Identifying botnets by capturing group activities in DNS traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking
Detecting parasite p2p botnet in eMule-like networks through quasi-periodicity recognition
ICISC'11 Proceedings of the 14th international conference on Information Security and Cryptology
Security and Communication Networks
Collaborative behavior visualization and its detection by observing darknet traffic
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
Simulation-based study of botnets and defense mechanisms against them
Journal of Computer and Systems Sciences International
Computer Networks: The International Journal of Computer and Telecommunications Networking
Dissecting SpyEye - Understanding the design of third generation botnets
Computer Networks: The International Journal of Computer and Telecommunications Networking
Malicious automatically generated domain name detection using Stateful-SBB
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Survey and taxonomy of botnet research through life-cycle
ACM Computing Surveys (CSUR)
Analyzing and defending against web-based malware
ACM Computing Surveys (CSUR)
Anomaly detection and mitigation at internet scale: a survey
AIMS'13 Proceedings of the 7th IFIP WG 6.6 international conference on Autonomous Infrastructure, Management, and Security: emerging management mechanisms for the future internet - Volume 7943
A botnet-based command and control approach relying on swarm intelligence
Journal of Network and Computer Applications
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Among the various forms of malware, botnets are emerging as the most serious threat against cyber-security as they provide a distributed platform for several illegal activities such as launching distributed denial of service attacks against critical targets, malware dissemination, phishing, and click fraud. The defining characteristic of botnets is the use of command and control channels through which they can be updated and directed. Recently, botnet detection has been an interesting research topic related to cyber-threat and cyber-crime prevention. This paper is a survey of botnet and botnet detection. The survey clarifies botnet phenomenon and discusses botnet detection techniques. This survey classifies botnet detection techniques into four classes: signature-based, anomaly-based, DNS-based, and mining-base. It summarizes botnet detection techniques in each class and provides a brief comparison of botnet detection techniques.