Multi-Stage Event Detector for Cyber-Attack Identification
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Solution Overview
Problem
Current systems face challenges in detecting sophisticated multi-stage cyber-attacks, which often involve complex sequences of events that can evade single monitors and require linking various activities to identify, especially when external factors like social media discussions and vulnerabilities are not considered.
Innovation Solution
A multi-stage event detector using Hidden Markov Models (HMMs) with a transition determiner to verify and refine hidden state determinations, incorporating parameter generating units and event detecting units to track and distinguish between different stages of attacks, and a process generator to manage detection agents and trigger events, allowing for early notification of potential threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single monitor is used to detect cyber-attacks, then the device complexity is low, but the detection precision deteriorates because sophisticated multi-stage attacks cannot be detected
Solution Approach 1:
The detection system is segmented into multiple independent detector units, each responsible for detecting specific stages of cyber-attacks (e.g., initial access, execution, persistence). Each detector analyzes particular indicators relevant to its stage, allowing the system to detect complex multi-stage attacks by combining results from multiple specialized detectors rather than using a single general-purpose monitor
Solution Approach 2:
The patent merges outputs from multiple independent detector units into a unified detection result. The system combines detection results from different detectors monitoring various attack stages, correlating them to identify multi-stage attacks. This merging process enables the system to achieve high detection precision for sophisticated attacks while maintaining modular detector design
2Measurement precision
If multiple detectors act in isolation to monitor different attack stages, then the device complexity increases, but the detection precision deteriorates because they cannot correlate activities across stages
Solution Approach 1:
The system implements feedback mechanisms where detection results from one stage inform detection at subsequent stages. Detectors share information about detected activities, and the correlation unit uses feedback from multiple detectors to refine detection accuracy. This feedback loop enables the system to achieve high detection precision by continuously adjusting detection based on accumulated evidence across attack stages
Solution Approach 2:
A correlation unit acts as an intermediary between independent detectors, receiving detection results from multiple detectors and correlating them to identify multi-stage attacks. This intermediary component enables detectors to work together effectively without requiring direct integration, maintaining their independence while achieving coordinated detection through the correlation layer
3Measurement precision
If the system monitors only traditional network traffic without considering external factors like social media, then the device complexity is low, but the detection precision deteriorates because early attack indicators are missed
Solution Approach 1:
The detection system is designed with multi-functionality to monitor diverse data sources including traditional network traffic, social media platforms, news sources, and vulnerability databases. Different detector units are configured to analyze specific data types (e.g., one detector monitors social media for attack discussions, another monitors network traffic for suspicious patterns), enabling the system to detect attacks at multiple stages across different information domains while maintaining specialized detection capabilities
Solution Approach 2:
The system extends detection beyond the traditional single dimension of network traffic analysis by incorporating external information dimensions such as social media discussions, news articles, and vulnerability announcements. This multi-dimensional approach allows the system to detect early indicators of attacks that manifest in external channels before reaching the target network, achieving earlier and more accurate detection
4Reliability
If the system waits for complete attack sequences to detect threats, then the detection precision is high, but the loss of time increases making response difficult
Solution Approach 1:
The system performs preliminary detection actions by monitoring for early-stage indicators of attacks (such as reconnaissance activities, initial access attempts, or discussions about potential targets on social media) before the complete attack sequence unfolds. Detectors are configured to trigger alerts when early indicators are detected, enabling security teams to take preventive actions before the attack reaches later, more damaging stages while maintaining reliable detection through correlation of subsequent events
Data Source
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AI summary
A multi-stage event detector for monitoring a system, the multi-stage event detector including: a process generator operable to generate main and sub-processes, each main and sub-process being operable to generate and initiate one or more detection agents each of which is operable to be triggered by detecting the occurrence of a trigger event and to report back to its generating process or sub-process upon being so triggered. Each process or sub-process is operable to respond to receipt of a report from a triggered detection agent by either reporting the detection of a multi-stage event or part thereof to either a parent process or sub-process or to an overall controller, or to generate and initiate a detection agent or a sub-process.