Authors: Ishita Gupta, Sneha Kumari, Priya Jha, Mohona Ghosh

Published on: May 07, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2405.04373

Summary

• What is new: The paper introduces the use of LSTM networks and GANs to improve malware detection, leveraging raw bytestream data for better accuracy.
• Why this is important: Cybersecurity is struggling with rapidly evolving malware, and traditional detection methods are not keeping pace.
• What the research proposes: Utilizing a combination of LSTM networks and GANs for data augmentation and malware detection to enhance accuracy and speed.
• Results: Achieved 98% accuracy in malware detection, demonstrating significant improvement over traditional methods.

Technical Details

Technological frameworks used: Deep Learning, LSTM, GANs

Models used: LSTM networks, GANs

Data used: VirusShare dataset with over one million unique malware samples

Potential Impact

Cybersecurity providers, companies investing in cybersecurity technologies, and businesses relying on malware detection solutions.

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