TY - GEN
T1 - ClickGuard: Exposing Hidden Click Fraud via Mobile Sensor Side-channel Analysis
AU - Shi, Congcong
AU - Song, Rui
AU - Qi, Xinyu
AU - Song, Yubo
AU - Xiao, Bin
AU - Lu, Sanglu
N1 - Funding Information:
This work is supported in part by National Natural Science Foundation of China under Grant Nos. 61872174, 61832008, 61902175, 61872173, 61802169, 61772446; JiangSu Natural Science Foundation under Grant No. BK20190293, BK20180325. This work is partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization. This work is partially supported by the 2019 Science and Technology Project of SGCC “Research on End-to-End Security Threat Analysis and Precision Protection Technology of Ubiquitous Power Internet of Things”. This work is partially supported by the HK PolyU 4-ZZFF and G-YBJV.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Advertising income depends on the amount of clicks by users of websites and mobile applications. However, the emergence of click fraud greatly reduces the real benefits of the advertisement. Most existing researches focus on detecting click fraud by analyzing properties and patterns of click data streams, but attackers can construct data that looks legitimate by replaying former data streams. In this paper, we propose a novel system called ClickGuard to detect click fraud attacks. ClickGuard takes advantage of motion sensor signals from mobile devices, since the pattern of motion signals is completely different under real click events and fraud events. To prevent attackers from bypassing the system by faking the time-domain statistical characteristics of original signals, we introduce the MFCC algorithm in feature extraction phase. MFCC algorithm can extract frequency-domain features of original signals in specific frequency bands which are hardly constructed out of thin air. Classifiers are finally constructed using these features and several machine learning algorithms. Experiments show that ClickGuard can achieve the accuracy of 96.71% in general environment and 84.16% when attackers modify the time-domain statistical characteristics of raw data.
AB - Advertising income depends on the amount of clicks by users of websites and mobile applications. However, the emergence of click fraud greatly reduces the real benefits of the advertisement. Most existing researches focus on detecting click fraud by analyzing properties and patterns of click data streams, but attackers can construct data that looks legitimate by replaying former data streams. In this paper, we propose a novel system called ClickGuard to detect click fraud attacks. ClickGuard takes advantage of motion sensor signals from mobile devices, since the pattern of motion signals is completely different under real click events and fraud events. To prevent attackers from bypassing the system by faking the time-domain statistical characteristics of original signals, we introduce the MFCC algorithm in feature extraction phase. MFCC algorithm can extract frequency-domain features of original signals in specific frequency bands which are hardly constructed out of thin air. Classifiers are finally constructed using these features and several machine learning algorithms. Experiments show that ClickGuard can achieve the accuracy of 96.71% in general environment and 84.16% when attackers modify the time-domain statistical characteristics of raw data.
KW - Click Fraud
KW - MFCC
KW - Motion Sensor Signals
KW - Side-channel
UR - http://www.scopus.com/inward/record.url?scp=85089411999&partnerID=8YFLogxK
U2 - 10.1109/ICC40277.2020.9149420
DO - 10.1109/ICC40277.2020.9149420
M3 - Conference article published in proceeding or book
AN - SCOPUS:85089411999
T3 - IEEE International Conference on Communications
SP - 1
EP - 6
BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Communications, ICC 2020
Y2 - 7 June 2020 through 11 June 2020
ER -