The problem of anomaly and attack detection in IoT environment is one of the prime challenges in the domain of internet of things that requires an immediate concern. For example, anomalies and attacks in IoT environment such as scan, malicious operation, denial of service, spying, data type probing, wrong setup, malicious control can lead to failure of an IoT system. Datasets generated in an IoT environment usually have missing values. The presence of missing values makes the classifier unsuitable for classification task. This article introduces (a) a novel imputation technique for imputation of missing data values (b) a classifier which is based on feature transformation to perform classification (c) imputation measure for similarity computation between any two instances that can also be used as similarity measure. The performance of proposed classifier is studied by using imputed datasets obtained through applying Kmeans, F‐Kmeans and proposed imputation methods. Experiments are also conducted by applying existing and proposed classifiers on the imputed dataset obtained using proposed imputation technique. For experimental study in this article, we have used an open source dataset named distributed smart space orchestration system publicly available from Kaggle. Experiment results obtained are also validated using Wilcoxon non‐parametric statistical test. It is proved that the performance of proposed approach is better when compared to existing classifiers when the imputation process is performed using F‐Kmeans and K‐Means imputation techniques. It is also observed that accuracies for attack classes scan, malicious operation, denial of service, spying, data type probing, wrong setup are 100% while it is 99% for malicious control attack class when the proposed imputation and classification technique are applied.

中文翻译：

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物联网环境中的插补和异常检测的机器学习方法

物联网环境中的异常和攻击检测问题是物联网领域亟待关注的主要挑战之一。例如，物联网环境中的异常和攻击（例如扫描，恶意操作，拒绝服务，间谍，数据类型探测，错误设置，恶意控制）可能导致物联网系统故障。物联网环境中生成的数据集通常缺少值。缺少值的存在使分类器不适合分类任务。本文介绍（a）一种用于插补缺失数据值的新颖插补技术（b）一种基于特征变换执行分类的分类器（c）用于在任意两个实例之间进行相似性计算的插补度量，也可以用作相似性度量。通过使用应用Kmeans，F-Kmeans和拟议的插补方法获得的估算数据集来研究拟议的分类器的性能。通过将现有分类器和建议分类器应用于使用建议插补技术获得的估算数据集，也可以进行实验。对于本文的实验研究，我们使用了可从Kaggle公开获得的名为分布式智能空间编排系统的开源数据集。使用Wilcoxon非参数统计检验也验证了获得的实验结果。实践证明，当使用F-Kmeans和K-Means插补技术进行插补过程时，与现有分类器相比，该方法的性能更好。还可以看到，攻击类别的准确性包括扫描，恶意操作，拒绝服务，间谍，