Malware Analysis Platform Threat Intelligence Tagging
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Solution Overview
Problem
Current security approaches fail to effectively identify and address new, unknown malware threats, such as zero-day threats and Advanced Persistent Threats (APTs), which evade traditional detection methods and pose significant risks to networks.
Innovation Solution
A malware analysis platform for threat intelligence that generates an enhanced view of malware analysis results by processing log files through line counting techniques, identifying high-risk artifacts, and automatically generating signatures to block malicious activity, while also providing a dashboard for real-time threat monitoring and alerting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used, then existing malware can be blocked, but new unknown malware threats cannot be identified
Solution Approach 1:
The system performs preliminary actions by proactively analyzing malware samples in a sandbox environment before they can infect the production network. Multiple analysis techniques (static analysis, dynamic analysis, heuristic analysis) are executed in advance to identify threat patterns and generate intelligence data, preventing unknown threats from spreading.
Solution Approach 2:
A sandbox environment serves as an intermediary between the external threat source and the protected production network. The sandbox isolates malware execution, allowing safe analysis of unknown threats while preventing direct infection of the network. The threat intelligence platform acts as another intermediary layer that processes and contextualizes malware data before generating blocking rules.
2Measurement precision
If comprehensive malware analysis is performed, then threat identification improves, but analysis time increases
Solution Approach 1:
The malware analysis process is segmented into multiple independent techniques: static analysis (examining file properties without execution), dynamic analysis (monitoring runtime behavior), heuristic analysis (pattern recognition), and sandbox execution (isolated testing). These segmented techniques can be applied selectively based on threat level, reducing overall analysis time while maintaining detection accuracy.
Solution Approach 2:
The system applies partial analysis actions by selecting specific analysis techniques based on the suspected threat level and available intelligence. For low-risk samples, only lightweight static analysis is performed. For high-risk samples, comprehensive multi-technique analysis is executed. This partial action approach optimizes the balance between detection thoroughness and analysis time.
3Measurement precision
If manual threat analysis is performed, then detection accuracy is high, but processing speed decreases
Solution Approach 1:
The threat intelligence platform performs self-service by automatically ingesting malware samples, executing multiple analysis techniques, processing results, and generating blocking rules without continuous manual intervention. The system autonomously manages the entire threat analysis workflow, from sample reception to rule deployment, significantly increasing processing throughput while maintaining accuracy through automated quality controls.
Solution Approach 2:
The system implements feedback loops where analysis results from one sample inform the analysis of subsequent samples. Threat patterns identified in initial analyses are fed back into the heuristic engines to improve detection of similar threats. Blocking rules generated from analysis are fed back to the firewall to prevent future infections, creating a continuous improvement cycle that increases both accuracy and throughput.
Data Source
AI summary
Techniques for alerting and tagging using a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for alerting and tagging using a malware analysis platform for threat intelligence made actionable includes receiving a plurality of samples for performing automated malware analysis to generate log files based on the automated malware analysis; processing the log files to extract artifacts associated with the log files; determining whether a tag matches any of the plurality of samples based on the artifacts; and performing an action based on whether the tag matches any of the plurality of samples.