Scalable machine learning-based intrusion detection system for IoT-enabled smart cities

Rahman, Arafatur; Asyhari, A. Taufiq; Leong, Lai-Ying; Satrya, Gandeva Bayu; Tao, Mengyao; Zolkipli, Mohamed Fadli Bin

doi:10.1016/j.scs.2020.102324

Cited by 148 publications

(49 citation statements)

References 39 publications

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“…One of the well-known intrusion detection methods that has gained attention recently in the literature is using machine learning techniques [ 46 , 47 , 48 , 49 ]. As a result, the standard intrusion detection techniques show improved quality by incorporating the machine learning techniques with them [ 50 , 51 , 52 , 53 ]. Machine learning techniques can be used to improve intrusion detection methods for traditional networks effectively.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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The security of IoT networks is an important concern to researchers and business owners, which is taken into careful consideration due to its direct impact on the availability of the services offered by IoT devices and the privacy of the users connected with the network. An intrusion detection system ensures the security of the network and detects malicious activities attacking the network. In this study, a deep multi-layer classification approach for intrusion detection is proposed combining two stages of detection of the existence of an intrusion and the type of intrusion, along with an oversampling technique to ensure better quality of the classification results. Extensive experiments are made for different settings of the first stage and the second stage in addition to two different strategies for the oversampling technique. The experiments show that the best settings of the proposed approach include oversampling by the intrusion type identification label (ITI), 150 neurons for the Single-hidden Layer Feed-forward Neural Network (SLFN), and 2 layers and 150 neurons for LSTM. The results are compared to well-known classification techniques, which shows that the proposed technique outperforms the others in terms of the G-mean having the value of 78% compared to 75% for KNN and less than 50% for the other techniques.

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Section: Related Workmentioning

confidence: 99%

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Qaddoura

Al-Zoubi

Faris

et al. 2021

Sensors

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“…Qureshi et al [77] proposed a new and secure framework to detect the presence of smart city security threats in IoT and IIoT networks. Rahman et al [104] designed an intrusion detection system (IDS) for sustainable resource management and the network infrastructure protection of smart cities amid the expansion of IoT. Andrade et al [105] proposed and verified a model for evaluating the IoT cybersecurity maturity of smart cities in an IoT environment.…”

Section: Transportationmentioning

confidence: 99%

Research Models and Methodologies on the Smart City: A Systematic Literature Review

2022

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A smart city is a sustainable city that solves urban problems and improves citizens’ quality of life through the fourth industrial revolution technology and governance between stakeholders. With the advent of the fourth industrial revolution and the concept of smart cities changing, many smart city studies have been conducted. Still, studies on the overall flow of smart city research and major issues are insufficient. Therefore, this study analyzed the trends and characteristics of smart city research and proposes research directions through smart city literature analyses conducted over the past 10 years, from 2011 to 2020. This study conducted a systematic review of studies related to smart cities over the past 10 years. Smart city research was analyzed by dividing it into research methods and contents. In terms of research method, year, research methodology 1, research methodology 2, research purpose, data collection method, and use of research results were included in the analysis frame. In terms of research content, keywords related to research topics, analysis units, and the general classification criteria and security of smart cities were included. The research results mentioned in this study are expected to serve as useful guidelines for future smart city development projects.

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“…In this, knowledge-based and anomaly-based IDS were integrated. Different IDS architectures are developed [39] for IoT devices. A semi-distributed technique results in an accuracy of 99.97% and long CPU time of 186.26 s. However, in a distributed approach, the accuracy obtained is only 97.8% but CPU time is relatively low of about 73.52 s. The constraint of the work is updated datasets on IoT are not available.…”

Section: Vulnerability Mitigation Approaches and Security Testbedsmentioning

confidence: 99%

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

et al. 2021

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In recent years, different variants of the botnet are targeting government, private organizations and there is a crucial need to develop a robust framework for securing the IoT (Internet of Things) network. In this paper, a Hadoop based framework is proposed to identify the malicious IoT traffic using a modified Tomek-link under-sampling integrated with automated Hyper-parameter tuning of machine learning classifiers. The novelty of this paper is to utilize a big data platform for benchmark IoT datasets to minimize computational time. The IoT benchmark datasets are loaded in the Hadoop Distributed File System (HDFS) environment. Three machine learning approaches namely naive Bayes (NB), K-nearest neighbor (KNN), and support vector machine (SVM) are used for categorizing IoT traffic. Artificial immune network optimization is deployed during cross-validation to obtain the best classifier parameters. Experimental analysis is performed on the Hadoop platform. The average accuracy of 99% and 90% is obtained for BoT_IoT and ToN_IoT datasets. The accuracy difference in ToN-IoT dataset is due to the huge number of data samples captured at the edge layer and fog layer. However, in BoT-IoT dataset only 5% of the training and test samples from the complete dataset are considered for experimental analysis as released by the dataset developers. The overall accuracy is improved by 19% in comparison with state-of-the-art techniques. The computational times for the huge datasets are reduced by 3–4 hours through Map Reduce in HDFS.

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Scalable machine learning-based intrusion detection system for IoT-enabled smart cities

Cited by 148 publications

References 39 publications

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

A Multi-Layer Classification Approach for Intrusion Detection in IoT Networks Based on Deep Learning

Research Models and Methodologies on the Smart City: A Systematic Literature Review

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

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