A machine learning approach for imputation and anomaly detection in IoT environment

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.

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

“…The proposal divides WSN nodes into different clusters, with each cluster head using the MAS method to separate legitimate traffic from phishing scams. The authors in [ 20 , 46 ] improved the k-means clustering scheme for finding DDoS and misdirection attacks. To detect attacks in a home WSN, the authors of [ 47 ] used user-behaviors learning analysis.…”

IoT-Based Small Scale Anomaly Detection Using Dixon’s Q Test for e-Health Data

“…We can express the formulation based on the three observations such as i − 1, n − j − i − 1, and j − 1 which are below x j and within the range from x i to x n−j and from x n−j to x n , respectively. L and M are two variables, as shown in Equations ( 9) and (10).…”

“…With existing deep learning and machine learning algorithms, one can find outliers from a dataset [7][8][9]. However, due to lack of hardware resources such as processor and memory, an IoT device may face severe difficulty [10]. Further, depicting a point anomaly from a very small-size dataset makes the whole process questionable [11,12].…”

A Novel, Efficient, and Secure Anomaly Detection Technique Using DWU‐ODBN for IoT‐Enabled Multimedia Communication Systems

“…Anomalies or outliers are uncommon, but they can still be important in the event of a credit card transaction; for example, the aberrant conduct of credit card transactions may suggest that a credit card is stolen, whereas strange network traffic patterns can indicate that illegal network access is available [14]. According to the 2019 threat report SonicWALL, in 2018 in Gatlan (2019), more than 327 million threats from the Internet of Things (IoT) were detected globally [15]. As a consequence, assaults on and detection of abnormalities in IoT infrastructure in the IoT industry become an increasing cause of concern [16].…”