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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages166-171
Number of pages6
ISBN (Electronic)9781538642030
DOIs
Publication statusPublished - 2018 May 9
Event2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 - Taipei, Taiwan
Duration: 2017 Dec 12017 Dec 3

Publication series

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

Other

Other2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
CountryTaiwan
CityTaipei
Period17-12-0117-12-03

Fingerprint

Marketing
Smartphones
Application programs
Learning systems
Neural networks
Deep learning
Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction

Cite this

Huang, T. H. D., Yu, C. M., & Kao, H-Y. (2018). Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection. In Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 (pp. 166-171). (Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAAI.2017.30
Huang, Tonton Hsien De ; Yu, Chia Mu ; Kao, Hung-Yu. / Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection. Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 166-171 (Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017).
@inproceedings{d67e121989734d38b9e6115f5b6a7dc9,
title = "Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection",
abstract = "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.",
author = "Huang, {Tonton Hsien De} and Yu, {Chia Mu} and Hung-Yu Kao",
year = "2018",
month = "5",
day = "9",
doi = "10.1109/TAAI.2017.30",
language = "English",
series = "Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "166--171",
booktitle = "Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017",
address = "United States",

}

Huang, THD, Yu, CM & Kao, H-Y 2018, Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection. in Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017. Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017, Institute of Electrical and Electronics Engineers Inc., pp. 166-171, 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017, Taipei, Taiwan, 17-12-01. https://doi.org/10.1109/TAAI.2017.30

Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection. / Huang, Tonton Hsien De; Yu, Chia Mu; Kao, Hung-Yu.

Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 166-171 (Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection

AU - Huang, Tonton Hsien De

AU - Yu, Chia Mu

AU - Kao, Hung-Yu

PY - 2018/5/9

Y1 - 2018/5/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85048359555&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048359555&partnerID=8YFLogxK

U2 - 10.1109/TAAI.2017.30

DO - 10.1109/TAAI.2017.30

M3 - Conference contribution

T3 - Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017

SP - 166

EP - 171

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Huang THD, Yu CM, Kao H-Y. Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection. In Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 166-171. (Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017). https://doi.org/10.1109/TAAI.2017.30