Multi-Stage Anomaly Detection for Ransomware
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Solution Overview
Problem
Ransomware attacks, particularly human-operated ransomware (HumOR), pose a significant threat to enterprises due to their targeted nature and the challenge of detecting deviations from normal user behavior, especially when attackers blend in with legitimate activities, making it difficult to identify compromised accounts and prevent data encryption or access locking.
Innovation Solution
A multi-stage anomaly detection system using a lightweight first-stage detector and a time-series based transformer in the second stage to model individual account behaviors, identifying deviations from normal patterns and flagging potentially compromised accounts, with unsupervised learning to reduce computational overhead and false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional security monitoring methods are used to detect ransomware attacks, then the system can monitor user activities, but it fails to detect deviations from normal user behavior when attackers blend in with legitimate activities
Solution Approach 1:
The patent segments the anomaly detection process into multiple stages: a lightweight first-stage detector that filters obvious anomalies, and a second-stage time-series based transformer that performs detailed analysis on escalated cases. This segmentation allows the system to achieve high detection accuracy while managing computational complexity by applying intensive analysis only where needed.
Solution Approach 2:
The system dynamically adapts its detection approach by using unsupervised learning that automatically adjusts to normal user behavior patterns over time. The transformer model learns and adapts to evolving user behaviors, making the detection system dynamic rather than static, which improves accuracy without requiring manual reconfiguration.
2Reliability
If comprehensive user behavior monitoring is implemented to identify compromised accounts, then the detection rate improves, but computational overhead increases
Solution Approach 1:
The patent divides the monitoring population into two groups: those flagged by the lightweight first-stage detector and those that proceed to the second-stage transformer analysis. This segmentation ensures comprehensive monitoring of suspicious cases (maintaining high detection rate) while limiting intensive computational resources to only the necessary subset of cases (reducing overall computational overhead).
Solution Approach 2:
The system applies partial action by using a simplified detection approach for the majority of cases and reserving the more computationally intensive transformer analysis only for escalated cases. This partial application of intensive analysis maintains high detection rates where needed while avoiding unnecessary computational expenditure on clearly benign cases.
3Measurement precision
If advanced anomaly detection algorithms are used to identify lateral movement patterns, then false positives decrease, but system complexity increases
Solution Approach 1:
The patent segments the detection algorithm into a simple rule-based or statistical first stage and a sophisticated transformer-based second stage. This segmentation reduces false positives by applying advanced analysis only to cases that warrant it, while keeping the overall system complexity manageable through the hierarchical structure.
Solution Approach 2:
The first-stage detector acts as an intermediary that filters and prepares cases for the second-stage transformer analysis. This intermediary layer reduces the burden on the complex algorithm by pre-processing and selecting only relevant cases, thereby reducing false positives without requiring the complex algorithm to handle all cases.
4Speed
If real-time analysis of all user activities is performed to detect compromised accounts, then detection speed improves, but computational resources are overwhelmed
Solution Approach 1:
The patent segments the computational workload by implementing a two-stage detection system where the lightweight first stage processes all cases rapidly, and only escalated cases are passed to the resource-intensive second stage. This segmentation maintains high detection speed for the overall system while preventing computational resource overload.
Solution Approach 2:
The system applies partial real-time analysis by performing rapid preliminary analysis on all cases and detailed real-time analysis only on escalated cases. This partial application of real-time processing maintains detection speed for critical cases while avoiding the resource overload that would result from applying full real-time analysis to all user activities.
Data Source
AI summary
In certain embodiments, a time series-based anomaly detection method is provided, which is able to identify anomalous user accounts highly effectively. An activity predictor is used to model normal behaviors of individual accounts and to assess an extent to which a current behavior associated with differs from its past normal behavior. Part of an activity sequence is inputted to the activity predictor, and a resulting activity prediction (the activity predictor's prediction of normal behavior) is compared with the remaining part of the sequence. In preferred embodiments, a multi-stage approach is used, with a more lightweight form of anomaly detection applied in a first stage, and the time-series based detection performed in a second stage only on a subset of activity sequences escalated from the first stage.