Malicious URL Pattern Detection with Modular Features and Autoencoding
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Solution Overview
Problem
Existing systems for identifying malicious URLs lack flexibility and comprehensiveness, as they often rely on single approaches and struggle to adapt to evolving attack strategies, and there is a lack of clarity in extracting and interpreting features effectively.
Innovation Solution
A comprehensive platform utilizing a machine learning system with multiple modules for data collection, feature extraction, and autoencoding techniques to identify and classify malicious URLs, including a queue module, feature selector, vectorizer, model handler, and visualizer, which employs various vectorization and autoencoding methods to analyze URL data with reduced dimensionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple vectorization and autoencoding techniques are employed to improve detection accuracy, then the system becomes more comprehensive and adaptable, but the device complexity increases
Solution Approach 1:
The system segments the URL analysis process into multiple independent modules: queue module for data management, feature selector module for feature extraction, vectorizing module for dimensionality transformation, feature generation module for pattern recognition, model handler module for machine learning model management, and visualizer module for result presentation. Each module implements specific vectorization or autoencoding techniques independently, allowing the system to achieve comprehensive detection accuracy while maintaining modular complexity that is easier to manage and tune.
2Adaptability or versatility
If analytical approaches with multiple techniques are used to detect evolving malicious URL strategies, then the adaptability improves, but the difficulty of extracting and interpreting features increases
Solution Approach 1:
The feature selector module acts as an intermediary between the raw URL data and the analysis engines. It automatically selects and extracts relevant features from URLs using multiple techniques including lexical analysis, statistical methods, and machine learning-based feature selection. This intermediary layer simplifies the feature extraction process by filtering out irrelevant information and presenting only the most discriminative features to subsequent analysis modules, thereby improving adaptability to evolving threats while reducing the apparent complexity of feature interpretation.
Solution Approach 2:
The system dynamically changes parameters during feature extraction and analysis. The feature selector module adjusts feature weights and selection criteria based on the specific URL being analyzed and the detected threat patterns. The model handler module selects and switches between different machine learning models and parameters depending on the analysis context. This dynamic parameter adjustment enables the system to adapt to evolving malicious URL strategies while managing extraction complexity through automated parameter optimization.
Data Source
AI summary
A system for identifying and classifying malicious URLs comprising one or more processors having access to program instructions that when executed generate a queue module configured to receive a file including a potentially malicious URL from a source, a feature selector module configured to select features of interest to identifying URLs extracted from the file, a vectorizing module configured to generate vectorized feature data from the features, a feature generation module configured to generate URL data features with reduced dimensionality from the vectorized data using autoencoding techniques, a model handler module configured to select an artificial intelligence/machine learning (AI/ML) model to analyze the URL data features with reduced dimensionality, to transmit the model for execution, and to receive the results of the execution of the selected AI/ML model, and a visualizer module configured to provide a rendering of results of the execution of the selected AI/ML model.