Signal Injection for Network Node Relationship Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods are inadequate for identifying relationships between network nodes, particularly in botnets, due to fast-changing domains and malware variants, making it difficult to link new domains to suspicious IP addresses and thwart botnet operations.
Innovation Solution
A method that injects a generated signal into a network node, blocking and unblocking data traffic in a predetermined pattern, and detects correlations with other nodes to associate them, using statistical techniques to identify relationships and block malicious traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DNS query/resolution pattern analysis is used to identify network node relationships, then some relationships can be detected, but the method is insufficient when large numbers of nodes are analyzed due to patterns without sufficient characteristics matching too many nodes
Solution Approach 1:
The patent uses signal injection to create observable changes in network traffic patterns, analogous to color changes. By injecting distinctive signals into nodes and observing how these signals propagate or are reflected, the system can precisely identify relationships between nodes. This approach transforms invisible traffic patterns into detectable signal responses, enabling accurate relationship identification without requiring complex analysis of all node interactions.
Solution Approach 2:
The patent introduces signals as intermediaries to facilitate node relationship identification. Instead of directly analyzing complex relationships between large numbers of nodes, the system injects signals that act as mediators to reveal relationships. These signals propagate through the network and their responses provide clear indicators of node connections, simplifying the analysis process while maintaining high precision.
2Reliability
If botnet command and control server identification is performed, then one node in the botnet can be identified, but by the time other nodes are identified, the network is likely to have changed due to fast-changing domains and malware variants
Solution Approach 1:
The patent applies preliminary action by injecting signals into nodes before full botnet identification is complete. Rather than waiting to identify all nodes sequentially, the system proactively signals multiple nodes and observes their responses simultaneously. This allows the identification process to keep pace with the botnet's changing infrastructure, maintaining reliability even as domains and malware variants evolve.
Solution Approach 2:
The patent uses periodic signal injection to continuously monitor and identify botnet nodes. By repeatedly injecting signals and observing responses over time, the system can track changes in the botnet infrastructure and identify new nodes as they appear. This periodic monitoring approach ensures that identification keeps up with the dynamic nature of botnets without requiring complete re-analysis of all nodes each time changes occur.
3Adaptability or versatility
If traffic analytics based on DNS query/resolution patterns are used, then some network node relationships can be identified, but patterns without sufficient characteristics match too many patterns, resulting in too many possibly-related nodes to analyze effectively
Solution Approach 1:
The patent uses signal injection to create distinctive, observable changes in network traffic that serve as unique identifiers for node relationships. Instead of relying on generic DNS patterns that match many nodes, the injected signals create specific, traceable responses that precisely identify relationships. This transforms ambiguous traffic patterns into clear, distinguishable signal responses, reducing the number of false positives and making analysis more effective.
Solution Approach 2:
The patent extracts specific relationship information by injecting targeted signals into individual nodes and observing their unique responses. Rather than analyzing all DNS patterns simultaneously and dealing with excessive matches, the system extracts relationship data node-by-node through signal injection. This selective extraction approach isolates specific relationships of interest from the broader network traffic, making analysis more manageable and effective.
Data Source
AI summary
A generated signal is injected into a first network node in a set of network nodes. The generated signal comprises a predetermined pattern, the predetermined pattern comprises a plurality of time periods, wherein during each time period in the plurality of time periods a first data traffic is prevented from exiting the first network node. By monitoring data flow within the set of network nodes while the generated signal is being injected, a correlation with the generated signal is detected, the correlation correlating a second network node with the first network node. The second network node is associated with the first network node. Responsive to the association, traffic from the second network node to the set of network nodes is blocked.