A Multilayer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT

Imran Ahmed, Marco Anisetti, Awais Ahmad, Gwanggil Jeon

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

5G is becoming the foundation for the Industrial Internet of Things (IIoT) enabling more effective low-latency integration of artificial intelligence and cloud computing in a framework of a smart and intelligent IIoT ecosystems enhancing the entire industrial procedure. However, it also increases the functional complexities of the underlying control system and introduces new powerful attack vectors leading to severe security and data privacy risks. Malware attacks are starting targeting weak but highly connected IoT devices showing the importance of security and privacy in this scenario. This article designs a 5G-enabled system, consisted in a deep learning based architecture aimed to classify malware attacks on the IIoT. Our methodology is based on an image representation of the malware and a convolutional neural networks that is designed to differentiate various malware attacks. The proposed architecture extracts complementary discriminative features by combining multiple layers achieving 97% of accuracy.

Original languageEnglish
Pages (from-to)1495-1503
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume19
Issue number2
DOIs
Publication statusPublished - 1 Feb 2023
Externally publishedYes

Keywords

  • 5G
  • Industrial Internet of Things (IoT)
  • cybersecurity
  • deep learning
  • malware detection

Fingerprint

Dive into the research topics of 'A Multilayer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT'. Together they form a unique fingerprint.

Cite this