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

TL;DR

This paper presents a deep learning-based unified system for detecting deceptive advertising and phone scams, addressing limitations of traditional methods and demonstrating effective real-world deployment.

Contribution

The paper introduces a novel deep neural network framework that effectively detects deceptive ads and phone scams, surpassing conventional blacklist and machine learning approaches.

Findings

High detection accuracy demonstrated in experiments

System successfully deployed in operational environments

Research materials and datasets publicly available

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.