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Botnet Dection system
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Introduction Botnet problem Challenges for botnet detection
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What Is a Bot/Botnet? Bot A malware instance that runs autonomously and automatically on a compromised computer (zombie) without owner’s consent Profit-driven, professionally written, widely propagated Botnet (Bot Army): network of bots controlled by criminals Definition: “A coordinated group of malware instances that are controlled by a botmaster via some C&C channel” Architecture: centralized (e.g., IRC,HTTP), distributed (e.g., P2P) “25% of Internet PCs are part of a botnet!” ( - Vint Cerf)
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Botnets are used for … All DDoS attacks Spam Click fraud Information theft Phishing attacks Distributing other malware, e.g., spywarePCs are part of a botnet!” ( - Vint Cerf)
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Challenges for Botnet Detection Bots are stealthy on the infected machines – We focus on a network-based solution Bot infection is usually a multi-faceted and multiphased process – Only looking at one specific aspect likely to fail Bots are dynamically evolving – Static and signature-based approaches may not be effective Botnets can have very flexible design of C&C channels – A solution very specific to a botnet instance is not desirable
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Roadmap to three Detection Systems Bothunter: regardless of the C&C structure and network protocol, if they follow pre-defined infection live cycle Botsniffer:works for IRC and http, can be extended to detect centralized C&C botnets Botminer:independent of the protocol and structure
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BotHunter system-detection on single infected client Detecting Malware Infection Through IDS-Driven Dialog Correlation Monitors two-way communication flows between internal networks and the Internet for signs of bot and other malware Correlates dialog trail of inbound intrusion alarms with outbound communication patterns
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Bot infection case study: Phatbot
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Dialog-based Correlation BotHunter employs an Infection Lifecycle Model to detect host infection behavior
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Bothunter Architecture
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Evaluation Example: http://www.cyber- ta.org/releases/malware- analysis/public/2009-01-13-public/
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BotSniffer-detection on centralized C&C botnets(IRC,HTTP) WHY we will focus on C&C? C&C is essential to a botnet – Without C&C, bots are just discrete, unorganized infections C&C detection is important – Relatively stable and unlikely to change within botnets – Reveal C&C server and local victims – The weakest link
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Botnet C&C Communication Example
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Botnet C&C: Spatial-Temporal Correlation and Similarity
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BotSniffer Architecture
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Correlation Engine Based on two properties Response crowd – a set of clients that have (message/activity) response behavior -A Dense response crowd: the fraction of clients with message/activity behavior within the group is larger than a threshold (e.g., 0.5). A homogeneous response crowd – Many members have very similar responses
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Evaluation
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Why Botminer? Botnets can change their C&C content (encryption, etc.), protocols (IRC, HTTP, etc.),structures (P2P, etc.), C&C servers, dialog models So bothunter, botsniffer systems may be evaded. We need to consider more
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Revisit Botnet Definition “A coordinated group of malware instances that are controlled by a botmaster via some C&C channel” We need to monitor two planes – C-plane (C&C communication plane): “who is talking to whom” – A-plane (malicious activity plane): “who is doing what”
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C-Plane clustering What characterizes a communication flow (Cflow) between a local host and a remote service? –
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A-plane clustering
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Cross-clustering Two hosts in the same A-clusters and in at least one common C-cluster are clustered together
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Botminer Architecture
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Evaluation Data
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Evaluation Result(FP)
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Evaluation Result(Detection Rate)
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Botnet Detection Systems summary Bothunter: Vertical Correlation. Correlation on the behaviors of single host. Botsniffer: Horizontal Correlation. On centralized C&C botnets Botminer: Extension on Botsniffer, no limitations on the C&C types.
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Thank you! Questions?
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