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ACM Computing Surveys (CSUR)
An analysis of Internet chat systems
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Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Ending Spam: Bayesian Content Filtering and the Art of Statistical Language Classification
Awarded Best Paper! - Scalable Centralized Bayesian Spam Mitigation with Bogofilter
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On instant messaging worms, analysis and countermeasures
Proceedings of the 2005 ACM workshop on Rapid malcode
Fast statistical spam filter by approximate classifications
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Detecting covert timing channels: an entropy-based approach
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An advanced hybrid peer-to-peer botnet
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Rishi: identify bot contaminated hosts by IRC nickname evaluation
HotBots'07 Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets
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WOOT '07 Proceedings of the first USENIX workshop on Offensive Technologies
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SS'07 Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium
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EUROCRYPT'03 Proceedings of the 22nd international conference on Theory and applications of cryptographic techniques
Detecting and filtering instant messaging spam: a global and personalized approach
NPSEC'05 Proceedings of the First international conference on Secure network protocols
A study of Internet instant messaging and chat protocols
IEEE Network: The Magazine of Global Internetworking
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Proceedings of the 16th ACM conference on Computer and communications security
Honeybot, your man in the middle for automated social engineering
LEET'10 Proceedings of the 3rd USENIX conference on Large-scale exploits and emergent threats: botnets, spyware, worms, and more
Detecting algorithmically generated malicious domain names
IMC '10 Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
Who is tweeting on Twitter: human, bot, or cyborg?
Proceedings of the 26th Annual Computer Security Applications Conference
Applying biometric principles to avatar recognition
Transactions on computational science XII
User-Assisted host-based detection of outbound malware traffic
ICICS'09 Proceedings of the 11th international conference on Information and Communications Security
Review: SMS spam filtering: Methods and data
Expert Systems with Applications: An International Journal
Detecting algorithmically generated domain-flux attacks with DNS traffic analysis
IEEE/ACM Transactions on Networking (TON)
Computer Networks: The International Journal of Computer and Telecommunications Networking
Effective bot host detection based on network failure models
Computer Networks: The International Journal of Computer and Telecommunications Networking
Architecture specification of rule-based deep web crawler with indexer
International Journal of Knowledge and Web Intelligence
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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 machine-learning-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.