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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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Transfer Learning for Image-Based Malware Detection for IoT.

Pratyush Panda¹, Om Kumar C U¹, Suguna Marappan¹

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

This summary is machine-generated.

A novel stacked ensemble model, SE-AGM, effectively detects malware using lightweight neural networks and essential features. This approach achieves high accuracy, outperforming existing methods for improved cybersecurity.

Keywords:

CNN GRU MLP MalImg autoencoder ensemble malware detection transfer learning

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Area of Science:

Cybersecurity and Machine Learning
Deep Learning for Malware Detection

Background:

The proliferation of online activity and the Internet of Things (IoT) has led to a significant rise in cyberattacks, with malware affecting numerous households.
Current malware detection methods often rely on deep learning with visualization, which can be resource-intensive and prone to overfitting with large datasets.
Existing deep learning models face challenges in generalizing effectively without overfitting, especially with complex architectures and large datasets.

Purpose of the Study:

To propose a novel ensemble model, Stacked Ensemble-autoencoder, GRU, and MLP (SE-AGM), for efficient and accurate malware classification.
To investigate the suitability of the Gated Recurrent Unit (GRU) model for malware detection, a less commonly used approach in this domain.
To reduce the time and resource consumption in malware detection by utilizing a concise set of essential features.

Main Methods:

Developed a novel stacked ensemble model (SE-AGM) combining autoencoder, GRU, and MLP lightweight neural networks.
Extracted 25 essential encoded features from the MalImg dataset using a CNN-based transfer learning model trained from scratch.
Employed data augmentation techniques to enhance the classification of grayscale malware images.

Main Results:

The proposed SE-AGM model achieved an average accuracy of 99.43% on the benchmark MalImg dataset.
The SE-AGM model demonstrated superior performance compared to existing malware detection approaches.
The stacked ensemble method, where intermediate model outputs feed into subsequent models, effectively refined features.

Conclusions:

The SE-AGM model offers a highly accurate and efficient solution for malware classification.
The study validates the effectiveness of lightweight neural networks and feature engineering in cybersecurity.
The proposed method provides a competitive alternative to existing, more resource-intensive malware detection techniques.