Enterprise Process Trees for Proactive Threat Hypothesis Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional computer security techniques are often limited to reactive responses based on observed data, lacking proactive threat detection and anomaly detection capabilities, and struggle to continuously improve and adapt to evolving threats.
Innovation Solution
A method for conditional hypothesis generation for enterprise process trees that leverages observed data, historical data, and conditional reasoning to generate alternate process flows, using an encoder-decoder model and feedback loops to enhance threat detection and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional rule-based or statistical correlation engines are used to detect known cyber-attacks, then detection of known threats is achieved, but proactive threat detection and anomaly detection capabilities are lacking
Solution Approach 1:
The system performs preliminary action by generating hypothetical process trees representing potential attack scenarios before they occur. These hypothetical trees are created by modifying existing process trees to include alternative actions that could lead to security violations, enabling proactive detection of threats that have not yet been observed in the system.
Solution Approach 2:
The system creates copies of existing process trees to generate hypothetical scenarios. By copying the structural patterns from real process trees and introducing alternative actions, the system synthesizes realistic attack hypotheses that mirror actual system behavior, enabling anomaly detection without requiring identical replication of physical systems.
2Adaptability or versatility
If systems continuously improve and adapt to evolving threats, then detection effectiveness increases, but system complexity and computational resources increase
Solution Approach 1:
The system implements feedback by comparing hypothetical process trees against actual system logs and process trees. When discrepancies are detected between expected behavior from hypothetical scenarios and actual observed behavior, the system learns from these differences to improve future threat detection, enabling continuous adaptation without requiring complete system redesign.
Solution Approach 2:
The system applies dynamics by making the process tree generation adaptive and flexible. Rather than using fixed rules, the system dynamically generates hypothetical process trees based on learned patterns from historical data and evolving threat landscapes, allowing the detection system to adapt its behavior to changing conditions while maintaining manageable complexity through iterative refinement.
Data Source
AI summary
An embodiment for conditional hypothesis generation for enterprise process trees. The embodiment may receive a set of observed data. The embodiment may map the set of observed data with accessible historical data. The embodiment may perform conditional reasoning to determine, based on a process tree context for the set of observed data, functionality associated with the observed data and identify a series of alternative actions which, when performed, result in the determined functionality. The embodiment may generate, based on the mapped set of observed data and the identified series of alternative actions, a conditional hypothesis corresponding to a process flow.