Detecting phishing websites through deep reinforcement learning

Moitrayee Chatterjee, Akbar Siami Namin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations


Phishing is the simplest form of cybercrime with the objective of baiting people into giving away delicate information such as individually recognizable data, banking and credit card details, or even credentials and passwords. This type of simple yet most effective cyber-attack is usually launched through emails, phone calls, or instant messages. The credential or private data stolen are then used to get access to critical records of the victims and can result in extensive fraud and monetary loss. Hence, sending malicious messages to victims is a stepping stone of the phishing procedure. A phisher usually setups a deceptive website, where the victims are conned into entering credentials and sensitive information. It is therefore important to detect these types of malicious websites before causing any harmful damages to victims. Inspired by the evolving nature of the phishing websites, this paper introduces a novel approach based on deep reinforcement learning to model and detect malicious URLs. The proposed model is capable of adapting to the dynamic behavior of the phishing websites and thus learn the features associated with phishing website detection.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
EditorsVladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781728126074
StatePublished - Jul 2019
Event43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States
Duration: Jul 15 2019Jul 19 2019

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157


Conference43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
Country/TerritoryUnited States


  • Index Terms—Phishing, Deep Reinforcement Learning


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