Real-Time Detection of Fast Flux Service Networks
CATCH '09 Proceedings of the 2009 Cybersecurity Applications & Technology Conference for Homeland Security
An improvement for fast-flux service networks detection based on data mining techniques
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Auto-learning of SMTP TCP transport-layer features for spam and abusive message detection
LISA'11 Proceedings of the 25th international conference on Large Installation System Administration
Behavioral analysis of botnets for threat intelligence
Information Systems and e-Business Management
Computer Networks: The International Journal of Computer and Telecommunications Networking
Survey and taxonomy of botnet research through life-cycle
ACM Computing Surveys (CSUR)
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Here we present a behavioral analysis of fast flux service networks (FFSNs) using our database of FFSNs collected over a period of 9 months. FFSNs exploit a network of compromised machines (zombies) for illegal activities such as spam campaigns, phishing scams and malware delivery using DNS record manipulation techniques. In this paper, we use our fast flux domain and IP database collected using our real-time fast flux network detection algorithm to analyze the behavior of fast flux networks [1]. Our results show that such networks share common lifecycle characteristics, and form clusters based on size, growth and type of malicious behavior.