Neural Network Instruction Sequence Classification
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Solution Overview
Problem
Existing machine learning models struggle to effectively classify instruction sequences, particularly in detecting malicious scripts, due to limitations in recognizing interdependencies between tokens, especially in lengthy sequences.
Innovation Solution
The use of trained neural networks, such as long short-term memory (LSTM) and recursive neural tensor networks, that process instruction sequences to detect interdependencies by retaining information and processing tokens in both forward and reverse orders, along with encoding techniques like one-hot encoding and word2vec to generate vector representations, enabling the classification of instruction sequences as malicious or benign.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning models are used to classify instruction sequences, then the classification process is simple, but the models fail to recognize interdependencies between tokens in lengthy sequences
Solution Approach 1:
The instruction sequence is segmented into discrete tokens that are processed individually by the neural network. Each token is represented as a separate input unit, allowing the model to analyze relationships between specific tokens while maintaining overall sequence context through the recurrent architecture.
Solution Approach 2:
The patent transitions from traditional flat classification to a multi-dimensional approach using LSTM layers that process tokens sequentially across time steps. The hidden state vector adds a temporal dimension, capturing dependencies across the sequence length while maintaining fixed input/output dimensions for classification.
2Measurement precision
If the model processes lengthy instruction sequences to detect interdependencies, then classification accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The LSTM network maintains continuous processing of the instruction sequence through its recurrent structure, where each token is processed sequentially while preserving context from previous tokens. This continuous action allows the model to detect interdependencies across the entire sequence without requiring multiple separate processing passes.
Solution Approach 2:
The hidden state vector acts as a compressed copy of the sequence information processed so far, allowing the model to retain essential contextual information without storing the entire original sequence. This copying mechanism enables efficient processing by summarizing relevant information in a fixed-size vector representation.
3Measurement precision
If neural networks process tokens in both forward and reverse orders, then recognition of interdependencies improves, but device complexity increases
Solution Approach 1:
The patent applies bidirectional processing by running the LSTM network in both forward and reverse directions through the same instruction sequence. This inversion approach allows the model to capture dependencies from both temporal perspectives, where the forward pass captures earlier-to-later relationships and the reverse pass captures later-to-earlier relationships.
Data Source
AI summary
A system is provided for classifying an instruction sequence with a machine learning model. The system may include at least one processor and at least one memory. The memory may include program code that provides operations when executed by the at least one processor. The operations may include: processing an instruction sequence with a trained machine learning model configured to detect one or more interdependencies amongst a plurality of tokens in the instruction sequence and determine a classification for the instruction sequence based on the one or more interdependencies amongst the plurality of tokens; and providing, as an output, the classification of the instruction sequence. Related methods and articles of manufacture, including computer program products, are also provided.