Efficient intrusion detection and analysis of the security landscape in big data environments present challenge for today's users. Intrusion behavior can be described by provenance graphs that record the dependency relationships between intrusion processes and the infected files. Existing intrusion detection methods typically analyze and identify the anomaly either in a single provenance path or the whole provenance graph, neither of which can achieve the benefit on both detection accuracy and detection time.

Static analysis is increasingly used by companies and individual code developers to detect bugs and security vulnerabilities. As programs grow more complex, the analyses have to support new code concepts, frameworks and libraries. However, static-analysis code itself is also prone to bugs. While more complex analyses are written and used in production systems every day, the cost of debugging and fixing them also increases tremendously.

The file contains raw data collected from 9 pedestrians. Three of them walked in Track 1, another three walked in Track 2 and the last three walked in Track 3. All the pedestrians ended their walks at the starting point. Track 1 and Track 3 cover a distance of 150.3m. While, the Track covers a distance of 111.4m.

This dataset contains the numerical results and Excel graphing used to create the results images in the main paper.