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A Multilayer Deep Learning Approach for Malware Classification in 5G-Enabled IIoT
IEEE Transactions on Industrial Informatics ( IF 12.3 ) Pub Date : 2022-09-09 , DOI: 10.1109/tii.2022.3205366
Imran Ahmed, Marco Anisetti, Awais Ahmad, Gwanggil Jeon

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.

中文翻译:

支持 5G 的 IIoT 中恶意软件分类的多层深度学习方法

5G 正在成为工业物联网 (IIoT) 的基础,在智能 IIoT 生态系统框架中更有效地低延迟集成人工智能和云计算,从而增强整个工业流程。然而,它也增加了底层控制系统的功能复杂性,并引入了新的强大攻击向量,导致严重的安全和数据隐私风险。恶意软件攻击开始针对薄弱但高度连接的 IoT 设备,表明安全和隐私在这种情况下的重要性。本文设计了一个支持 5G 的系统,包含一个基于深度学习的架构,旨在对 IIoT 上的恶意软件攻击进行分类。我们的方法基于恶意软件的图像表示和旨在区分各种恶意软件攻击的卷积神经网络。所提出的架构通过组合多个层来提取互补的判别特征,达到 97% 的准确率。
更新日期:2022-09-09
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