Neural Network Malicious Code Detection System
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Solution Overview
Problem
Current host-based intrusion detection systems (HIDS) are heuristic-driven, slow, and often inaccurate, struggling to cope with the increasing volume of malicious attacks, leading to data breaches and labor-intensive re-classification due to their rule-based methodologies that fail to cover all edge cases efficiently.
Innovation Solution
A deep learning-based system utilizing convolutional neural networks (CNN) and recurrent neural networks (RNN) for pattern detection and memory context, respectively, to classify malicious code with human-expert level precision and speed, eliminating the need for rule sets by learning patterns in code sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based methodologies are used for malicious code detection, then detection coverage can be established for known patterns, but the system becomes slow and inaccurate when facing increasing volume and variety of attacks
Solution Approach 1:
The patent replaces mechanical rule-based systems with neural network-based machine learning systems. Specifically, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to automatically learn detection patterns from training data, eliminating the need for manual rule creation and enabling faster, more accurate detection of both known and novel malicious code patterns.
Solution Approach 2:
The system changes the fundamental parameters of detection by transitioning from fixed rule sets to adaptive neural network models. The neural networks learn optimal detection parameters from training data and can dynamically adjust to new attack patterns, maintaining high accuracy while improving processing speed through parallel computation architectures.
2Adaptability or versatility
If rule sets are expanded to cover more attack varieties, then detection coverage improves, but compute time increases significantly
Solution Approach 1:
The system performs preliminary action by pre-training neural networks on extensive datasets of malicious and benign code patterns before deployment. This allows the model to learn complex attack patterns in advance, enabling rapid detection during actual operation without requiring real-time computation of extensive rule sets.
Solution Approach 2:
The patent uses copying by training the neural network on copies of malicious code patterns from training datasets. The model learns to recognize these patterns through repeated exposure during training, enabling it to quickly identify similar patterns in production without requiring explicit rules for each variant.
3Ease of manufacture
If heuristic-driven detection systems are used, then implementation is straightforward, but precision and recall remain low leading to false positives and data breaches
Solution Approach 1:
The system applies self-service by enabling the detection model to automatically improve through continuous training on new data. The neural network learns optimal detection parameters autonomously from training datasets and can be retrained on new attack patterns without requiring manual rule updates, maintaining high precision while reducing false positives.
Data Source
AI summary
There is provided a neural network system for detection of malicious code, the neural network system comprising: an input receiver configured for receiving input text from one or more code input sources; a convolutional neural network unit including one or more convolutional layers, the convolutional unit configured for receiving the input text and processing the input text through the one or more convolutional layers; a recurrent neural network unit including one or more long short term memory layers, the recurrent neural network unit configured to process the output from the convolutional neural network unit to perform pattern recognition; and a classification unit including one or more classification layers, the classification unit configured to receive output data from the recurrent neural network unit to perform a determination of whether the input text or portions of the input text are malicious code or benign code.