Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Keshav Sood;Shigang Liu;Dinh Duc Nha Nguyen;Neeraj Kumar;Bohao Feng;Shui Yu
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引用次数: 0

Abstract

In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks’ operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.
在物联网(IoT)网络中,物联网传感器收集了维持基于人工智能(AI)的网络运行所需的宝贵原始数据。人工智能模型是数据驱动的,因为它们利用数据做出准确的网络安全、管理和运营决策。遗憾的是,传感器部署在恶劣的环境中,这会影响传感器的行为,最终影响网络的运行。此外,物联网设备通常容易受到一系列恶意事件的影响。因此,物联网传感器的正确运行(包括故障恢复能力)对于持续运行至关重要。当然,时间序列数据的状态变量可能会发生变化,也就是说,在这些情况下产生的数据流可能是不正确、不完整或缺失的,也可能是稀疏的,这对人工智能模型做出可解释的智能管理和控制决策的实时决策能力提出了巨大挑战。本文旨在缓解这一根本问题,正确预测缺失和错误的读数,从而使人工智能模型的决策能力在不正确、缺失和高度不平衡数据集的情况下不会下降。我们采用了一种基于模糊信息分解的新方法来恢复遗漏的数据值。我们使用了三个数据集,初步结果表明,我们的方法能有效恢复遗漏或受损的数据样本,帮助人工智能模型做出准确的决策。最后,我们讨论了这项研究的局限性和未来工作。
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来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
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