  Automatic fake news detection is a challenging problem in misinformation
spreading, and it has tremendous real-world political and social impacts. Past
studies have proposed machine learning-based methods for detecting such fake
news, focusing on different properties of the published news articles, such as
linguistic characteristics of the actual content, which however have
limitations due to the apparent language barriers. Departing from such efforts,
we propose FNDaaS, the first automatic, content-agnostic fake news detection
method, that considers new and unstudied features such as network and
structural characteristics per news website. This method can be enforced
as-a-Service, either at the ISP-side for easier scalability and maintenance, or
user-side for better end-user privacy. We demonstrate the efficacy of our
method using data crawled from existing lists of 637 fake and 1183 real news
websites, and by building and testing a proof of concept system that
materializes our proposal. Our analysis of data collected from these websites
shows that the vast majority of fake news domains are very young and appear to
have lower time periods of an IP associated with their domain than real news
ones. By conducting various experiments with machine learning classifiers, we
demonstrate that FNDaaS can achieve an AUC score of up to 0.967 on past sites,
and up to 77-92% accuracy on newly-flagged ones.
