Contextual Session Prediction to Reduce Cloud Security False Positives
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Solution Overview
Problem
Cloud computing services are vulnerable to cyber threats, and existing security schemes often generate high false-positive detections due to their inability to contextualize user behavior, leading to inefficient resource utilization.
Innovation Solution
A contextual session-based prediction system that generates and compares current user sessions to historical profiles using machine learning to identify normal or abnormal operations, allowing for real-time alerts and actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security schemes monitor user behavior without contextualization, then detection coverage is provided, but false-positive detections increase and resource utilization becomes inefficient
Solution Approach 1:
The system pre-generates contextual session profiles containing historical and expected user behavior patterns before actual security monitoring occurs. These profiles include normal operational sequences, typical user interactions, and expected event flows, enabling the system to quickly compare current sessions against established baselines and reduce false positives through pre-established contextual understanding
Solution Approach 2:
The patent introduces contextual session profiles as an intermediary layer between raw user events and security detection algorithms. These profiles act as mediators that translate complex user behavior into comparable contextual patterns, allowing the detection system to evaluate whether current sessions deviate from expected behavior without directly analyzing every individual event, thereby reducing false positives while maintaining detection accuracy
2Measurement precision
If multiple events are used to generate contextual sessions, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct components: event collection, contextual session generation, profile matching, and anomaly detection. By dividing the monitoring process into these manageable segments, the system can process multiple events efficiently without overwhelming complexity, as each segment handles a specific aspect of the analysis independently
Solution Approach 2:
The patent creates simplified copies of user sessions in the form of contextual session profiles that capture essential behavioral patterns without replicating every detail of actual user interactions. These profile copies enable efficient comparison and detection while reducing the computational burden of analyzing complete raw event sequences, thereby improving detection accuracy without proportionally increasing system complexity
3Speed
If real-time contextual session comparison is performed, then abnormal behavior detection speed increases, but computational resources are consumed
Solution Approach 1:
The system performs partial comparison by focusing on key contextual features and critical deviation points rather than exhaustively analyzing every aspect of each session against the profile. This selective approach enables real-time detection of abnormal behavior by identifying significant deviations without consuming computational resources proportional to complete session analysis, thus maintaining detection speed while managing resource consumption
Data Source
AI summary
Contextual session-based operational prediction greatly improves computer functioning. As a cloud service is provided, a current contextual session is generated using multiple events provided by the cloud service. The current contextual session is compared to a contextual session profile. The contextual session profile represents historical contextual sessions that have been historically logged in associated with the cloud service. If the current contextual session conforms to the contextual session profile, then the cloud service is normally operating as historically observed and may be predicted as normal operation. If, however, the current contextual session fails to conform to the contextual session profile, then the cloud service is not operating as historically observed and may be predicted as abnormal operation. Alerts and warning may be generated to notify of abnormal cloud service operation. The contextual session-based operational prediction produces a faster and more accurate detection of the abnormal operation.