Malicious URI Classification via ML Feature Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying malicious Uniform Resource Identifiers (URIs) are static and lack flexibility in adapting to new threats, primarily relying on rule-based filtering and blacklists, which do not effectively classify new URIs based on diverse features.
Innovation Solution
A method and system that utilize a machine learning classifier trained on sets of known malicious and non-malicious URIs, extracting textual and metadata information, and content features to classify URIs as malicious or non-malicious, with customizable scoring and alert systems for user notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based filtering and blacklists are used to identify malicious URIs, then implementation simplicity is maintained, but adaptability to new threats deteriorates
Solution Approach 1:
The patent transitions from static rule-based filtering to dynamic machine learning models that continuously learn and adapt to new malicious URI patterns. The system updates its classification algorithms with new data, enabling it to dynamically respond to evolving threats while maintaining automated operation.
Solution Approach 2:
The patent changes the fundamental parameters of URI analysis by moving from fixed rules to probabilistic scoring based on multiple features. Instead of binary match/no-match decisions, the system evaluates numerous characteristics (textual, metadata, content features) and assigns risk scores, allowing flexible adaptation to diverse threat types.
2Measurement precision
If machine learning classifiers with multiple features are used to classify URIs, then classification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the URI analysis process into distinct feature extraction categories (textual features, metadata features, content features). Each category is processed separately through dedicated extraction modules, which then feed into the classification system. This segmentation manages complexity by organizing the analysis into manageable, modular components.
Solution Approach 2:
The patent introduces an intermediary layer of feature extraction that transforms raw URI data into structured feature vectors before classification. This intermediary processing stage organizes complex information into standardized formats, making the subsequent classification task more manageable and interpretable while maintaining high accuracy.
3Adaptability or versatility
If diverse feature extraction is performed on URIs, then classification capability improves, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction and categorization before the actual classification decision. By pre-processing URIs to extract and organize textual, metadata, and content features in advance, the system prepares data structures that enable faster classification execution. This preliminary action separates data preparation from decision-making, optimizing overall processing efficiency.
Data Source
AI summary
Methods and systems for identifying malicious URIs. The system accepts a list of URIs as input and extracts features related to a given URI and uses the features to discover patterns that indicate malicious content. Once trained, the classifier can then classify new inputs as either malicious or non-malicious.