Development of a Statistical Threat Level Detection Indicator Assessment for Deception Tactics

Morgan Taylor, Western Kentucky University

Document Type Thesis

Abstract

This thesis is concerned with the act of combatting terrorists by using defensive deception methods generated from Social Media data analytics, by implementing a Statistical Threat Level Detection Indicator. This research focuses on the use of cyber deception by terrorists, and the analysis of the threat level of the terroristic motivation by utilizing a threshold meme. Social Media is an active and efficient database of information. We have previously tracked the spread of social events such as the spread of Chikungunya, the activity of a school related shooting or an active crowd surge similar to the Hong-Kong political unrest via data mining of active Twitter accounts. We are now able to use word memes to search the Twitter database for indicators of possible life altering events such as the spread of a virus, potential terroristic activity, or a potential mass shooting. Memes of interest relate activity to geospatial growth. The meme spreads through the vast number of social users similar to a wave. The memes are considered Twitter variables and are used in conjunction with the Statistical Threat Level Detection Indicator to identify a list of users of interest.