Host Application User Agent Mapping for Zero Day Threat Detection
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Solution Overview
Problem
Current anti-malware systems rely on signature-based detection methods that are ineffective against zero-day threats and botnets that randomize their HTTP user-agent communications, failing to detect unknown malware and evaded defenses.
Innovation Solution
A host application to user-agent mapping technique is employed, where network devices intercept and analyze HTTP traffic to build a mapping table, identifying deviations from known patterns and generating alerts for potential malware, even in cases of randomized user-agent data, allowing for signatureless detection of malware and zero-day threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature-based detection methods are used, then known malware can be detected, but zero-day threats and randomized user-agent communications cannot be detected
Solution Approach 1:
The system dynamically builds and updates a mapping table that associates host applications with their expected user-agent strings. This mapping evolves over time as new applications are observed and their user-agent patterns are learned, allowing the system to adapt to changing traffic patterns while maintaining detection accuracy for both known and unknown threats
Solution Approach 2:
The patent introduces an intermediary mapping table that sits between the host application layer and the user-agent analysis layer. This mapping table serves as a mediator that translates host application identification into expected user-agent patterns, enabling detection without relying on traditional malware signatures
2Reliability
If user-agent signature matching is employed, then malware communication can be identified, but randomized user-agent strings evade detection
Solution Approach 1:
The system performs preliminary action by pre-establishing the mapping between host applications and their expected user-agent strings before malware attempts to communicate. This advance preparation allows the system to immediately recognize and flag deviations from expected patterns, preventing evasion by randomized user-agent strings
Solution Approach 2:
The system implements feedback by continuously monitoring HTTP traffic, comparing observed user-agent strings against the established mapping table, and using the results to update and refine the mapping. This feedback loop enhances detection reliability while adapting to new malware evasion techniques
3Measurement precision
If traditional anti-malware systems are used, then known threats are detected, but new zero-day threats go undetected
Solution Approach 1:
The system performs preliminary action by establishing baseline user-agent mappings for legitimate applications before malware infection occurs. This allows for immediate detection of zero-day threats that deviate from these baselines, eliminating the detection delay inherent in signature-based systems that wait for threat intelligence updates
Solution Approach 2:
The system provides self-service by automatically building and maintaining its own detection rules through the host application to user-agent mapping table. This eliminates dependence on external signature databases and threat intelligence feeds, enabling immediate detection of new threats without waiting for updates
Data Source
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AI summary
A technique allows associating host applications and user agents in network traffic and detecting possible malware without relying on signatures of the user agents. A database of host applications and user agents is maintained, allowing automatic update of the database when a new application or new application to user agent mapping is discovered. Partial matches may be made when a change is made to the application, allowing learning the new mapping automatically. If an application is associated with more than a threshold number of user agents, an indication may be generated that the application is suspicious and possibly malware.