Fake News and Phishing Detection Using a Machine Learning Trained Expert System
This work addresses cybersecurity and misinformation problems for users and platforms, but it is incremental as it applies existing MLES methods to these domains.
The paper tackles fake news and phishing detection by developing machine learning trained expert systems (MLES) for each, resulting in explainable decisions based on rule-fact networks that analyze site properties for phishing and story factors like emotion for fake news.
Expert systems have been used to enable computers to make recommendations and decisions. This paper presents the use of a machine learning trained expert system (MLES) for phishing site detection and fake news detection. Both topics share a similar goal: to design a rule-fact network that allows a computer to make explainable decisions like domain experts in each respective area. The phishing website detection study uses a MLES to detect potential phishing websites by analyzing site properties (like URL length and expiration time). The fake news detection study uses a MLES rule-fact network to gauge news story truthfulness based on factors such as emotion, the speaker's political affiliation status, and job. The two studies use different MLES network implementations, which are presented and compared herein. The fake news study utilized a more linear design while the phishing project utilized a more complex connection structure. Both networks' inputs are based on commonly available data sets.