Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection

Tonton Hsien De Huang, Chia Mu Yu, Hung Yu Kao

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

The advance of smartphones and cellular networks boosts the need of mobile advertising and targeted marketing. However, it also triggers the unseen security threats. We found that the phone scams with fake calling numbers of very short lifetime are increasingly popular and have been used to trick the users. The harm is worldwide. On the other hand, deceptive advertising (deceptive ads), the fake ads that tricks users to install unnecessary apps via either alluring or daunting texts and pictures, is an emerging threat that seriously harms the reputation of the advertiser. To counter against these two new threats, the conventional blacklist (or whitelist) approach and the machine learning approach with predefined features have been proven useless. Nevertheless, due to the success of deep learning in developing the highly intelligent program, our system can efficiently and effectively detect phone scams and deceptive ads by taking advantage of our unified framework on deep neural network (DNN) and convolutional neural network (CNN). The proposed system has been deployed for operational use and the experimental results proved the effectiveness of our proposed system. Furthermore, we keep our research results and release experiment material on http://deceptiveads.twman.org and http://phonescams.twman.org if there is any update.

原文English
主出版物標題Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
發行者Institute of Electrical and Electronics Engineers Inc.
頁面166-171
頁數6
ISBN(電子)9781538642030
DOIs
出版狀態Published - 2018 五月 9
事件2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 - Taipei, Taiwan
持續時間: 2017 十二月 12017 十二月 3

出版系列

名字Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017

Other

Other2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
國家/地區Taiwan
城市Taipei
期間17-12-0117-12-03

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信
  • 電腦科學應用
  • 人機介面

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