Large-Scale Automatic Classification of Phishing Pages

By Colin Whittaker, Brian Ryner, Marria Nazif. 2010

455 citations recorded by the source corpus.

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Citations in the corpus

DeltaPhish: Detecting Phishing Webpages in Compromised Websites (2017, background): “…URL and its domain name [16, 14, 17, 18], from its HTML content and structure, and from other sources, including search engines, HTTP cookies, website certiﬁ-cates [19, 20, 10, 21, 22, 23, 24, 25], and even publicly-available blacklisting services like Google Safe Browsing and PhishTank [26].”
PhishEye: Live Monitoring of Sandboxed Phishing Kits (2016, background): “Our study presents accurate measurements of the duration and effectiveness of this popular threat, and discusses many new and interesting aspects we observed by monitoring hundreds of phishing campaigns.”
Deep Text Classification Can be Fooled (2017, background): “uage processing. Text processing also plays an important role in modern information analysis. For instance, many spam or phishing webpage detection systems are primarily based on text classification (Whittaker et al. 2010). Google has begun to detect web spams by using a deep network1. The content o”
PhishZoo: Detecting Phishing Websites by Looking at Them (2011, background): “Google’s anti-phishing filter detects phishing and malware by examining page URL, page rank, WHOIS information and contents of a page including HTML, javascript, images, iframe, etc. [18].”
Cracking Classifiers for Evasion: A Case Study on the Google's Phishing Pages Filter (2016, methodology): “Various classifiers were trained for detecting malicious web pages [25], spam [54], phishing [58], malware [47], etc.”
Malicious URL Detection using Machine Learning: A Survey (2017, background): “Here we discuss two categories of representation learning: feature selection, where the features are evaluated and selected based on their performance, and sparsity regularization, where the feature selection is implicitly done by incorporating it into the objective function.”
Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare (2015, background): “These applications include healthcare [1], network intrusion detection systems[2], spam and fraud detection, phishing detection [3], political decision making [4], adversarial advertisement detection [5], and financial engineering [6].”
High-performance content-based phishing attack detection (2011, background): “Previous researchers [14] [29] [30] have shown the ability of these methodologies to reduce the false positive rate while not affecting the detection rate.”
Emerging phishing trends and effectiveness of the anti-phishing landing page (2014, background): “showed that URLs where the number of host components is greater than 3 are generally phishing pages [38].”
Handcrafted Fraud and Extortion: Manual Account Hijacking in the Wild (2014, methodology): “Next we use a manually reviewed random sample of 100 phishing pages detected by the SafeBrowsing anti-phishing pipeline [26] The manual review was used to identify what type of account are targeted by phishers (e.g Bank, Social Network).”
Social Phishing (2018, methodology): “[21] present a logistic regression classifier based on features that describe the composition of the web page’s URL, the hosting of the page and the page’s HMTL content as collected by a crawler.”
Click Trajectories: End-to-End Analysis of the Spam Value Chain (2011, background): “Similarly, the network characteristics of spam senders have seen extensive study for characterizing botnet membership [58], identifying preﬁx hijacking [45], classifying domains and URLs [14], [32], [44], [55], [56], and evaluating black-lists [47], [48].”
Machine learning for email spam filtering: review, approaches and open research problems (2019, methodology): “RF technique is gaining more popularity these days and it has ﬁ nd application in different ﬁ elds and in literature it has been used to provide solution to analogous problem, as found in [41,92,93].”
Learning to Detect Phishing Webpages (2014, background): “Keywords: phishing attack, phishing webpages, content-based approach, batch learning, online learning”
Phishing Detection: A Literature Survey (2013, methodology): “For this reason, a number of other solutions (such as [4]) use a collaborative approach where a”