Victim or Attacker? A Multidataset Domain Classification of Phishing Attacks In xe2x80x9cphishing attacksxe2x80x9d, phishing websites disguised as trustworthy websites attempt to steal sensitive information from end users. Remediation 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 by an attacker. We propose here a novel machinelearning domain classifier, introducing features based on the internet presence and history of a domain, using only publicly available information. Using a phish feed and malicious domain feed from the AntiPhishing Working Group (APWG), evaluation of our domain classifier achieves 94% accuracy on future malicious domains, while maintaining 88% and 92% accuracy on malicious and compromised datasets respectively from two other sources. To increase our training set we introduce a semisupervised technique to label part of APWGu0027s phish feed. For the rest of the feed we use our classifier and show that 62% of the websites hosting attacks are compromised while the remaining 38% belong to the attackers. The result of this research is a tool which gives some important insights on phisheru0027s current modus operandi. It also provides a quick mechanism, entirely based on freely available data, to assess crucial information about the server and better respond to an attack by having it taken down as quickly as possible.

Catching the Phish: Detecting Phishing Attacks Using Recurrent Neural Networks (RNNs). The emergence of online services in our daily lives has been accompanied by a range of malicious attempts to trick individuals into performing undesired actions, often to the benefit of the adversary. The most popular medium of these attempts is phishing attacks, particularly through emails and websites. In order to defend against such attacks, there is an urgent need for automated mechanisms to identify this malevolent content before it reaches users. Machine learning techniques have gradually become the standard for such classification problems. However, identifying common measurable features of phishing content (e.g., in emails) is notoriously difficult. To address this problem, we engage in a novel study into a phishing content classifier based on a recurrent neural network (RNN), which identifies such features without human input. At this stage, we scope our research to emails, but our approach can be extended to apply to websites. Our results show that the proposed system outperforms stateoftheart tools. Furthermore, our classifier is efficient and takes into account only the text and, in particular, the textual structure of the email. Since these features are rarely considered in email classification, we argue that our classifier can complement existing classifiers with high information gain.