CSMC: A Secure and Efficient Visualized Malware Classification Method Inspired by Compressed Sensing.

Area of Science:

• Cybersecurity
• Machine Learning
• Internet of Things (IoT)

Background:

• Intelligent sensors in IoT are vital but vulnerable to sophisticated malware attacks.
• Classifying malware is crucial, yet challenging due to massive sample sizes and limited IoT resources.
• Existing methods struggle with efficient processing, secure sharing, and robust classification in complex networks.

Purpose of the Study:

• To propose an efficient and secure malware classification method for IoT environments.
• To address challenges of limited bandwidth, resource constraints, and sample exploitation risks.
• To enhance malware classification accuracy and robustness against complex network threats.

Main Methods:

• Developed Compressed Sensing Malware Classification (CSMC), integrating compressed sensing and deep learning.
• Implemented malware sample compression before sharing and classification.
• Utilized deep learning for feature extraction during compression, ensuring irreversibility for enhanced security.

Main Results:

• CSMC demonstrated superior performance compared to existing compressed sensing and machine/deep learning methods for Windows and Android malware.
• Experiments confirmed CSMC's effectiveness in secure sample sharing and processing.
• Robustness was validated through sample reconstruction and noise-handling experiments.

Conclusions:

• CSMC offers an efficient, secure, and robust solution for malware classification in IoT.
• The integration of deep learning within compressed sensing enables advanced feature extraction and protection.
• CSMC significantly improves malware identification capabilities for resource-constrained and security-sensitive IoT applications.