Tripartite Download Graph for Malware Detection
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Solution Overview
Problem
Existing solutions for preventing remote malware downloads, such as antivirus scanning and URL blacklists, are inadequate as they lag behind new threats and can be circumvented, leaving computers vulnerable to infections.
Innovation Solution
A system that utilizes a malware download detection system to build and update a tripartite download graph, which includes files, URLs, and client machines, and uses classifiers to classify download events as malicious, thereby proactively detecting and quarantining malware by analyzing network and system-level information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If antivirus scanning and URL blacklists are used to prevent malware downloads, then existing malware can be blocked, but new malware threats cannot be detected in real-time
Solution Approach 1:
The system performs preliminary actions by proactively monitoring download events, files, and URLs before malware can execute or spread. It builds a tripartite download graph in advance that captures relationships between clients, files, and URLs, enabling rapid classification of new threats as they emerge without waiting for traditional signature updates.
Solution Approach 2:
The system implements continuous feedback loops where classification results from the machine learning models are fed back into the tripartite download graph, which is continuously updated with new download events. This feedback mechanism allows the system to learn from new threats and improve detection accuracy in real-time, addressing both reliability and response time requirements.
2Measurement precision
If traditional malware detection methods are used, then known malware signatures can be identified, but malware code obfuscation and domain distribution circumvent detection
Solution Approach 1:
The system transitions from traditional single-dimension signature matching to a multi-dimensional approach by constructing a tripartite download graph that captures relationships across three dimensions: client machines, files, and URLs. This dimensional expansion enables the system to detect malware through contextual relationships rather than relying solely on file signatures, effectively countering obfuscation and domain distribution techniques.
Solution Approach 2:
The machine learning classification system serves multiple functions simultaneously: it classifies files as malicious or benign, evaluates URLs for malware distribution, analyzes download event patterns, and adapts to various attack methods including obfuscation and domain generation algorithms. This multi-functionality provides versatile defense against evolving threats while maintaining precise identification capabilities.
3Productivity
If real-time monitoring of all download events is implemented, then malware can be detected promptly, but system complexity and computational resources increase
Solution Approach 1:
The system segments the monitoring task into distinct components: client agents that collect download event data, a centralized system that builds and maintains the tripartite download graph, and machine learning classifiers that analyze the graph structure. This segmentation distributes computational complexity across multiple modules, enabling real-time monitoring while managing system complexity through modular architecture.
Solution Approach 2:
The tripartite download graph serves as an intermediary data structure that mediates between raw download event data and classification decisions. It organizes and stores relationships between clients, files, and URLs in a structured format that facilitates efficient machine learning analysis, reducing the computational burden of real-time monitoring while maintaining high detection speed.
Data Source
AI summary
A system for protecting computers against remote malware downloads includes a malware download detection system and participating client computers that provide download event information to the malware download detection system. A download event information identifies a file, a network address (e.g., uniform resource locator) from which the file was downloaded, and an identifier of the client computer that downloaded the file. The malware download detection system uses the download event information to build and update a tripartite download graph, and uses the download graph to train one or more classifiers. The malware download detection system consults the one or more classifiers to classify a download event. The download event is classified as malicious if either the file or the network address is classified as malicious.