Measurement and Classification of Humans and Bots in Internet Chat

Abstract

The abuse of chat services by automated programs, known as chat bots, poses a serious threat to Internet users. Chat bots target popular chat networks to distribute spam and malware. In this paper, we first conduct a series of measurements on a large commercial chat network. Our measurements capture a total of 14 different types of chat bots ranging from simple to advanced. Moreover, we observe that human behavior is more complex than bot behavior. Based on the measurement study, we propose a classification system to accurately distinguish chat bots from human users. The proposed classification system consists of two components: (1) an entropy-based classifier and (2) a machinelearning-based classifier. The two classifiers complement each other in chat bot detection. The entropy-based classifier is more accurate to detect unknown chat bots, whereas the machine-learning-based classifier is faster to detect known chat bots. Our experimental evaluation shows that the proposed classification system is highly effective in differentiating bots from humans.

Extracted Key Phrases

9 Figures and Tables

Statistics

010202008200920102011201220132014201520162017
Citations per Year

75 Citations

Semantic Scholar estimates that this publication has 75 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Gianvecchio2008MeasurementAC, title={Measurement and Classification of Humans and Bots in Internet Chat}, author={Steven Gianvecchio and Mengjun Xie and Zhengyu Wu and Haining Wang}, booktitle={USENIX Security Symposium}, year={2008} }