Given that disturbances to the stable and normal operation of power systems have grown phenomenally, particularly in terms of unauthorized access to confidential and critical data, injection of malicious software, and exploitation of security vulnerabilities in a poorly patched software among others; then developing, as a countermeasure, an assessment solutions with machine learning capabilities to match up in real-time, with the growth and fast pace of these cyber-attacks, is not only critical to the security, reliability and safe operation of power system, but also germane to guaranteeing advanced monitoring and efficient threat detection. Using the Mississippi State University and Oak Ridge National Laboratory dataset, the study used an XGB Classifier modeling approach in machine learning to diagnose and assess power system disturbances, in terms of Attack Events, Natural Events and No-Events. As test results show, the model, in all the three sub-datasets, generally demonstrates good performance on all metrics, as it relates to accurately identifying and classifying all the three power system events.

Recently we read a lot about fake news, alternate facts and journalism lies. Companies like Facebook develop data science algorithms to detect these postings, based among other things on crowd sourcing (collective intelligence.) But can the data scientist, with her inquisitive mind and strong sense of numbers and probabilities, use her brain to assess how true a piece of information is? I am talking here about fuzzy logic, and human rather than artificial intelligence to determine the probabilities. Here is a recent, popular example: the Firefall in the Yosemite National Park (California), pictured below.