Streaming Threat Classification With Event Aggregation for Lower Alert Noise
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Solution Overview
Problem
Determining whether a program is malware or exhibiting malicious behavior is time-consuming and resource-intensive due to the increasing obfuscation of malicious software, leading to inefficient security measures.
Innovation Solution
A system that aggregates data from various devices, assigns unique identifiers to events, and compares their similarity and frequency to identify potential security threats, generating recommendation data to secure data streams from future malicious events by reducing urgent alerts and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional malware detection methods are used, then security coverage is maintained, but detection time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary actions by analyzing event data before actual malware execution occurs. It aggregates event data from multiple sources, identifies patterns indicative of malicious behavior, and generates threat predictions in advance, allowing security measures to be taken before the actual threat materializes, thus reducing detection time while maintaining security coverage.
Solution Approach 2:
The system introduces an intermediary layer of event aggregation and pattern analysis between raw system events and final threat detection. This intermediary component processes and correlates event data from multiple sources, identifying malicious patterns without requiring direct analysis of every individual event, thereby reducing computational resources and detection time while maintaining reliability.
2Measurement precision
If comprehensive malware analysis is performed, then detection accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system segments the comprehensive malware analysis task into smaller, manageable components by dividing event data into categories and processing them in parallel. It segments the analysis into event collection, event aggregation, pattern identification, and threat prediction stages, allowing computational resources to be allocated efficiently across different analysis depths without requiring exhaustive processing of all data.
Solution Approach 2:
The system applies local quality by differentiating the depth of analysis based on event characteristics and risk levels. High-risk events receive more intensive analysis while low-risk events are processed with lighter computational overhead. This localized approach to analysis depth maintains detection accuracy for critical threats while reducing overall computational resource consumption.
3Reliability
If all events are monitored and analyzed, then threat detection capability is maximized, but alert noise and false positives increase
Solution Approach 1:
The system merges multiple event data streams and correlates them to identify true threats. By combining events from different sources and timeframes, it can distinguish between isolated normal operations and coordinated malicious behavior patterns. This merging approach filters out alert noise by requiring multiple correlated indicators before triggering a threat alert, while maintaining comprehensive threat detection capability.
Solution Approach 2:
The system implements feedback mechanisms that continuously learn from analyzed events and adjust detection thresholds. By feedback from historical data and analyst input, it refines its pattern recognition to better distinguish between benign events and actual threats, reducing false positives and alert noise while maintaining high threat detection capability through iterative improvement.
Data Source
AI summary
Techniques for identify data usable for generating security recommendations are discussed herein. A system can determine unique identifiers for events associated with a data stream, and determine a frequency of different events occurring in the data stream. The system can generate recommendation data usable for defending the data stream from future malicious events based on a number of similar events occurring over a time period.