Anomaly Detection for Industrial Internet of Things Cyberattacks

Abstract

The evolution of the Internet of Things (IoT) has empowered modern industries with the capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things (IIoT). The IIoT is vulnerable to a diverse range of cyberattacks that can be exploited by intruders and cause substantial reputational and financial harm to organizations. To preserve the confidentiality, integrity, and availability of IIoT networks, an anomaly-based intrusion detection system (IDS) can be used to provide secure, reliable, and efficient IIoT ecosystems. In this paper, we propose an anomaly-based IDS for IIoT networks as an effective security solution to efficiently and effectively overcome several IIoT cyberattacks. The proposed anomaly-based IDS is divided into three phases: pre-processing, feature selection, and classification. In the pre-processing phase, data cleaning and normalization are performed. In the feature selection phase, the candidates' feature vectors are computed using two feature reduction techniques, minimum redundancy maximum relevance and neighborhood components analysis. For the final step, the modeling phase, the following classifiers are used to perform the classification: support vector machine, decision tree, k-nearest neighbors, and linear discriminant analysis. The proposed work uses a new data-driven IIoT data set called X-IIoTID. The experimental evaluation demonstrates our proposed model achieved a high accuracy rate of 99.58%, a sensitivity rate of 99.59%, a specificity rate of 99.58%, and a low false positive rate of 0.4%.

Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

“…Compared with Al-Hawawreh et al [25], the proposed model improved the accuracy rate by 0.25%. Our proposed clustering model also outperformed a recently proposed method by Alanazi et al [24], who used the minimum redundancy maximum relevance (MRMR) method with the decision tree algorithm, the proposed method enhanced the accuracy rate by 0.21%. In addition, the proposed model improved accuracy by 7.4% when compared to Ludwig [29], who used the neural network ensemble method.…”

“…Several related works have been proposed for anomaly-based IDS in the IoT/IIoT networks. A study by [24] proposed anomaly-based IDS to overcome cyberattacks in industrial IoT networks. To improve the classifier techniques, the proposed method used two feature selection techniques called minimum redundancy maximum relevance and neighborhood components analysis.…”

“…Al-Hawawreh et al [25] Decision tree 99.54% Alanazi et al [24] Minimum redundancy maximum relevance with decision tree 99.58%…”

Anomaly Detection of Water Pollution Prevention Ecosystem Based on Artificial Intelligence

“…Heavy metals are an important indicator for the detection of pollutants. The main methods for detecting heavy metals in water include electrochemical methods, removal of chemical voltammetry, atomic emission spectrometry, ultraviolet-visible detection, and induction plasma techniques [6]. Electrochemical sensors are used to detect more accurate data related to water contaminants.…”

Ectopic thyroid gland resembling a thyroglossal duct cyst

“…Ectopic location of the thyroid gland is a rare event, with a reported prevalence of 1:100,000 to 1:300,000 in the general population. 1 This anomaly is due to the embryological development of the thyroid gland from the base of the tongue to its normal eutopic location in the lower third of the neck, just anterior to the trachea. 1 Ectopic location in the trachea, thorax, and abdomen has also been reported.…”