Multi-Level Fingerprinting for Network Threat Detection
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Solution Overview
Problem
Existing computing security systems struggle to detect threats effectively due to attackers modifying attack parameters, leading to variations that evade recognition by traditional signature-based systems.
Innovation Solution
The implementation of multi-level fingerprinting systems that analyze network traffic data to identify patterns correlated with known malicious signatures, generating new signatures based on these patterns to detect both known and unknown threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signature-based security systems are used to detect threats, then known attack patterns can be identified, but attackers can modify attack parameters to evade detection
Solution Approach 1:
The patent segments the attack detection into multiple levels: first-level fingerprinting identifies known attack patterns using traditional signatures, while second-level fingerprinting analyzes behavioral patterns and anomalies in network traffic. This segmentation allows the system to detect both known attacks and novel variations by operating at different analytical depths.
Solution Approach 2:
The patent transitions from single-level signature matching to multi-level fingerprinting that operates across multiple dimensions: traffic behavior patterns, temporal sequences, spatial relationships between packets, and statistical anomalies. This dimensional expansion enables detection of attacks that evade traditional signature-based approaches.
2Reliability
If multiple security signatures are created to detect attack variations, then detection coverage increases, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically generating new security signatures and update rules based on analyzed network traffic patterns. The second-level fingerprinting module autonomously identifies anomalies and creates corresponding detection rules, eliminating the need for manual signature creation and reducing operational complexity.
Solution Approach 2:
The patent implements feedback mechanisms where network traffic analysis results feed back into signature generation. The system continuously learns from observed traffic patterns, refines its detection rules, and updates its fingerprinting models, creating an adaptive detection system that improves over time without requiring proportional increases in complexity.
3Speed
If focus is placed on detecting known attack signatures, then immediate threat response is possible, but future attack variations remain undetected
Solution Approach 1:
The patent applies preliminary action by analyzing network traffic patterns in advance to identify potential threats before they manifest as known attack signatures. The second-level fingerprinting detects anomalous behaviors and creates proactive detection rules that can identify novel attacks as they occur, rather than waiting for known signatures to match.
Data Source
AI summary
A computer-implemented method may include receiving a set of security signatures for analysis; correlating the set of security signatures with corresponding computing traffic data within which the set of security signatures have appeared; extracting from the computing traffic data a set of features describing the computing traffic data; correlating the set of features with the set of security signatures; and generating a new security signature based at least in part on a correlation between the set of features and the set of security signatures. Various other methods and systems are also disclosed.