Partial Download Malware Scanning for Early File Blocking
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Solution Overview
Problem
Existing malware detection systems require complete file downloads for analysis, which can be resource-intensive and expose systems to potential threats before identification, leading to increased cleanup costs and performance challenges.
Innovation Solution
A deep learning-based system that analyzes partial file downloads using image-based malware detection, allowing classification before the file is fully transferred, thereby preventing malicious files from reaching the system and reducing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete file downloads are required for malware analysis, then analysis accuracy is improved, but detection time and resource consumption increase
Solution Approach 1:
The system performs preliminary malware detection by analyzing partial file downloads before complete downloads finish. The deep learning model processes image representations of downloaded file portions in real-time, enabling early classification of malicious files before they fully reach the enterprise network, thus preventing potential harm while reducing detection time.
2Measurement precision
If complete file downloads are required for malware analysis, then detection accuracy is improved, but network resource consumption and potential harm increase
Solution Approach 1:
The system performs preliminary malware detection by analyzing partial file downloads before complete downloads finish. The deep learning model processes image representations of downloaded file portions in real-time, enabling early classification of malicious files before they fully reach the enterprise network, thus preventing potential harm while reducing detection time.
Solution Approach 2:
The system extracts and analyzes only the necessary portions of downloaded files for malware detection, rather than requiring complete file transfers. By processing partial downloads through the deep learning model, the system can identify malicious content early and block it before it fully enters the network, reducing both resource consumption and potential harm.
3Productivity
If partial file downloads are analyzed for malware, then detection speed is improved, but analysis accuracy may decrease
Solution Approach 1:
The system changes the parameter of analysis timing from post-download to during-download. By processing image representations of partial file portions in real-time as they are downloaded, the deep learning model achieves both high detection speed and maintained accuracy. The model is trained to recognize malware patterns in partial data, enabling rapid classification without sacrificing precision.
Data Source
AI summary
By way of example, a method includes, responsive to a user request to download, from the internet, a downloadable file with executable content, downloading a portion of the downloadable file, wherein the downloadable file is not executable with the portion; after download the portion of the downloadable file, scanning the portion of the downloadable file for malware characteristics to classify the downloadable file; and completing downloading the downloadable file only after determining, based on the scanning of the portion of the downloadable file, that the downloadable file is not malware.