Malicious URL Classification Using IUPG Deep Learning
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Solution Overview
Problem
Existing malware detection techniques are easily circumvented by nefarious authors, necessitating improved methods for identifying and mitigating malicious software.
Innovation Solution
Implementing an 'Innocent Until Proven Guilty' (IUPG) learning framework for deep learning-based malware classification, which uses orthogonal features and static/dynamic analysis to enhance detection accuracy and resilience against adversarial attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional malware detection techniques are used, then detection simplicity is maintained, but detection accuracy and resilience against adversarial attacks deteriorates
Solution Approach 1:
The malware detection system is segmented into multiple independent analysis modules: static analysis component, dynamic analysis component, and feature extraction modules. Each module processes specific aspects of the malware independently, improving detection accuracy through specialized analysis while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system transitions from single-dimension detection to multi-dimensional analysis by incorporating both static features (code structure, metadata) and dynamic features (execution behavior, system calls). This dimensional expansion enables more comprehensive malware characterization and improves detection reliability against adversarial attacks.
2Measurement precision
If aggressive detection methods are used to reduce false negatives, then malware detection sensitivity improves, but false positive rate increases
Solution Approach 1:
The system implements feedback mechanisms where detection results from static analysis inform dynamic analysis priorities, and vice versa. Confidence scores from feature extraction feed back into the classification process, allowing the system to adjust detection aggressiveness based on accumulated evidence, thereby reducing both false negatives and false positives through iterative refinement.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on the specific malware characteristics and context. By changing parameters adaptively rather than using fixed aggressive thresholds, the system maintains high detection sensitivity while controlling false positive rates through context-aware parameter optimization.
3Reliability
If comprehensive static and dynamic analysis is performed, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary static analysis and feature extraction before dynamic execution. By pre-processing and extracting key features in advance, the system reduces the computational burden during time-critical detection phases, maintaining high detection accuracy while minimizing actual processing time when malware interception is most needed.
Solution Approach 2:
The system applies partial dynamic analysis by executing only critical code paths or limiting execution time for suspicious samples. This selective approach provides sufficient behavioral evidence for accurate detection without incurring the full time cost of complete dynamic analysis, balancing detection accuracy with processing efficiency.
Data Source
AI summary
Techniques for providing deep learning for malicious URL classification (URLC) using the innocent until proven guilty (IUPG) learning framework are disclosed. In some embodiments, a system, process, and/or computer program product includes storing a set comprising one or more innocent until proven guilty (IUPG) models for static analysis of a sample; performing a static analysis of one or more URLs associated with the sample, wherein performing the static analysis includes using at least one stored IUPG model; and determining that the sample is malicious based at least in part on the static analysis of the one or more URLs associated with the sample, and in response to determining that the sample is malicious, performing an action based on a security policy.