Lightweight Machine Learning Classifiers of IoT Traffic Flows

Bikmukhamedov, Radion; Nadeev, Adel

doi:10.1109/synchroinfo.2019.8814156

Cited by 20 publications

(7 citation statements)

References 8 publications

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“…Since operating on network gateways requires a lightweight attack detection model, experimented algorithms are simple machine learning algorithms, including linear support vector machine, quadratic support vector machine, K-nearestneighbor, linear discriminant analysis, quadratic discriminant analysis, multilayer perceptron, long short-term memory, autoencoder classifier, and decision tree classifier; their results are presented in Table 6. As shown in the table, the decision tree classifier outperforms other algorithms, and it is considered a lightweight machine learning algorithm [34].…”

Section: Resultsmentioning

confidence: 99%

MidSiot: A Multistage Intrusion Detection System for Internet of Things

Dat-Thinh

Xuan-Ninh

2022

Wireless Communications and Mobile Computing

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Internet of Things (IoT) has been thriving in recent years, playing an important role in a multitude of various domains, including industry 4.0, smart transportation, home automation, and healthcare. As a result, a massive number of IoT devices are deployed to collect data from our surrounding environment and transfer these data to other systems over the Internet. This may lead to cybersecurity threats, such as denial of service attacks, brute-force attacks, and unauthorized accesses. Unfortunately, many IoT devices lack solid security mechanisms and hardware security supports because of their limitations in computational capability. In addition, the heterogeneity of devices in IoT networks causes nontrivial challenges in detecting security threats. In this article, we present a collaborative intrusion detection system (IDS), namely, MidSiot, deployed at both Internet gateways and IoT local gateways. Our proposed IDS consists of three stages: (1) classifying the type of each IoT device in the IoT network; (2) differentiating between benign and malicious network traffic; and (3) identifying the type of attacks targeting IoT devices. The last two stages are handled by the Internet gateways, whereas the first stage is on the local gateway to leverage the computational resources from edge devices. The evaluation results on three popular IDS datasets (IoTID20, CIC-IDS-2017, and BOT-IoT) indicate our proposal could detect seven common cyberattacks targeting IoT devices with an average accuracy of 99.68% and outperforms state-of-the-art IDSs. This demonstrates that MidSiot could be an effective and practical IDS to protect IoT networks.

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Section: Resultsmentioning

confidence: 99%

MidSiot: A Multistage Intrusion Detection System for Internet of Things

Dat-Thinh

Xuan-Ninh

2022

Wireless Communications and Mobile Computing

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“…The general motivation for using a decision tree is to create a training model that can be used to predict the category or value of target variables by learning decision rules inferred from past data (training data). Therefore, the level of understanding of the decision tree algorithm is very easy compared to other classification algorithms [84].…”

Section: Decision Treementioning

confidence: 99%

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Ahmad

Wazirali

Abu-Ain

2022

Sensors

112

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Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

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“…We adopt clustering to obtain homogeneous classes so that the traffic prediction can thereafter be applied with the purpose to plan suitable resource management mechanisms. The adoption of machine learning solutions for traffic analysis in IoT is provided in [21] where multiclass IoT flow classification task with lightweight machine learning models is applied. In this case a DT has been used, showing its potentialities in improving the performance.…”

Section: Related Workmentioning

confidence: 99%

Predicting LoRaWAN Behavior: How Machine Learning Can Help

et al. 2020

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Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases.

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Lightweight Machine Learning Classifiers of IoT Traffic Flows

Cited by 20 publications

References 8 publications

MidSiot: A Multistage Intrusion Detection System for Internet of Things

MidSiot: A Multistage Intrusion Detection System for Internet of Things

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Predicting LoRaWAN Behavior: How Machine Learning Can Help

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