Open-Source Dependency Malware Detection Using Cached Graphs
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Solution Overview
Problem
Existing tools for identifying malware in open-source software require installation on a user's machine, consuming significant storage and bandwidth, and are inefficient in detecting malware that hides during installation, while generative machine learning is computationally expensive and not scalable.
Innovation Solution
A malware detection tool uses a known package cache organized as a directed acyclic graph (DAG) to efficiently compare files with stored dependencies, employing hashing techniques and generative machine learning only when necessary to determine malware presence, updating the cache with new information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing malware identification tools require full installation of packages on user machines, then malware detection capability is achieved, but storage space and bandwidth consumption increase significantly
Solution Approach 1:
The patent segments the malware detection process into two parts: (1) a server-side component that maintains a comprehensive database of package files and their malware status, and (2) a client-side component that only downloads and analyzes specific files or metadata. This segmentation allows the system to achieve thorough malware detection while minimizing the amount of data stored and transmitted on user machines.
Solution Approach 2:
The patent introduces a server as an intermediary between the user and the package repository. This server maintains a comprehensive database of package files, dependencies, and malware information, acting as a mediator that provides malware detection capabilities without requiring users to store or download complete package installations. The server intermediates by providing targeted file comparisons and malware information.
2Reliability
If existing malware tools scan all package files locally, then comprehensive malware detection is possible, but the process is inefficient and cannot detect malware that covers tracks during installation
Solution Approach 1:
The patent implements preliminary action by pre-computing and storing malware analysis results, file hashes, and dependency information in a server-side database before users need them. Packages are analyzed and their malware status is determined in advance, so when users request malware detection, the system can quickly compare against pre-analyzed data rather than performing full scans during installation or usage.
Solution Approach 2:
The patent replaces the mechanical approach of local full-package installation and scanning with a data-driven approach using cryptographic hashes and metadata comparison. Instead of physically installing and scanning all files, the system substitutes this with hash-based file identification and comparison against a database of known malware patterns, significantly improving detection efficiency.
3Measurement precision
If generative machine learning is used to process malware detection queries, then sophisticated analysis is achieved, but computational cost and time consumption increase
Solution Approach 1:
The patent applies partial action by using generative machine learning selectively rather than for all malware detection queries. The system first performs quick hash-based comparisons and rule-based checks, and only invokes computationally expensive generative ML models when preliminary analysis indicates potential malware presence or when the query requires sophisticated pattern recognition that simpler methods cannot provide.
Solution Approach 2:
The patent implements local quality by applying different levels of analysis sophistication to different parts of the detection process. Simple file comparisons and hash matches use lightweight algorithms, while specific suspicious patterns or ambiguous cases are directed to generative ML models for sophisticated analysis. This localized application of computational resources optimizes the balance between detection precision and energy consumption.
Data Source
AI summary
A request is received to scan a package integration for a malicious dependency. A subset of dependencies of the package integration is determined that, if executed by an application, would be used. A known package cache is referenced to determine that at least a portion of a file of the subset was not previously scanned. A graph representation of the portion is generated, the graph representation including a tree with edges that connect the portion with one or more further dependent files that depend from the portion. The portion and its further dependent files are scanned for malware, and the known package cache is updated with the tree and with results of the scan. It is determined whether malware is within the package integration using the known package cache and the results of the scan, and an alert is output where malware is detected.