Details

Autor(en) / Beteiligte

• Le Page, Sophie
• Jourdan, Guy-Vincent
• Bochmann, Gregor V.
• Onut, Iosif-Viorel
• Flood, Jason
• Ko, In-Young
• Frasincar, Flavius
• Bakaev, Maxim

Titel

Domain Classifier: Compromised Machines Versus Malicious Registrations

Ist Teil von

• Web Engineering, p.265-279

Ort / Verlag

Cham: Springer International Publishing

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• In “phishing attacks”, phishing websites disguised as trustworthy websites attempt to steal sensitive information. Remediation and mitigation options differ depending on whether the phishing website is hosted on a legitimate but compromised domain, in which case the domain owner is also a victim, or whether the domain itself is maliciously registered. We accordingly attempt to tackle here the important question of classifying known phishing sites as either compromised or maliciously registered. Following the recent adoption of GDPR standards now putting off-limits any personal data, few relevant literature criteria still satisfy those standards. We propose here a machine-learning based domain classifier, introducing nine novel features which exploit the internet presence and history of a domain, using only publicly available information. Evaluation of our domain classifier was performed with a corpus of phishing websites hosted on over 1,000 compromised domains and 10,000 malicious domains. In the randomized evaluation, our domain classifier achieved over 92% accuracy with under 8% false positive rate, with compromised cases as the positive class. We have also collected over 180,000 phishing website instances over the past 3 years. Using our classifier we show that 73% of the websites hosting attacks are compromised while the remaining 27% belong to the attackers.