Automated Malware Detection Rule Generation Using Machine Learning
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Solution Overview
Problem
Current cybersecurity systems face challenges in efficiently generating and updating malware detection rules due to the time-intensive and subjective nature of manual processes, leading to sub-optimal rule generation, resource waste, and increased false positives/negatives.
Innovation Solution
An automated malware detection rule generation system that uses machine learning models to analyze meta-information from monitored events, extracting salient features and generating rule recommendations, which are then tested and refined to improve detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of malware detection rules is performed by human analysts, then expertise and judgment can be applied to identify malicious patterns, but the process becomes highly time intensive and prone to subjective errors
Solution Approach 1:
The system enables automated self-service for rule generation by having the cybersecurity system automatically analyze detected events, identify patterns, and generate detection rules without requiring manual human intervention. The processor extracts features from event meta-information and autonomously formulates detection rules, eliminating the time-intensive manual review process while maintaining detection effectiveness.
Solution Approach 2:
The patent replaces the mechanical manual process of rule generation with an automated computational system. Instead of human analysts manually reviewing events and creating rules, a processor automatically performs feature extraction, pattern recognition, and rule formulation, substituting human cognitive work with machine-based automation.
2Reliability
If manual review and selection of detected events is performed by analysts, then subjective expertise can be applied to identify relevant patterns, but the process is slow and arduous, greatly delaying the release of malware detection rule updates
Solution Approach 1:
The system performs automated self-service by having the processor independently analyze detected events, extract relevant features, and generate detection rules without requiring slow manual analyst review. This automation maintains reliability through systematic feature extraction while dramatically improving productivity by eliminating human review bottlenecks.
Solution Approach 2:
The automated system enables continuous generation and updating of detection rules without the interruptions and delays inherent in manual processes. The processor can continuously analyze new events and update rules in real-time, maintaining continuous protective coverage rather than relying on periodic manual updates.
3Reliability
If malware detection rules are frequently evaluated and updated to maintain effectiveness against changing threats, then detection accuracy is maintained, but system resources are wasted when rules become repetitive or non-effective
Solution Approach 1:
The system incorporates feedback mechanisms where detection rules are automatically evaluated based on their performance in identifying malicious objects. The processor analyzes the effectiveness of generated rules and adjusts or removes rules that become repetitive or non-effective, creating a closed-loop system that optimizes resource utilization while maintaining detection reliability.
Solution Approach 2:
The system dynamically changes parameters of detection rules based on performance metrics and evolving threat patterns. The processor adjusts rule sensitivity, specificity, and priority levels to optimize the balance between detection effectiveness and resource consumption, removing or modifying rules that no longer serve their purpose.
Data Source
AI summary
A method for generating rule recommendation utilized in a creation of malware detection rules is described. Meta-information associated with a plurality of events collected during a malware detection analysis of an object by a cybersecurity system is received and a first plurality of features is selected from the received meta-information. Machine learning (ML) models are applied to each of the first plurality of features to generate a score that represents a level of maliciousness for the feature and thereby a degree of usefulness of the feature in classifying the object as malicious or benign. Thereafter, a second plurality of features is selected as the salient features, which are used in creation of the malware detection rules in controlling subsequent operations of the cybersecurity system. The second plurality of features being lesser in number that the first plurality of features.