collision detection between objects is fundamental in many different communities including CAD, robotics, computer graphics, and computational geometry. This article presents a fast collision detection technique for all types of rigid bodies, demonstrated using polygon soups. We present two algorithms for computing a discrete spherical distance field of models. For compactly storing the distance field, we use a subsampling filter bank.

Building Information Modeling is an essential element for the digitization of the construction industry and forms the basis for the exchange of model-based data. In order to exploit the full potential of digitization, it is necessary for all stakeholders to operate digitally and avoid media discontinuities and format changes. A particular part in this process is given to the building authority, which has to examine and approve every application for a building permit. Our long-term goal is to automate the approval process in Austria; to reach this goal, we investigate which tasks have the highest potential for automation. We address this research question by interviewing the main stakeholders, by evaluating the tasks for the ability to automate them, and by clustering them in order to identify common subroutines and algorithms. As a result, we identify eight algorithmic tasks that can be automated and that are a part of every approval process, and we implemented these algorithms to prove the automation concept. This proof of concept is the basis for future automation steps in the processes of building authorities, whose implementation requires not only changes in the technical but also in the social, societal and political environments.