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Data augmentation based malware detection using convolutional neural networks.

Ferhat Ozgur Catak¹, Javed Ahmed², Kevser Sahinbas³

¹Simula Research Laboratory, Fornebu, Norway.

Peerj. Computer Science

|April 5, 2021

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This study introduces an image augmentation enhanced deep convolutional neural network (CNN) model for detecting metamorphic malware families. The novel approach achieves up to 98% accuracy in identifying sophisticated cyber threats.

Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

Metamorphic malware poses a significant threat due to its ability to evade traditional signature-based detection methods by altering its code.
Ransomware attacks like WannaCry and Petya highlight the severe financial and operational damages caused by advanced cyber threats targeting critical infrastructure.
Existing antivirus solutions struggle to detect polymorphic and metamorphic malware because each variant exhibits unique characteristics.

Purpose of the Study:

To develop and evaluate deep convolutional neural network (CNN) models enhanced with image augmentation techniques for detecting malware families in a metamorphic environment.
To address the challenge of identifying malware variants that continuously change their structure and hash representations.
To improve the accuracy and robustness of malware detection systems against sophisticated, evolving cyber threats.

Keywords:

Convolutional neural networks Cybersecurity Image augmentation Malware analysis

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

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Published on: May 10, 2024

Main Methods:

Conversion of malware samples into 3-channel image representations using a windowing technique.
Application of data augmentation methods to generate diverse image variants, simulating malware mutations.
Training and evaluation of five different deep CNN models for classifying malware families based on generated and augmented images.

Main Results:

The proposed image augmentation enhanced CNN models demonstrated high performance in malware family detection.
Classification accuracy reached up to 98%, indicating a significant improvement over traditional methods for metamorphic malware.
The study successfully validated the effectiveness of converting malware into image formats and using augmentation for detection.

Conclusions:

Image augmentation combined with deep CNN models offers a promising solution for detecting metamorphic malware.
The developed approach effectively overcomes the camouflage techniques employed by attackers, enhancing cybersecurity defenses.
This research contributes a novel methodology for identifying and classifying evolving malware threats with high accuracy.