Feature Modification Guidance for Malicious ML Misclassification
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Solution Overview
Problem
Conventional systems for detecting malicious activities in computing operations are either overinclusive or underinclusive, leading to incorrect flagging of legitimate operations and lack transparency in guiding users to modify their behavior to avoid future blocks.
Innovation Solution
A feature modification system that analyzes the impact of various features on malicious activity predictions using a feature impact model, generating recommendations for modifying these features to avoid future blocks by integrating with a generative AI model for natural language output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static rules are applied to detect malicious activity, then detection coverage is improved, but false positive rate increases causing legitimate operations to be blocked
Solution Approach 1:
The system dynamically changes detection parameters by computing user-specific baseline behaviors and adjusting detection thresholds based on individual user patterns. Instead of applying fixed static rules, the system adapts parameters like operation timing, frequency, and sequence expectations to match each user's unique behavior profile, thereby reducing false positives while maintaining detection accuracy
Solution Approach 2:
The system performs preliminary learning of user behavior patterns during a baseline period before enforcement begins. By pre-computing user-specific baselines and establishing personalized operation profiles in advance, the system prepares detection parameters that are tailored to each user's legitimate behavior patterns, enabling accurate distinction between legitimate and malicious operations from the start
2Device complexity
If static detection rules are used, then system simplicity is maintained, but adaptability to individual user patterns is lost
Solution Approach 1:
The system transitions from static detection rules to dynamic, adaptive detection by continuously learning and updating user-specific behavior patterns. The detection model evolves over time as it processes user operations, adjusting its parameters and thresholds based on observed behavior changes, thereby achieving adaptability while maintaining a relatively simple overall system architecture
Solution Approach 2:
The system automatically learns and adapts to each user's behavior patterns without requiring manual configuration or intervention. The detection model self-adjusts by processing user operations and computing baseline behaviors autonomously, enabling the system to adapt to individual users while maintaining simplicity through automated self-configuration
3Reliability
If operations are blocked without transparency, then security enforcement is maintained, but user guidance for behavior modification is lost
Solution Approach 1:
The system implements feedback by providing users with specific, actionable guidance on which operations triggered blocks and how to modify their behavior to avoid future blocks. Instead of merely enforcing security blocks, the system communicates detection reasons and suggests corrective actions, enabling users to understand and adjust their behavior patterns while maintaining security enforcement
Solution Approach 2:
The system introduces an intermediary communication layer between the detection model and the user, translating technical detection decisions into understandable guidance. This intermediary provides users with information about why operations were blocked and how to modify behavior, bridging the gap between security enforcement and user understanding without compromising either
Data Source
AI summary
Systems and methods for analyzing operations to determine factors contributing to a malicious classification and providing recommendations to prevent future misclassifications are disclosed herein. A feature modification system receives operation data associated with monitored operations, where each operation is characterized by a set of feature values. A machine learning-based detection model processes the operation data to generate a prediction indicating whether each operation is malicious. Based on a prediction indicating that an operation has been classified as malicious, the system determines the impact of each feature on the prediction as well as the variability of each feature to identify impactful and modifiable features. The system generates an input dataset including entries with modified features and processes the dataset to obtain a new set of predictions. If a modified entry results in a non-malicious classification, the system generates a recommendation indicating the modifications required to avoid future misclassifications.