Intrusion Detection System for Internet of Things based on a Machine Learning approach

Liang, Chao; Shanmugam, Bharanidharan; Azam, Sami; Jonkman, Mirjam; Boer, Friso De; Narayansamy, Ganthan

doi:10.1109/vitecon.2019.8899448

Cited by 47 publications

(26 citation statements)

References 20 publications

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“…The proposed prediction based on the RaNN achieved a higher performance than other machine learning algorithms such as ANN, SVM, and DT. A new IDS using a DNN algorithm was suggested by Chao et al [43]. The proposed model achieved a high efficiency for detecting transport layer attacks.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

et al. 2021

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In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

et al. 2021

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“…The research summaries above show current progress in the development IDSs for IoT. In the following section, the technological aspects of IDS models for IoT are further explored [34].…”

Section: Previous Research On Ids For Iotmentioning

confidence: 99%

“…The smart, efficient, secure, and scalable (SESS) system [34] will enable the IoT network administrator to monitor IoT devices based on network traffic data. The area that will be monitored is based on criteria defined by the administrator.…”

Section: System Functionsmentioning

confidence: 99%

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Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

et al. 2020

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With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.

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“…The authors show that utilizing the operational data and anomaly-based detection method can accurately detect cyberattacks or malicious events. Van et al [19] and Liang et al [14] also show high intrusion detection accuracy using the machine learning approach through deep learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

SGF-MD: Behavior Rule Specification-Based Distributed Misbehavior Detection of Embedded IoT Devices in a Closed-Loop Smart Greenhouse Farming System

et al. 2020

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Smart farming is rapidly revolutionizing the agricultural sector where embedded Internet of Things (IoT) devices are integrated into the field to maintain or improve the quality of products as well as increase food production. Despite the tremendous benefits, various cybersecurity threats of IoT can also be inherited by the sector. In this paper, we propose a lightweight specification-based distributed detection to identify the misbehavior of heterogeneous embedded IoT nodes efficiently and effectively in a closed-loop smart greenhouse farming system. To expand the monitoring space of a node, we exploited the Kalman-filter algorithm and simple statistical operations to obtain estimates of data. Accordingly, this enables a monitoring node to assess a target node that has distinct physical characteristics and access to natural phenomena. Along with this, we derive the behavior-rules that are specific to the target system and carefully translate these rules into a state machine diagram. Besides, we formally verify the functional correctness of the monitoring processes as well as ensure that the behavior specifications are completely covered by using the model checker tool UPPAAL. Through extensive experimental simulation using Proteus, we verify its applicability to resource-constrained embedded devices, e.g., Arduino-Uno, as well as show high accuracy in detecting misbehaving nodes while having low false alarms.

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Intrusion Detection System for Internet of Things based on a Machine Learning approach

Cited by 47 publications

References 20 publications

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems

SGF-MD: Behavior Rule Specification-Based Distributed Misbehavior Detection of Embedded IoT Devices in a Closed-Loop Smart Greenhouse Farming System

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