HIGHLIGHTS
- who: XUELEI WANG et al. from the School of Computer Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia have published the paper: Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems, in the Journal: (JOURNAL)
- what: Based on various experiments, the authors provide recommended settings for the window size and step size in sliding window algorithms for anomaly detection within SASs. In the research , the datasets were collected from a simulation testbed. In each experiment, the main objective was to train all benign behaviours and stealthy attack behaviours only . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.