Web Application Attack Detection Using Ontology Learning Loops
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Solution Overview
Problem
Existing software and web application security mechanisms fail to detect and prevent complex attacks, such as business logic-based attacks and privilege escalation, due to their reliance on syntactic code analysis, and manual security testing is impractical for the increasing number of applications with complex logic implementations.
Innovation Solution
Implement an intelligent learning loop using artificial intelligence to create an ontology-based knowledge base from application request and response sequences, applying stochastic probabilistic measures and Markov Logic Networks for real-time anomaly detection and prevention of complex attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If syntactic code analysis is used for security detection, then implementation is simple, but detection capability for complex attacks is insufficient
Solution Approach 1:
The patent replaces traditional syntactic code analysis (mechanical rule-based system) with machine learning models that analyze runtime behavior patterns. The system captures application requests and responses, extracts behavioral features, and uses trained models to detect complex attacks like business logic attacks and privilege escalation that syntactic analysis cannot identify.
2Measurement precision
If manual security testing is performed, then detection accuracy for complex attacks is high, but scalability is poor
Solution Approach 1:
The system implements automated security testing that performs itself without continuous human intervention. Machine learning models are trained on captured application behavior data and automatically detect attacks. The system self-adapts by continuously learning from new attack patterns, eliminating the need for manual re-testing while maintaining high detection accuracy across multiple applications.
Solution Approach 2:
The patent creates virtual models of application behavior through machine learning that replicate the characteristics of manual security testing. These digital twins capture the essence of expert security analysis and can be deployed across multiple applications simultaneously, providing scalable automated testing with accuracy comparable to manual methods.
3Device complexity
If traditional security mechanisms are used, then system complexity is low, but ability to detect complex attacks is insufficient
Solution Approach 1:
The patent implements dynamic security detection that adapts to the specific application being tested. Machine learning models are trained on the actual runtime behavior of each application, allowing the system to detect attacks specific to that application's logic and patterns. This dynamic adaptation enables detection of complex attacks without requiring overly complex static rule sets.
Data Source
AI summary
An apparatus and method for cyber risk quantification calculated from the likelihood of a cyber-attack on the target enterprise and/or cyber ecosystem based on its security posture. The cyber-attack likelihood can be derived as a probability-based time-to-event (TTE) measure using survivor function analysis. The likelihood probability measure can also be passed to cyber risk frameworks to determine financial impacts of the cyber-attacks. Embodiments of the present invention also relate to an apparatus and method (1) to identify and validate application attack surfaces and protect web applications against business logic-based attacks, sensitive data leakage and privilege escalation attacks; and/or (2) that protects web applications against business logic-based attacks, sensitive data leakage and privilege escalation attacks. This can include implementing an intelligent learning loop using artificial intelligence that creates an ontology-based knowledge base from application request and response sequences. Stochastic probabilistic measures are preferably applied to a knowledge base for predicting malicious user actions in real time.