Input Grammar Monitoring for Low-False-Positive Attack Detection
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Solution Overview
Problem
Existing intrusion detection systems struggle with high false positive rates and the challenge of obtaining sufficient training data for machine learning, making it difficult to effectively detect attacks on computer systems.
Innovation Solution
A method involving the continuous ascertainment of a context-free grammar that describes how a computer system processes inputs, comparing successive versions of this grammar to detect deviations that indicate potential attacks, and triggering appropriate security measures based on predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning-based intrusion detection systems are used, then detection capability is improved, but false positive rate increases and training data requirements increase
Solution Approach 1:
The patent replaces machine learning-based detection mechanisms with a formal grammar-based approach. Instead of using statistical models that require training data and produce false positives, the system uses context-free grammars to formally define legitimate input patterns, enabling precise detection without the drawbacks of ML methods.
Solution Approach 2:
The patent changes the fundamental parameter of detection from statistical probability (ML) to formal linguistic structure (grammar). By transforming detection into a problem of validating input against formal grammar rules, the system achieves high precision without false positives while maintaining strong detection capability.
2Reliability
If machine learning models are trained to detect attacks, then detection accuracy improves, but sufficient training data is difficult to obtain
Solution Approach 1:
The patent substitutes data-driven machine learning with rule-based formal grammar systems. Instead of requiring large quantities of training data to learn attack patterns, the system uses explicitly defined grammar rules that capture legitimate input structures, eliminating the data collection bottleneck entirely.
Solution Approach 2:
The patent performs preliminary action by formally defining the grammar rules for legitimate inputs before any detection occurs. This preliminary grammatical framework is established once and then used continuously for detection, eliminating the need for ongoing training data collection and model retraining.
3Measurement precision
If continuous monitoring of input grammar is performed, then attack detection precision improves, but system complexity increases
Solution Approach 1:
The patent implements self-service by having the system automatically update its own grammar knowledge base as it processes inputs. The grammar ascertainment tool continuously refines the context-free grammar based on observed input patterns, allowing the system to adapt and improve its detection precision without external intervention or complex configuration.
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously monitors input processing results and uses this feedback to refine the grammar model. The grammar ascertainment tool analyzes processing outcomes and updates the grammar rules accordingly, creating a self-improving detection system that maintains high precision while managing complexity through automated feedback loops.
Data Source
AI summary
A method for detecting attacks on a computer system. The method includes ascertaining an input grammar, according to which a program running on the computer system processes inputs, by observing how the program processes a set of inputs; receiving one or more further inputs to the program; checking whether the ascertained input grammar can generate the one or more further inputs; and triggering a security measure depending on whether the ascertained input grammar can generate the one or more further inputs.