Abstract
One of the important features of routing protocol for low-power and lossy networks (RPLs) is objective function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the contrary, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyberattacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.
Highlights
Introduction eInternet of ings (IoT) can be described as the evergrowing global network of smart devices with built-in sensing features and communication interfaces such as local area network (LAN) interfaces, sensors, and global positioning devices (GPS)
Results and Analysis ere were eight simulated experiments conducted to capture results for discussion. e experiments were designed to understand how machine learning (ML) can be used to detect a combination of attacks against OF0 and Minimum rank with hysteresis objective function (MRHOF) based on power consumption and network metrics. e experiments were designed to understand the impact normalisation, sampling, feature selection, and classifier ensemble techniques have on results. e classifier ZeroR was used to determine a performance baseline as a reference to consider when comparing naıve Bayes (NB), support vector machines (SVMs), multilayer perceptron (MLP), and Random Forest (RF) algorithms. e baseline confirmed malicious and benign classifier prediction at 20.59% which is reasonable since there is a single benign behaviour and four attacks
Experiment 1: Preprocessed Dataset—All Attributes and Metrics. e aim of Experiment 1 is to create classifiers for each ML algorithm based on a preprocessed dataset considering all attributes and metrics. is means considering both power and network parameters from our novel dataset
Summary
Relevant academic papers are reviewed and discussed in five groups. IoT methodologies, MRHOF and OF0 attacks, IDS methodologies and feature selection, datasets and ML classifiers, and preprocessor and balancing techniques were identified as the five core topics. Critical analysis of the current literature identified the following key areas of interest to address in this paper: power consumption and network-related metrics, combination of IoT attacks, MRHOF and OF0 vulnerability analysis, feature selection, and the development of a novel dataset based on IoT attacks. As far as we are aware, no one else has successfully employed time series ML classifiers alongside a novel IoT dataset, whilst detecting a combination of attacks against multiple objective function (e.g., OF0 and MRHOF) based on network and power consumption metrics. 3. Research Methodology e aim of this paper is to use ML to detect a combination of attacks against OF0 and MRHOF, as two popular OFs for the RPL protocol, based on power consumption and network metrics using a novel dataset.
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