M-TADS:一种支持mec的多信任DoS攻击检测系统

Eric Gyamfi, A. Jurcut
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引用次数: 2

摘要

工业物联网(IIoT)在现代工业革命中需要数据收集和处理的各种应用中仍然是一个不可避免的系统。工业物联网负责关键数据的收集和传输到云服务器,以解决依赖生命的问题。但是,这些网络物理设备容易受到选择性转发、泛洪攻击、Sybil攻击等网络攻击。同时,由于其对传输延迟、功耗和计算时间的影响,行为模式表征了此类攻击下工业物联网设备的特征。因此,本文提出了一种多信任安全系统来监控和记录这些参数,例如IIoT设备上的网络字节输入和字节输出、CPU使用情况和能耗。基于机器学习模型,我们创建了一个高效的多信任攻击检测系统(M-TADS)来检测工业物联网中的拒绝服务攻击(DoS)。工业物联网设备具有资源限制,这实际上阻止了它们在同一网络物理设备上完全实现拟议的M-TADS。因此,从IIoT设备捕获的参数被卸载到使用长短期记忆(LSTM)创建的深度神经网络模型中。LSTM驻留在网络边缘的MEC (multi-access edge computing)服务器上,用于判断是否存在DoS攻击签名。由于DoS攻击的高延迟,我们在IIoT设备上引入了自定义保持和检查过滤器。通过仿真验证了所提出的M-TADS的性能,结果证实了在吞吐量、能耗、数据包延迟和IIoT网络DoS攻击检测精度方面的高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
M-TADS: A Multi-Trust DoS Attack Detection System for MEC-enabled Industrial loT
The Industrial Internet of Things (IIoT) remains an inevitable system in various applications that require data collection and processing in the modern industrial revolution. The IIoTs are responsible for critical data collection and transmission to cloud servers to address life-dependent problems. However, these cyber-physical devices are vulnerable to network attacks such as selective forwarding, flooding, and Sybil attacks. Meanwhile, behavioural patterns characterise the IIoT devices under such attacks due to their effect on transmission latency, power consumption, and computational time. Hence, this paper presents a multi-trust security system to monitor and record these parameters, such as network byte-in and byte-out, CPU usage, and energy consumption on the IIoT device. Based on the ML model, we created an efficient multi-trust attack detection system (M-TADS) to detect denial of service attacks (DoS) in the IIoT. IIoT devices have resource constraints that practically prevent them from fully implementing the proposed M-TADS on the same cyber-physical device. Hence, the captured parameters from the IIoT devices are offloaded to a deep neural network model created with long short term memory (LSTM). The LSTM is hosted on a multi-access edge computing (MEC) server at the network edge to determine the possible existence of the DoS attack signature. Due to the high latency accompanying DoS attack, we introduce a custom hold and check filter on the IIoT devices. The proposed M-TADS performance is verified through simulations, and the results confirm high performance in terms of throughput, energy consumption, packet delay, and IIoT network DoS attack detection accuracy.
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