Attack Tree Security Alert Prioritization
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Solution Overview
Problem
Current intrusion detection and security information management systems generate a high volume of false alarms, overwhelming security analysts and making it difficult to prioritize critical alerts, leading to delayed responses and resource inefficiency in identifying and addressing potential threats.
Innovation Solution
The SilverlineRT system analyzes and prioritizes security alerts using an attack tree model that considers overall system architecture and mission goals, calculating impact and risk metrics to automatically rank and respond to threats, thereby reducing false positives and enhancing automated response capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of asset behavior is implemented to detect anomalous events, then detection capability is improved, but false positive rate increases and human resources are consumed
Solution Approach 1:
The system segments the attack detection process into multiple hierarchical levels using attack trees. Each node represents a specific attack condition or indicator, allowing the system to evaluate alerts at different levels of granularity. This segmentation enables selective monitoring and evaluation of only the most relevant attack indicators, reducing false positives while maintaining comprehensive detection capability.
Solution Approach 2:
The system performs preliminary actions by pre-defining attack trees with multiple levels of attack indicators and conditions before actual monitoring occurs. These pre-structured attack trees serve as filters that automatically prioritize and categorize alerts based on predefined attack patterns, reducing the need for human analysts to sort through all alerts manually.
2Measurement precision
If human specialists manually sort through events to determine priority, then accuracy of threat identification is improved, but time consumption increases
Solution Approach 1:
The system implements feedback mechanisms where attack tree evaluation results automatically adjust alert prioritization and routing. The feedback loop continuously refines the analysis by comparing detected indicators against the hierarchical attack tree structure, enabling automated prioritization that improves over time as the system learns from analyst feedback and adjusts its evaluation criteria.
Solution Approach 2:
The attack tree model acts as an intermediary between raw alert data and human analyst decisions. It processes and pre-sorts alerts according to predefined attack patterns and priorities, presenting only the most relevant events to analysts. This intermediary layer maintains accuracy by preserving contextual relationships between different attack indicators while significantly reducing the time needed for manual analysis.
3Speed
If automated response systems are implemented to reduce response time, then speed of response is improved, but system complexity increases
Solution Approach 1:
The system implements dynamic response capabilities where the automated response actions are adjusted based on the current state of the attack tree evaluation. The response mechanism dynamically selects appropriate actions (such as alert routing, isolation, or notification) based on the specific attack indicators detected and their positions in the hierarchical attack tree, enabling flexible automated responses without requiring complex predetermined rules for every possible scenario.
4Reliability
If security personnel are deployed to monitor highly protected assets, then detection and response capability is improved, but operational cost increases
Solution Approach 1:
The attack tree system enables self-service monitoring by automatically evaluating alerts against predefined attack patterns and prioritizing events based on their severity and relevance. The system performs self-diagnosis and self-prioritization of security events, reducing the need for continuous human oversight while maintaining high security monitoring capability. This self-service approach allows the system to operate autonomously for routine monitoring tasks, with human analysts only介入 for complex or high-priority events.
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and products comprise an analytic server, which provides a SilverlineRT system that prioritizes and analyzes security alerts and events. The server builds an attack tree based on attack detection rules. The server monitors large-scale distributed systems and receives alerts from various devices. The server determines attacks using the attack tree while excluding false alarms. The server determines impact and risk metrics for attacks in real-time, and calculates an impact score for each attack. The server ranks and prioritizes the attacks based on the impact scores. The server also generates real-time reports. By consider the mission and system specific context in the analysis alert information, the server gives insight into the overall context of problems and potential solutions, improving decision-making. By showing the impacts of alters, the server allows security personnel to prioritize responses and focus on highest value defense activities.