A semantic model bridging DISARM framework and Situation Awareness for disinformation Attacks Attribution
Danilo Cavaliere, Giuseppe Fenza, Domenico Furno, Vincenzo Loia · 2024
Online disinformation is an ever-increasing phenomenon that badly affects society stability by using myriads of forms, techniques and channels. Up to now, there is a lack of standard models to analyze the strategies behind disinformation attacks that increase the proliferation of fake or misleading contents over the Internet. Fake or misleading contents are mostly generated by AI-based techniques to be then spread by the minute worldwide. Due to the myriads of different attack strategies and spread speed, analyzing the attack attribution is not an easy task. In this regard, this article presents a knowledge-based approach that exploits the DISARM framework inheriting Cybersecurity principles alongside Situation Awareness concepts to provide analysts with a robust and exhaustive model to analyze and fight back disinformation attacks through the extraction of threat actors’ behaviors by reasoning on their attack patterns. Case scenarios on real disinformation incidents demonstrate the practical utility and usability of the proposed model to extract rules for detection and analysis of specific kinds of attackers.