Botnet Detection Module Scoring HTTP Connections
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network security technologies are inadequate in detecting malicious command and control channels, particularly botnets that employ stealthy tactics such as using normal network connections and encryption, which makes it difficult to distinguish them from legitimate traffic, and reputation systems are ineffective against zero-day threats.
Innovation Solution
A network environment with a sensor equipped with a botnet detection module that uses behavioral analysis and heuristics to identify repetitive HTTP connections from idle hosts, scoring these connections to detect botnets and other threats without the need for signature updates, and taking appropriate actions based on policy and confidence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reputation systems are used to detect malicious traffic, then known threats can be identified, but zero-day threats and stealthy botnets cannot be detected
Solution Approach 1:
The system performs preliminary behavioral analysis by monitoring and scoring connection patterns before malicious activity occurs. It establishes baseline behavior for hosts and proactively identifies deviations, enabling detection of zero-day threats before they can execute their malicious payload. The scoring mechanism accumulates evidence over time, allowing the system to flag suspicious activity before it becomes confirmed malware behavior.
Solution Approach 2:
The patent replaces traditional signature-based detection mechanisms with a behavioral scoring system. Instead of relying on predefined malicious patterns (mechanical rules), the system uses dynamic behavioral analysis that observes actual connection patterns, timing, and host states. This substitution enables detection of previously unseen threats by analyzing behavior rather than matching known signatures.
2Reliability
If traditional detection methods are used, then signature-based threats can be identified, but frequent updates are required to maintain effectiveness
Solution Approach 1:
The system performs self-updating through continuous behavioral learning. Instead of requiring external signature databases to be updated, the system automatically adapts by monitoring connection patterns, learning new behaviors, and adjusting its scoring thresholds dynamically. The behavioral profiles are continuously refined based on observed traffic, allowing the system to maintain detection effectiveness without manual updates.
Solution Approach 2:
The detection system transitions from static signature databases to dynamic behavioral analysis. The scoring thresholds and behavioral profiles are not fixed but continuously adapt based on observed network traffic patterns. This dynamic approach allows the system to automatically respond to new threat variants without requiring updates, as the behavior-based detection naturally evolves with changing traffic patterns.
3Measurement precision
If behavioral analysis is performed on all network traffic, then detection accuracy improves, but system complexity and processing overhead increase
Solution Approach 1:
The system applies behavioral analysis selectively rather than uniformly across all traffic. It focuses monitoring resources on hosts exhibiting suspicious characteristics or those with higher risk profiles. The scoring mechanism prioritizes analysis of connections from hosts with abnormal behavior patterns, reducing the overall processing load while maintaining high detection accuracy for the most suspicious traffic.
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on observed behavior. Instead of continuously analyzing all traffic at maximum depth, it modifies its analysis intensity based on risk scores and connection patterns. Low-risk traffic receives minimal scrutiny while high-risk traffic triggers more intensive behavioral analysis, optimizing the balance between detection accuracy and system complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method is provided in one example embodiment that includes detecting repetitive connections from a source node to a destination node, calculating a score for the source node based on the connections, and taking a policy action if the score exceeds a threshold score. In more particular embodiments, the repetitive connections use a hypertext transfer protocol and may include connections to a small number of unique domains, connections to small number of unique resources associated with the destination node, and/or a large number of connections to a resource in a domain. Moreover, heuristics may be used to score the source node and identify behavior indicative of a threat, such as a bot or other malware.