Detection of botnets using combined host- and network-level information

Bots are coordinated by a command and control (C&C) infrastructure to launch attacks that seriously threaten the Internet services and users. Most botnet-detection approaches function at the network level and require the analysis of packets' payloads, raising privacy concerns and incurring large computational overheads. Moreover, network traffic analysis alone can seldom provide a complete picture of botnets' behavior. By contrast, in-host detection approaches are useful to identify each bot's host-wide behavior, but are susceptible to the host-resident malware if used alone. To address these limitations, we consider both the coordination within a botnet and the malicious behavior each bot exhibits at the host level, and propose a C&C protocol-independent detection framework that combines host- and network-level information for making detection decisions. The framework is shown to be effective in detecting various types of botnets with low false-alarm rates.