Malware Risk Prediction via User Behavior Analysis
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Solution Overview
Problem
Existing anti-malware systems often fail to prevent malware infections effectively, as they struggle to keep pace with the evolving number and types of malicious applications, and may compromise user experience by limiting downloads or settings, necessitating a more precise method to predict user likelihood of encountering malware.
Innovation Solution
A computer-implemented method that identifies high-risk users by analyzing their download behavior patterns, comparing them to those of users who have been infected with malware, and categorizing uncategorized users based on similarity thresholds, with the aim of enhancing security posture and reducing malware infection risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional systems limit user downloads or setting changes to increase protection, then malware prevention capability is improved, but usability of the computing device deteriorates
Solution Approach 1:
The system applies different security policies to different users based on their risk profiles. High-risk users receive more restrictive policies (e.g., download limitations, setting changes), while low-risk users experience minimal restrictions. This localized application of quality control resolves the contradiction by tailoring protection levels to individual user behaviors rather than applying uniform restrictions to all users.
Solution Approach 2:
The system dynamically adjusts security parameters such as download limits, setting change restrictions, and policy enforcement levels based on the user's risk score. As user behavior patterns change over time, the system modifies these parameters accordingly, allowing the security posture to adapt without permanently compromising usability for low-risk users.
2Reliability
If more anti-malware applications are run to improve protection, then malware detection capability is improved, but computing resources consumed increases
Solution Approach 1:
The system extracts and monitors only the specific download behavior patterns that are most indicative of malware risk, rather than analyzing all user activities. By focusing on key behavioral indicators such as download frequency, source diversity, and file types, the system achieves effective malware prediction with minimal computing resource consumption.
Solution Approach 2:
The system uses the user's own download behavior history and patterns as the primary data source for risk assessment, eliminating the need for extensive external scanning or multiple anti-malware applications. The behavioral analysis engine processes locally collected data to generate risk scores, reducing the computational overhead associated with traditional signature-based scanning approaches.
3Reliability
If traditional systems attempt to prevent all malware downloads, then security coverage is improved, but false positive rate increases
Solution Approach 1:
The system performs preliminary behavioral analysis on download patterns before classifying files as malicious. By establishing baseline user behavior and comparing new download activities against these patterns, the system can identify suspicious behaviors that may indicate malware attempts, reducing false positives compared to traditional signature-based methods that may incorrectly flag legitimate files.
Solution Approach 2:
The system continuously refines its malware prediction accuracy by incorporating feedback from actual malware infections and false positive cases. The behavioral analysis engine learns from confirmed malware patterns and adjusts its risk scoring methodology, improving measurement precision over time while maintaining comprehensive security coverage.
Data Source
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AI summary
The disclosed computer-implemented method for determining malicious-download risk based on user behavior may include (1) identifying a set of users that are at high risk for malicious downloads and a set of users that are at low risk for malicious downloads, (2) determining a high-risk pattern of download behavior that is shared by the set of high-risk users and that is not shared by the set of low- risk users, (3) analyzing download behavior of an uncategorized user over a predefined time period in order to categorize the download behavior as high-risk or low-risk, and (4) categorizing the uncategorized user as a high-risk user in response to determining that the download behavior of the uncategorized user falls within a predefined similarity threshold of the high-risk pattern of download behavior. A corresponding system and computer-readable media are also disclosed.