Bayesian Threat Graphs for Complex Cloud Attack Detection
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Solution Overview
Problem
Conventional automated techniques for detecting security threats in cloud systems are inefficient due to their reliance on hard-coded rules, noise, and inability to handle the complexity and breadth of modern attack patterns, limiting scalability and requiring substantial manual effort for updates.
Innovation Solution
A graph-based detection system utilizing Bayesian networks to automatically identify potential security threats by analyzing logs and events, grouping network nodes into clusters, removing redundant connections, and assigning scores based on historical patterns, with machine learning for pattern recognition and generation of an output graph indicating potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automated techniques use hard-coded rules and correlation engines, then detection can be performed automatically, but the techniques become noisy and require substantial manual effort for maintaining and updating rules
Solution Approach 1:
The patent replaces the mechanical correlation engine with a graph-based machine learning system. Instead of using hard-coded rules and statistical correlation, the system constructs a knowledge graph from security data and uses graph neural networks to automatically learn detection patterns, eliminating the need for manual rule maintenance while improving detection reliability
Solution Approach 2:
The graph-based system automatically learns and updates detection patterns from incoming security data without requiring manual intervention. The machine learning model continuously refines the knowledge graph and detection algorithms, enabling the system to self-improve and adapt to new threat patterns without substantial manual effort
2Extent of automation
If conventional automated techniques rely on hard-coded rules, then automated detection is possible, but maintaining and updating rules involves substantial effort, negatively affecting efficiency
Solution Approach 1:
The patent substitutes the manual rule-based system with an automated graph-based machine learning system that continuously learns from data, eliminating the productivity loss associated with maintaining hard-coded rules while preserving automated detection capabilities
3Extent of automation
If correlation engines are used for detection, then automated analysis can be performed, but the techniques are not comprehensively intelligent enough to detect complex attack patterns in modern cloud systems
Solution Approach 1:
The patent transitions from traditional correlation-based analysis to graph-based representation, adding the dimension of relational structure to security data. By representing entities and their relationships as a knowledge graph, the system can detect complex multi-step attack patterns that correlate engines cannot identify, significantly improving adaptability to modern cloud-based threats
Data Source
AI summary
Techniques are described herein that are capable of performing automatic graph-based detection of potential security threats. A Bayesian network is initialized using an association graph to establish connections among network nodes in the Bayesian network. The network nodes are grouped among clusters that correspond to respective intents. Patterns in the Bayesian network are identified. At least one redundant connection, which is redundant with regard to one or more other connections, is removed from the patterns. Scores are assigned to the respective patterns in the Bayesian network, based on knowledge of historical patterns and historical security threats, such that each score indicates a likelihood of the respective pattern to indicate a security threat. An output graph is automatically generated. The output graph includes each pattern that has a score that is greater than or equal to a score threshold. Each pattern in the output graph represents a potential security threat.