2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLSP-28.2
Paper Title MULTIPHISH: MULTI-MODAL FEATURES FUSION NETWORKS FOR PHISHING DETECTION
Authors Lei Zhang, Peng Zhang, Luchen Liu, Jianlong Tan, Institute of Information Engineering, Chinese Academy of Sciences, China
SessionMLSP-28: ML and Time Series
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Phishing is an increasingly serious cybercrime. Phishers create phishing websites by mimicking legitimate websites to confuse users and steal their personal information. The proliferation of phishing websites and more advanced camouflage techniques are problems faced by most existing methods. In this paper, we propose a features fusion networks (MultiPhish) which is the first study on fusing multi-modal features with neural networks for the phishing detection task. In this end-to-end network, the domain and favicon of the website are represented via deep neural networks, and the representation of the website identity is obtained through multi-modal features fusion. In addition, the variation autoencoder (VAE) is introduced to optimize the representation. In the phishing detection module, we incorporate URL features to improve situations where phishing websites cannot be detected only by estimating whether the website identity is disguised. Based on the latest collected dataset, we have carried out extensive experiments and proved that our model is superior to the relevant methods. In addition, MultiPhish is a completely language-independent strategy, so it can perform phishing detection regardless of the text language.