Botnet Detection via Connectivity Graph Features
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Solution Overview
Problem
Existing methods for detecting botnets in computer networks face challenges due to the use of HTTP protocols to bypass firewalls, encryption for stealth, and domain fast-flux techniques, which obscure command-and-control channels, making it difficult to identify periodic beaconing traffic amidst high background noise and random timing perturbations.
Innovation Solution
A method using machine learning algorithms to analyze historical and real-time network data, extracting connectivity graph-based features such as outdegree, indegree, PageRank, and anti-trust rank, to differentiate between legitimate and malicious IP addresses, with supervised models like Elastic-Net regularized Logistic Regression and Logistic Model Trees for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional signature-based detection methods are used, then detection simplicity is maintained, but detection accuracy deteriorates due to encryption and obfuscation techniques
Solution Approach 1:
The patent replaces traditional mechanical signature-based detection with machine learning algorithms that analyze connectivity graph features. The system uses supervised learning models (such as decision trees, random forests, and neural networks) to classify network traffic patterns, substituting the rigid signature matching approach with a more adaptive, data-driven mechanism that can handle encrypted and obfuscated communications.
Solution Approach 2:
The patent changes the detection parameters from fixed payload signatures to dynamic connectivity graph features. Instead of looking for specific malicious payloads that can be encrypted, the system analyzes topological parameters such as node degrees, clustering coefficients, and path lengths in the network connectivity graph. This parameter transformation enables detection to work effectively against encrypted traffic while maintaining operational simplicity through automated feature extraction.
2Measurement precision
If detailed flow data is collected at high speed routers, then detection precision is improved, but network overhead increases to unacceptable levels
Solution Approach 1:
The patent extracts only the essential connectivity information from high-speed router flow data to construct the network connectivity graph. Instead of processing complete detailed flow records, the system extracts key topological features such as source-destination IP pairs, connection timestamps, and communication patterns. This extraction approach maintains detection precision by preserving the critical structural information needed for botnet identification while significantly reducing the data volume and processing overhead.
Solution Approach 2:
The patent segments the network detection task into multiple independent components: flow data collection at routers, connectivity graph construction, feature extraction, and machine learning classification. By dividing the complex detection process into these modular segments, the system can process data more efficiently and reduce the computational burden on network devices, thereby lowering overall network overhead while maintaining high detection precision.
3Reliability
If periodic beaconing traffic is identified, then botnet detection capability is improved, but reliability deteriorates due to high background noise and random timing perturbations
Solution Approach 1:
The patent transitions from analyzing temporal patterns of beaconing traffic to analyzing the topological dimension of network connectivity. Instead of trying to detect periodic signals that are obscured by noise and timing variations, the system constructs a connectivity graph that captures the structural relationships between network nodes. This dimensional shift from time-domain signal analysis to graph-theoretic topology analysis enables reliable botnet detection by identifying characteristic connection patterns that persist despite temporal perturbations and background noise.
Data Source
AI summary
A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a connectivity graph based feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the connectivity graph based feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the connectivity graph based feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the connectivity graph based feature, and categorizing the data unit as associated with the botnet based on the label.