Bayesian Network XSS Risk Analysis Using STRIDE Threat Model
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Solution Overview
Problem
Current XSS attack detection methods, primarily relying on server-side input filtering, are inadequate in providing timely protection and lack quantitative risk analysis, failing to support effective defense decision-making in complex cyber environments.
Innovation Solution
An XSS risk analysis method based on a Bayesian network and STRIDE model is developed, constructing a threat model, extracting information assets and data flows, obtaining prior probabilities, and performing network training to reason quantitative XSS attack risks, enabling more robust defense strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If server-side input filtering is used for XSS prevention, then implementation simplicity is maintained, but detection accuracy and protection timeliness deteriorate with high missing report rates
Solution Approach 1:
The patent introduces a Bayesian network model as an intermediary between input filtering and attack detection. This model incorporates multiple risk factors (user behavior, historical data, network context) to evaluate XSS risks, thereby improving detection accuracy without completely replacing the simple filtering mechanism. The intermediary layer processes and analyzes data to provide more reliable detection results.
Solution Approach 2:
The patent combines multiple detection approaches into a composite defense system. It integrates traditional filtering methods with machine learning algorithms (random forest, support vector machine, neural networks) and Bayesian network analysis. This composite approach leverages the strengths of each method while compensating for their individual weaknesses, achieving both simplicity and accuracy.
2Reliability
If traditional machine learning algorithms are used for XSS detection, then detection effectiveness is improved, but quantitative risk analysis capability deteriorates
Solution Approach 1:
The patent merges traditional machine learning algorithms with Bayesian network theory. The system uses machine learning for accurate classification and detection while simultaneously employing Bayesian networks to calculate probabilistic risk scores. This combination preserves the detection effectiveness of machine learning while adding the quantitative risk analysis capability of Bayesian methods, preventing information loss.
Solution Approach 2:
The patent designs a multi-functional system that performs both detection and quantitative risk assessment. The Bayesian network model serves multiple purposes: it classifies XSS attacks, evaluates risk levels, provides probability distributions for different attack scenarios, and supports decision-making. This universal approach eliminates the need for separate systems for detection and risk analysis.
3Adaptability or versatility
If comprehensive risk assessment is implemented, then defense decision-making support is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex risk assessment system into modular components: data collection modules, feature extraction modules, machine learning classification modules, Bayesian network calculation modules, and visualization modules. Each module performs a specific function and can be independently developed, tested, and maintained. This segmentation reduces overall system complexity while enabling comprehensive risk assessment.
Solution Approach 2:
The patent implements feedback mechanisms where the Bayesian network continuously updates risk assessments based on new data and previous results. The system provides feedback to users about risk levels and recommended actions, and also uses this feedback to refine its models and improve future assessments. This feedback loop enhances decision-making support while managing complexity through iterative improvement rather than monolithic design.
Data Source
AI summary
A cross-site scripting (XSS) risk analysis method based on a Bayesian network and a STRIDE model includes: constructing an XSS attack-related STRIDE threat model of a network information release system; obtaining a network structure of a Bayesian network-based XSS attack risk analysis model based on the STRIDE model; obtaining prior probabilities of all nodes in the network structure of the Bayesian network-based XSS attack risk analysis model based on expert experience and a node ranking algorithm; obtaining a training dataset through simulation by using a rejection sampling algorithm or a direct sampling method; performing network training on the network structure of the Bayesian network-based XSS attack risk analysis model by using the training dataset to obtain the Bayesian network-based XSS attack risk analysis model; and reasoning a Bayesian network to obtain a quantitative analysis result of an XSS attack risk of the network information release system.