A machine learning approach for imputation and anomaly detection in IoT environment. Issue 5 (13th April 2020)

• Record Type: Journal Article
• Authors: Vangipuram, Radhakrishna; Gunupudi, Rajesh Kumar; Puligadda, Veereswara Kumar; Vinjamuri, Janaki
• Other Names: Chang Victor guestEditor.; Aljawarneh Shadi A. guestEditor.; Li Chung‐Sheng guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-04-13
• Subjects: anomaly detection -- dimensionality reduction -- imputation -- IoT -- missing value -- similarity; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12556
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: 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.