Generative Adversarial Network for Zero-Day Malware Detection
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Solution Overview
Problem
Current malware detection methods, whether supervised or unsupervised, face challenges in accurately identifying zero-day malware and handling concept drift, as they rely on labeled data, are inefficient, and prone to inaccuracies due to the reliance on human expertise and labeling errors.
Innovation Solution
The use of a generative adversarial network (GAN) and autoencoder network model for unsupervised malware detection, employing a plurality of neural networks including encoder networks, a generator network, and a discriminator network to classify input samples as malicious or safe, without the need for labeled data, by training on benign samples and using loss functions to determine the authenticity of input samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning methods are used for malware detection, then detection accuracy can be improved with labeled data, but the system becomes inefficient and prone to labeling errors
Solution Approach 1:
The system uses unsupervised learning where the model automatically learns from unlabeled data without requiring human annotation. The autoencoder and GAN work together to detect anomalies autonomously, eliminating the need for manual labeling while maintaining detection accuracy and improving efficiency.
Solution Approach 2:
The patent replaces the mechanical process of manual data labeling with an automated neural network system. The unsupervised learning model substitutes human experts in the labeling process, using the autoencoder-GAN framework to automatically identify malware patterns without human intervention.
2Adaptability or versatility
If traditional detection methods are used, then the system is simpler to implement, but it cannot effectively detect zero-day malware or handle concept drift
Solution Approach 1:
The system employs dynamic neural network architectures including autoencoders and GANs that can adapt to new malware patterns. The model continuously learns from incoming data, enabling it to detect zero-day malware and handle concept drift through its ability to adjust to changing data distributions without retraining on labeled examples.
Solution Approach 2:
The detection system is divided into multiple specialized components: an autoencoder for feature extraction and reconstruction, a GAN for generating realistic malware samples, and a discriminator for classification. This segmentation allows each component to specialize in specific tasks, improving overall adaptability while managing complexity through modular design.
3Measurement precision
If human expertise is used for labeling malware data, then detection accuracy can be improved, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system eliminates the need for human labeling by using unsupervised learning. The autoencoder and GAN automatically learn malware characteristics from unlabeled data, performing the labeling function self-service without human intervention, thus achieving high accuracy while eliminating time loss associated with manual labeling.
Solution Approach 2:
The patent introduces neural network models as intermediaries between raw malware data and detection decisions. The autoencoder-GAN framework acts as a mediator that automatically extracts features and identifies malware patterns, replacing human experts in the labeling process and eliminating both time loss and human error.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to perform malware detection using a generative adversarial network. An example apparatus includes a first encoder network to encode an input sample into a first encoded sample, the first encoder network implemented using a multilayer perception (MLP) network, a generator network to reconstruct the first encoded sample to generate a reconstructed sample, a discriminator network to, in response to obtaining the first encoded sample and the reconstructed sample, generate a loss function based on the reconstructed sample and the input sample, and an optimization processor to, when the loss function satisfies a threshold loss value, classify the input sample as malicious.