ML Security Platform Predicting Attack Paths
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Solution Overview
Problem
Current security measures in computer networks and enterprise environments fail to effectively predict and mitigate potential attack paths and vulnerabilities, leading to increased risks of unauthorized access and cyberattacks.
Innovation Solution
A security platform utilizing machine learning models to analyze contextual security information, identify vulnerabilities, and predict potential attack paths by correlating data from asset management and security tools, providing remediation actions to prevent exploitation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are used to monitor and detect vulnerabilities, then basic security coverage is maintained, but the ability to predict and prevent attack paths is insufficient
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict potential attack paths before actual attacks occur. The system analyzes vulnerability data, asset information, and security events to proactively identify and prioritize security risks, enabling security teams to remediate vulnerabilities before they can be exploited. This shifts security from reactive detection to predictive prevention.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw security data and security decision-making. The ML model processes and correlates diverse security inputs (vulnerability scans, asset inventories, security events) to generate predictive insights about attack paths, bridging the gap between data collection and actionable security intelligence.
2Measurement precision
If comprehensive security data collection is implemented to improve attack path prediction, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and pre-processing security data in the background, maintaining updated models of asset inventories, vulnerability landscapes, and attack patterns. This pre-computation enables rapid prediction and analysis when security events occur, reducing real-time processing requirements.
Solution Approach 2:
The patent applies partial action by prioritizing the analysis of the most critical and relevant security data. The machine learning model focuses computational resources on processing high-value inputs that most significantly impact attack path predictions, rather than uniformly processing all security data with equal depth, thus optimizing the balance between accuracy and processing speed.
3Reliability
If machine learning models are deployed to predict attack paths, then security prediction capability is enhanced, but system resource consumption increases
Solution Approach 1:
The system uses partial action by deploying machine learning models selectively for the most critical prediction tasks. Rather than continuously running full-scale ML inference on all security data, the system triggers predictions based on specific events or thresholds, and focuses computational resources on analyzing the most impactful vulnerability-attack relationships, reducing overall resource consumption while maintaining prediction reliability.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the complexity and resource allocation of machine learning model inference based on security conditions. The system can modify model parameters, sampling rates, and analysis depth according to the current security posture and threat level, optimizing the balance between prediction accuracy and computational resource usage.
Data Source
AI summary
Systems and methods for predictive analysis of potential attack patterns based on contextual security information are described. In one embodiment, a method includes generating a profile for an enterprise that indicates one or more software application stacks and a network architecture for the one or more software application stacks; determining one or more vulnerabilities of the one or more software application stacks and one or more vulnerabilities of the network architecture; providing the one or more vulnerabilities of the one or more software application stacks, the one or more vulnerabilities of the network architecture, and the profile as input to a machine learning model; generating an inference by the machine learning model that indicates one or more attack paths for an attacker in the one or more software application stacks and the network architecture; and transmitting the inference to a storage location or a security software application.