Endpoint Inter-Process Activity Graph for Stealthy Attack Detection
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Solution Overview
Problem
Current cybersecurity solutions are inadequate in detecting stealthy cyberattacks that leverage inter-process activities, as they often focus on single-process detection and struggle to identify direct and indirect inter-process behaviors, leading to missed detections and false alarms.
Innovation Solution
A method that records inter-process events, constructs an inter-process activity graph, and matches it against known malicious patterns to perform post-detection operations, enabling systematic modeling and detection of complex cyberattacks through direct and indirect inter-process activity analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signature matching approach is used, then detection of known attacks is achieved, but detection of new or rapidly updated attacks is delayed and inter-process activities are not detected
Solution Approach 1:
The system transitions from static signature matching to dynamic behavior-based detection by continuously monitoring and analyzing inter-process activities. The behavioral models are updated in real-time to adapt to new attack patterns, enabling the system to detect both known and emerging threats without manual signature updates.
Solution Approach 2:
The patent introduces a new dimension of analysis by examining inter-process activities and relationships between multiple processes rather than analyzing single processes in isolation. This multi-dimensional approach enables detection of sophisticated attacks that span multiple processes and evade traditional single-process detection mechanisms.
2Device complexity
If single-process detection mechanisms are used, then implementation simplicity is maintained, but detection of inter-process attacks fails
Solution Approach 1:
The system segments the analysis into distinct components: individual process behavior monitoring, inter-process activity tracking, and relationship analysis. This segmentation allows the complex multi-process detection task to be broken down into manageable parts while maintaining comprehensive coverage of inter-process attacks.
Solution Approach 2:
The patent introduces behavioral models as intermediary representations that capture both intra-process and inter-process behaviors. These models serve as mediators between raw system events and detection decisions, enabling the system to analyze complex inter-process relationships without requiring overly complicated detection logic.
3Productivity
If ad hoc inter-process activity modeling is used, then some direct activities are detected, but indirect inter-process activities and stealthy attacks are missed
Solution Approach 1:
The behavioral models are designed to be universal and multi-functional, capable of detecting both direct inter-process activities (such as process spawning) and indirect activities (such as file system interactions between processes). This universality enables a single detection framework to handle diverse attack patterns without requiring separate specialized models for each attack type.
Solution Approach 2:
The system implements continuous monitoring and analysis of inter-process activities rather than periodic or event-triggered checks. This continuous observation enables the detection of stealthy attacks that unfold over extended periods and ensures that indirect inter-process activities are captured even when they occur across different time windows.
4Reliability
If comprehensive inter-process monitoring is implemented, then detection coverage is improved, but system performance overhead increases
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on detecting specific inter-process activities that are most indicative of malicious behavior. Rather than monitoring all possible process interactions equally, the system prioritizes analysis of high-risk activities while maintaining awareness of overall system state, reducing overhead while preserving detection effectiveness.
Data Source
AI summary
An automated method for cyberattack detection and prevention in an endpoint. The technique monitors and protects the endpoint by recording inter-process events, creating an inter-process activity graph based on the recorded inter-process events, matching the inter-process activity (as represented in the activity graph) against known malicious or suspicious behavior (as embodied in a set of one or more pattern graphs), and performing a post-detection operation in response to a match between an inter-process activity and a known malicious or suspicious behavior pattern. Preferably, matching involves matching a subgraph in the activity graph with a known malicious or suspicious behavior pattern as represented in the pattern graph. During this processing, preferably both direct and indirect inter-process activities at the endpoint (or across a set of endpoints) are compared to the known behavior patterns. The approach herein provides for systematic modeling of inter-process behaviors for characterizing malicious or suspicious patterns among processes.