Protection policy in networked locations using machine learning and data science approach

Abstract

A protection policy employs diverse techniques and mechanisms to sense protection related glitches and coercions in a networked location. The protection policy is big-data focused and services machine learning technique to perform protection analytics using data science. The protection policy performs entity behavioral analytics to sense the protection related glitches and coercions, regardless of whether such glitches/coercions were previously known. The protection policy can include both real-time and group modes for sensing glitches and coercions. By visually presenting diagnostic results scored with jeopardy assessments and auxiliary indication, the protection policy enables network protection administrators to respond to a sensed glitch or coercions, and to take action promptly.