Malware Analysis Platform for Threat Intelligence
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Solution Overview
Problem
Existing 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 processes log files from dynamic and static analysis to generate enhanced views of malware activity, identifies high-risk artifacts, and automatically generates signatures for detection, using line counting techniques to differentiate between benign and malicious activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection methods are used, then existing security approaches can detect known threats, but they fail to identify new, unknown malware threats such as zero-day threats and APTs
Solution Approach 1:
The system performs preliminary actions by conducting both static and dynamic analysis on malware samples before they can execute malicious activities in production environments. Static analysis examines the malware code structure, imports, and strings without execution, while dynamic analysis runs the malware in isolated sandboxes to observe actual behavior patterns. This preliminary investigation enables the system to generate detection signatures and threat intelligence before the malware can cause harm to real systems.
Solution Approach 2:
The malware analysis system segments the detection process into multiple independent analysis components: static analysis (examining code structure, imports, strings), dynamic analysis (observing runtime behavior in sandboxes), network analysis (monitoring communication patterns), and artifact extraction (identifying unique malware characteristics). Each segment produces specific findings that are aggregated to form comprehensive threat profiles and detection signatures, enabling multi-faceted detection of both known and unknown threats.
2Loss of information
If comprehensive malware analysis is performed, then threat intelligence quality improves, but analysis time and computational resources increase
Solution Approach 1:
The system applies partial action by performing static analysis on all incoming malware samples to extract basic characteristics, imports, and strings. For samples that require deeper investigation or show suspicious characteristics during static analysis, the system then applies dynamic analysis in isolated sandboxes. This tiered approach ensures that not every sample receives the full computational treatment, reducing overall analysis time while maintaining high threat intelligence quality for suspicious samples.
Solution Approach 2:
The system introduces intermediaries in the form of isolated sandbox environments that mediate between the malware sample and the analysis system. These sandboxes provide a controlled intermediate layer where dynamic analysis can be performed safely, capturing runtime behavior, network communications, and system interactions without risking the host system. This intermediary approach enables comprehensive behavioral analysis while protecting resources and enabling parallel processing of multiple samples.
3Productivity
If automated analysis is implemented, then processing speed increases, but the ability to detect sophisticated threats may be reduced
Solution Approach 1:
The system implements feedback mechanisms where automated static and dynamic analysis results are continuously evaluated, and detection signatures are refined based on observed malware behaviors and patterns. The system learns from analyzed samples by updating threat intelligence databases, refining detection rules, and adjusting analysis parameters. This feedback loop enables automated processing to maintain or improve detection accuracy over time, as the system adapts to new threat patterns while preserving processing speed through automation.
Data Source
AI summary
Techniques for a malware analysis platform for threat intelligence made actionable are disclosed. In some embodiments, a system, process, and/or computer program product for 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 determine artifacts associated with malware; and performing an action based on an artifact.