Machine Learning Threat Model for Malicious String Detection
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Solution Overview
Problem
Existing systems face challenges in determining whether short strings represent malicious artifacts, often requiring multiple resource-intensive models and manual coding to identify malicious characteristics.
Innovation Solution
A processor converts input strings into character vectors, applies convolution matrices to generate subscores, and combines them to produce a score for a machine learning threat model, allowing for efficient detection of malicious artifacts without multiple models or manual coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple resource-intensive models are used to analyze strings, then detection accuracy improves, but resource consumption increases
Solution Approach 1:
The patent combines multiple analysis functions into a single machine learning model. The model integrates character-level convolutional neural networks and word-level recurrent neural networks to simultaneously perform multiple types of string analysis, eliminating the need for separate models while maintaining comprehensive detection accuracy.
Solution Approach 2:
The single machine learning model is designed to perform multiple detection functions universally. It can analyze both character patterns and word sequences, detect various types of malicious artifacts including malware URLs and phishing domains, and adapt to different threat types without requiring separate specialized models.
2Measurement precision
If manual coding of malicious indicators is performed, then detection precision improves, but device complexity increases
Solution Approach 1:
The machine learning model automatically learns malicious indicators from training data without requiring manual coding. The system self-trains on labeled examples of malicious and benign strings, automatically extracting relevant features and patterns, thereby eliminating the need for manual indicator development while maintaining high detection precision.
Solution Approach 2:
The system transforms the approach from static manual coding to dynamic parameter learning. The model learns optimal detection parameters and features from data, allowing it to adapt to evolving threats without manual intervention. This includes automatically learning character n-gram weights, word embedding representations, and other critical detection parameters.
3Reliability
If multiple models are used for string analysis, then detection reliability improves, but productivity decreases
Solution Approach 1:
The patent merges multiple detection models into a single integrated architecture. The unified model processes strings through combined character-level and word-level analysis pipelines simultaneously, achieving the reliability of multiple models while processing strings in a single pass rather than sequentially through separate models.
Solution Approach 2:
The single model is segmented into specialized components that work in parallel. Character-level CNNs analyze local patterns while word-level RNNs analyze sequential dependencies, with both segments processing information concurrently within the same model framework, maintaining reliability through diverse analysis perspectives while improving processing efficiency.
Data Source
AI summary
In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second subscore and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.