Marchal, Samuel[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
François, Jérôme[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Engel, Thomas[University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
10th International Conference on Network and Service Management
from 17-11-2014 to 21-11-2014
Rio de Janeiro
Brazil
[en] Phishing detection ; URL rating ; Word relatedness ; Search engine query data
[en] Despite the growth of prevention techniques, phishing remains an important threat since the principal countermeasures in use are still based on reactive URL blacklisting. This technique is inefficient due to the short lifetime of phishing Web sites, making recent approaches relying on real-time or proactive phishing URLs detection techniques more appropriate. In this paper we introduce PhishScore, an automated real-time phishing detection system. We observed that phishing URLs usually have few relationships between the part of the URL that must be registered (upper level domain) and the remaining part of the URL (low level domain, path, query). Hence, we define this concept as intra-URL relatedness and evaluate it using features extracted from words that compose a URL based on query data from Google and Yahoo search engines. These features are then used in machine learning based classification to detect phishing URLs from a real dataset.
Interdisciplinary Centre for Security, Reliability and Trust (SnT)
Fonds National de la Recherche - FnR
I2R-NET-COM-110000 > Groupe de Recherche Netlab > > ENGEL Thomas
[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
