Building accurate HAV exploiting User Profiling and Sentiment Analysis
This addresses the need for individuals and organizations to screen their exposure to SE attacks, though it appears incremental as it builds on existing text mining techniques.
The paper tackles the problem of Social Engineering (SE) attacks by developing an automatic tool that extracts, categorizes, and summarizes target interests from social network data to identify weaknesses, aiming to reduce risks and save money for citizens, institutions, and private bodies.
Social Engineering (SE) is one of the most dangerous aspect an attacker can use against a given entity (private citizen, industry, government, ...). In order to perform SE attacks, it is necessary to collect as much information as possible about the target (or victim(s)). The aim of this paper is to report the details of an activity which took to the development of an automatic tool that extracts, categorizes and summarizes the target interests, thus possible weaknesses with respect to specific topics. Data is collected from the user's activity on social networks, parsed and analyzed using text mining techniques. The main contribution of the proposed tool consists in delivering some reports that allow the citizen, institutions as well as private bodies the screening of their exposure to SE attacks, with a strong awareness potential that will be reflected in a decrease of the risks and a good opportunity to save money.