Web phishing remains a serious cyber threat responsible for most data
breaches. Machine Learning (ML)-based anti-phishing detectors are seen as an
effective countermeasure, and are increasingly adopted by web-browsers and
software products. However, with an average of 10K phishing links reported per
hour to platforms such as PhishTank and VirusTotal (VT), the deficiencies of
such ML-based solutions are laid bare. We first explore how phishing sites
bypass ML-based detection with a deep dive into 13K phishing pages targeting
major brands such as Facebook. Results show successful evasion is caused by:
(1) use of benign services to obscure phishing URLs; (2) high similarity
between the HTML structures of phishing and benign pages; (3) hiding the
ultimate phishing content within Javascript and running such scripts only on
the client; (4) looking beyond typical credentials and credit cards for new
content such as IDs and documents; (5) hiding phishing content until after
human interaction. We attribute the root cause to the dependency of ML-based
models on the vertical feature space (webpage content). These solutions rely
only on what phishers present within the page itself. Thus, we propose
Anti-SubtlePhish, a more resilient model based on logistic regression. The key
augmentation is the inclusion of a horizontal feature space, which examines
correlation variables between the final render of suspicious pages against what
trusted services have recorded (e.g., PageRank). To defeat (1) and (2), we
correlate information between WHOIS, PageRank, and page analytics. To combat
(3), (4) and (5), we correlate features after rendering the page. Experiments
with 100K phishing/benign sites show promising accuracy (98.8%). We also
obtained 100% accuracy against 0-day phishing pages that were manually crafted,
comparing well to the 0% recorded by VT vendors over the first four days.