Vulnerability Attack Prediction System Using ML Feature Extraction
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Solution Overview
Problem
Current vulnerability management tools lack predictive functionality to identify types of cybersecurity attacks associated with vulnerabilities and predict the likelihood of these attacks occurring, limiting the ability to prioritize and remediate security issues effectively.
Innovation Solution
The system uses machine learning models trained with known vulnerabilities and attack types to extract vulnerability features, apply them to attack class predictors, and generate probabilities of attack occurrence, providing attack type labels and suggested remedial actions through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional vulnerability management tools are used to identify vulnerabilities, then vulnerabilities can be detected and listed, but the ability to predict attack types and likelihood is lacking
Solution Approach 1:
The system performs preliminary analysis by extracting vulnerability features and applying them to attack class predictors before attacks occur. This allows prediction of attack types and likelihood scores to be generated in advance, enabling proactive security measures rather than reactive responses to known attacks.
Solution Approach 2:
The patent introduces an intermediary layer between vulnerability identification and attack response. The attack class predictor and likelihood score generator act as intermediaries that process vulnerability features and produce predictive outputs, bridging the gap between detecting vulnerabilities and preventing attacks.
2Measurement precision
If vulnerability features are extracted and processed through multiple classifiers, then attack prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system segments the prediction process into distinct stages: vulnerability feature extraction, attack class prediction, and likelihood score generation. Each stage uses specialized classifiers trained on specific aspects of vulnerability data, allowing for more accurate predictions while maintaining manageable processing time through modular architecture.
Solution Approach 2:
The system changes parameters by transforming vulnerability features into standardized input formats suitable for different attack class predictors. This normalization process enables efficient processing across multiple classifiers while maintaining prediction accuracy through consistent feature representation.
3Adaptability or versatility
If multiple attack types are predicted for each vulnerability, then comprehensive security coverage is achieved, but the complexity of analyzing and prioritizing vulnerabilities increases
Solution Approach 1:
The system provides feedback through likelihood scores that indicate the probability of each attack type occurring. This quantitative feedback allows security professionals to prioritize vulnerabilities based on predicted attack likelihood rather than manually analyzing each attack type, reducing complexity while maintaining comprehensive coverage.
Solution Approach 2:
The attack class predictors are designed to handle multiple attack types through a unified framework. The same vulnerability features can be processed by different classifiers to predict various attack types, providing multi-functional capability without requiring separate analysis systems for each attack vector.
Data Source
AI summary
The present disclosure provides systems and methods for predicting attack types and likelihood the attack types will occur for new vulnerabilities. According to the present disclosure, the method includes receiving a disclosure of a new vulnerability, the disclosure comprising a plurality of vulnerability details. The method includes developing a series of vulnerability features associated with the details of the new vulnerability. The method includes extracting each of the vulnerability features into intermediate inputs. The method includes providing each of the intermediate inputs to one or more attack type classifiers to thereby determine if an attack type is associated with the new vulnerability. The method includes determining ranking for each of the one or more attacks occurring for the new vulnerability. The method finally includes assigning one or more attack type labels to the new vulnerability based on each attack type associated with the new vulnerability. Other aspects are also described.