Reference : PhishStorm: Detecting Phishing With Streaming Analytics
Scientific journals : Article
Engineering, computing & technology : Computer science
PhishStorm: Detecting Phishing With Streaming Analytics
Marchal, Samuel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
François, Jérôme mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Engel, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
IEEE Transactions on Network and Service Management
IEEE Communications Society
Yes (verified by ORBilu)
New York
[en] Big Data ; Phishing Detection ; Machine Learning ; Mining and Statistical Methods ; Search Engine Query Data ; URL rating ; Word Relatedness
[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 URL detection techniques more appropriate. In this paper, we introduce PhishStorm, an automated phishing detection system that can analyze in real time any URL in order to identify potential phishing sites. PhishStorm can interface with any email server or HTTP proxy. We argue that phishing URLs usually have few relationships between the part of the URL that must be registered (low-level domain) and the remaining part of the URL (upper-level domain, path, query). We show in this paper that experimental evidence supports this observation and can be used to detect phishing sites. For this purpose, we define the new concept of 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. Our technique is assessed on 96 018 phishing and legitimate URLs that result in a correct classification rate of 94.91% with only 1.44% false positives. An extension for a URL phishingness rating system exhibiting high confidence rate ( $>$ 99%) is proposed. We discuss in this paper efficient implementation patterns that allow real-time analytics using Big Data architectures such as STORM and advanced data structures based on the Bloom filter.
Interdisciplinary Centre for Security, Reliability and Trust
Fonds National de la Recherche - FnR
Researchers ; Professionals

File(s) associated to this reference

Fulltext file(s):

Open access
phishStorm-revised.pdfAuthor preprint2.24 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.