Finding Phish in a Haystack: A Pipeline for Phishing Classification on Certificate Transparency Logs

Abstract

Current popular phishing prevention techniques mainly utilize reactive blocklists, which leave a "window of opportunity" for attackers during which victims are unprotected. One possible approach to shorten this window aims to detect phishing attacks earlier, during website preparation, by monitoring Certificate Transparency (CT) logs. Previous attempts to work with CT log data for phishing classification exist, however they lack evaluations on actual CT log data. In this paper, we present a pipeline that facilitates such evaluations by addressing a number of problems when working with CT log data. The pipeline includes dataset creation, training, and past or live classification of CT logs. Its modular structure makes it possible to easily exchange classifiers or verification sources to support ground truth labeling efforts and classifier comparisons. We test the pipeline on a number of new and existing classifiers, and find a general potential to improve classifiers for this scenario in the future. We publish the source code of the pipeline and the used datasets along with this paper [12], thus making future research in this direction more accessible. CCS CONCEPTS• Security and privacy → Intrusion detection systems; • Computing methodologies → Machine learning.

Open Source Intelligence Opportunities and Challenges: a Review

“…The study aims to advance the HS-Decoy and LPC-MTD-based combined model into an international standard-based complex architecture characterized as game theory. Drichel et al in [79] focused on phishing prevention techniques. They proposed a new pipeline that addresses this issue by monitoring Certificate Transparency logs during website preparation.…”

“…Penetration testing automation [66] Characterization and profiling the identified threats [67] Detecting cybersecurity-related Twitter accounts and different sub-groups [70] Compile data regarding the Pulwama attacks [74] Phishing prevention [79] Social Media group solutions. The first group was concerned with detecting and solving cyberspace problems to increase Internet users' security.…”