LSTM Neural Network for Instruction Sequence Classification
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Solution Overview
Problem
Existing machine learning models struggle to effectively classify instruction sequences, particularly in recognizing interdependencies between tokens, due to limited memory and structural complexity, which can lead to misclassification of malicious scripts.
Innovation Solution
The implementation of a long short-term memory (LSTM) neural network and recursive neural tensor network (RNTN) to process and classify instruction sequences, utilizing techniques like one-hot encoding and word2vec for vector representation, and feedback architecture to retain information and detect interdependencies, both sequentially and structurally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used to classify instruction sequences, then the classification process is simple and fast, but the models lack sufficient memory to learn interdependencies among lengthy sequences of tokens
Solution Approach 1:
The patent introduces an embedding layer as an intermediary component that transforms discrete tokens into continuous vector representations. This embedding layer serves as a bridge between the input instruction sequences and the LSTM network, enabling the model to capture semantic relationships and interdependencies among tokens while maintaining computational efficiency.
Solution Approach 2:
The patent transforms the one-dimensional sequence of tokens into multi-dimensional vector representations through the embedding layer and LSTM architecture. This dimensional transformation allows the model to capture complex interdependencies and contextual relationships that cannot be represented in the original token sequence space.
2Measurement precision
If the model processes lengthy instruction sequences to capture interdependencies, then classification accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The LSTM network maintains continuous processing of token sequences through its recurrent architecture, where each token is processed in sequence while preserving information from previous tokens. This continuous processing enables the model to capture long-range dependencies without requiring repeated passes or breaking the sequence into chunks.
Solution Approach 2:
The embedding layer performs preliminary transformation of tokens into meaningful vector representations before the main classification process. This preliminary action condenses semantic information into compact vectors, reducing the computational burden during subsequent LSTM processing and classification stages.
3Adaptability or versatility
If the model structure is increased to handle structural complexity, then the ability to classify complex scripts improves, but the device complexity and training difficulty increase
Solution Approach 1:
The patent segments the classification task into distinct functional components: an embedding layer for token representation, an LSTM network for sequence processing and interdependency detection, and a classification layer for final prediction. This segmentation allows each component to be optimized independently while maintaining overall system effectiveness.
Solution Approach 2:
The LSTM network serves multiple functions simultaneously: it processes sequential tokens, captures interdependencies among tokens, maintains contextual information, and prepares features for classification. This multi-functionality reduces the need for separate specialized components, balancing versatility with architectural simplicity.
Data Source
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AI summary
Systems are provided to classify an instruction sequence with a machine learning model. An instruction sequence is processed with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and to determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens. The classification of the instruction sequence can then be provided as an output. Related methods and articles of manufacture, including computer program products, are also provided.