Detection of Phishing websites using Generative Adversarial Network
Title | Detection of Phishing websites using Generative Adversarial Network |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Robic-Butez, Pierrick, Win, Thu Yein |
Conference Name | 2019 IEEE International Conference on Big Data (Big Data) |
Publisher | IEEE |
ISBN Number | 978-1-7281-0858-2 |
Keywords | attack vector, Big Data analytics, Cloud Security, Computer crime, discriminator network, external metadata, feature extraction, Gallium nitride, generative adversarial network, generative adversarial networks, generator network, Generators, hacking endeavour, Human Behavior, human factors, internal structure, low-risk rightreward nature, meta data, neural nets, normal Websites, pattern classification, phishing, phishing datasets, Phishing Detection, phishing Websites, pubcrawl, security analytics, synthetic phishing features, Training, Uniform resource locators, Web sites |
Abstract | Phishing is typically deployed as an attack vector in the initial stages of a hacking endeavour. Due to it low-risk rightreward nature it has seen a widespread adoption, and detecting it has become a challenge in recent times. This paper proposes a novel means of detecting phishing websites using a Generative Adversarial Network. Taking into account the internal structure and external metadata of a website, the proposed approach uses a generator network which generates both legitimate as well as synthetic phishing features to train a discriminator network. The latter then determines if the features are either normal or phishing websites, before improving its detection accuracy based on the classification error. The proposed approach is evaluated using two different phishing datasets and is found to achieve a detection accuracy of up to 94%. |
URL | https://ieeexplore.ieee.org/document/9006352 |
DOI | 10.1109/BigData47090.2019.9006352 |
Citation Key | robic-butez_detection_2019 |
- internal structure
- Web sites
- Uniform resource locators
- Training
- synthetic phishing features
- security analytics
- pubcrawl
- phishing Websites
- Phishing Detection
- phishing datasets
- Phishing
- pattern classification
- normal Websites
- neural nets
- meta data
- low-risk rightreward nature
- attack vector
- Human Factors
- Human behavior
- hacking endeavour
- Generators
- generator network
- generative adversarial networks
- generative adversarial network
- Gallium nitride
- feature extraction
- external metadata
- discriminator network
- Computer crime
- Cloud Security
- Big Data Analytics