Fake News and Phishing Detection Using a Machine Learning Trained Expert System

TL;DR

This paper introduces a machine learning trained expert system (MLES) for detecting phishing websites and fake news, aiming to provide explainable decisions similar to domain experts.

Contribution

It presents a novel application of MLES for both phishing and fake news detection, with different network designs tailored to each task.

Findings

MLES effectively detects phishing sites using URL and site properties.

Fake news detection based on emotional and political factors shows promising results.

Different network structures impact detection performance.

Abstract

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.