基于机器学习的物联网设备识别中流量表示对准确率下降影响的初步研究

Nik Aqil, Firdaus Afifi, Faiz Zaki, N. B. Anuar
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引用次数: 0

摘要

物联网(IoT)在过去几年中因其快速增长而受到关注。物联网设备,如温度和湿度传感器和语音控制器被广泛应用,从家用电器到工业机器。然而,随着物联网的快速增长和带来的好处,我们面临着各种安全漏洞,例如数据泄露和物联网特定的恶意软件。研究人员正在使用物联网设备识别作为物联网安全问题的解决方案。物联网设备识别帮助网络管理员识别进入其源设备的网络流量。然而,研究人员经常忽视物联网设备识别中的一个重要问题,即随着时间的推移精度会下降。因此,本文探讨了物联网设备识别在不同流量表示方法(流、子流和分组)上精度下降的严重程度。本文利用了一个私有的,和UNSW物联网流量跟踪公共数据集。根据实验结果,基于子流的方法记录了最佳的整体性能,在私有数据集中仅下降8%,在公共数据集中下降1%。同时,尽管基于数据包的方法在私有数据集上只降低了5%,但在公共数据集上却记录了高达11%的准确性下降。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification
The Internet of Things (IoT) has gained attention for its rapid growth in the past few years. IoT devices such as temperature and humidity sensors and voice controllers are implemented widely, from household appliances to industrial machines. However, with the rapid growth and benefits IoT offers, we are exposed to various security vulnerabilities, such as data breaches and IoT-specific malware. Researchers are using IoT device identification as a solution for IoT security issues. IoT device identification helps network administrators identify network traffic into its originating devices. However, researchers often overlook an important issue in IoT device identification, which is accuracy degradation over time. Thus, this paper explores the severity of accuracy degradation in IoT device identification on different traffic representation approaches, which are flow, sub-flow, and packet. This paper utilizes a private, and the UNSW IoT Traffic Traces public dataset. Based on the experimental findings, the sub-flow-based approach recorded the best overall performance, with only 8% degradation in the private dataset and 1% degradation in the public dataset. Meanwhile, even though the packet-based approach only degraded 5% on the private dataset, it recorded up to an 11% accuracy decrease in the public dataset.
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