Phishing Detection: A Literature Survey

By Mahmoud Khonji, Youssef Iraqi, Andrew Jones. 2013

594 citations recorded by the source corpus.

This article surveys the literature on the detection of phishing attacks. Phishing attacks target vulnerabilities that exist in systems due to the human factor. Many cyber attacks are spread via mechanisms that exploit weaknesses found in end-users, which makes users the weakest element in the security chain. The phishing problem is broad and no single silver-bullet solution exists to mitigate all the vulnerabilities effectively, thus multiple techniques are often implemented to mitigate specific attacks. This paper aims at surveying many of the recently proposed phishing mitigation techniques. A high-level overview of various categories of phishing mitigation techniques is also presented, such as: detection, offensive defense, correction, and prevention, which we belief is critical to present where the phishing detection techniques fit in the overall mitigation process.

Subfields: Information Systems, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Battista Biggio
2. Fabio Roli
3. Igino Corona
4. Bin Liang
5. Miaoqiang Su
6. Wenchang Shi
7. Wei You
8. Gang Yang

In Signal Processing

1. Battista Biggio
2. Fabio Roli
3. Igino Corona
4. Giorgio Fumera
5. Bin Liang
6. Miaoqiang Su
7. Wenchang Shi
8. Wei You

Citations in the corpus

• DeltaPhish: Detecting Phishing Webpages in Compromised Websites (2017, background): “They may be even used to achieve political or military objectives [2, 3].”
• Phishing Attacks: A Recent Comprehensive Study and a New Anatomy (2021, background): “In the study presented by Khonji et al. (2013), security experts contradict the effectiveness and usability of user education.”
• Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding (2019, background): “focus on how to detect the emails or websites containing phishing fraud information [8].”
• Malicious URL Detection using Machine Learning: A Survey (2017, background): “[3], [15] gave a broad overview of Phishing and its problems, but do not extensively survey the feature representation or the learning algorithms aspect.”
• A survey of the applications of text mining in financial domain (2016, background): “Khonji and Iraqi (2013) presented a Literature Survey on Phishing Detection that included four methods viz., visual similarity, machine learning based, blacklists, and rule-based.”
• Cracking Classifiers for Evasion: A Case Study on the Google's Phishing Pages Filter (2016, methodology): “To minimize the impact of phishing attacks, a variety of methods have been proposed to detect phishing pages, involving machine learning [37][53][58] or other techniques [26][27][29][31][33][46][60][61].”