MidSiot: A Multistage Intrusion Detection System for Internet of Things

Dat-Thinh, Nguyen; Xuan-Ninh, Ho; Le, Kim-Hung

doi:10.1155/2022/9173291

Cited by 23 publications

(14 citation statements)

References 43 publications

(29 reference statements)

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“…Table 7 and Fig. 3 compares the classification accuracy of the traditional and proposed AI based detection methodologies used for IoT security by using IoT-IDS20 dataset 40 . Typically, the classification accuracy is one of the most prominent measure used to determine the overall intrusion detection rate of IDS framework.…”

Section: Resultsmentioning

confidence: 99%

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models

Rabie,

Selvarajan,

Hasanin

et al. 2024

Sci Rep

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The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters.

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Section: Resultsmentioning

confidence: 99%

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models

Rabie,

Selvarajan,

Hasanin

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Convolutional Neural Networks decision regression for an intrusion detection method as a special framework [5] is used, it detects only Man in the Middle Attack and DoS attack but no other threats. Network flow generator technique and deep packet inspection method [6] is used, both are related only to the IP address and MAC address if someone tries to break or block but it will not send an alert to the administration. trust integrated RPL protocol (TRPL) technique in IDS [7] is used, it successes in detecting black whole attack from the network in the IoT but it went with limitation in storage and computing capabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

Intrusion detection system to advance IoT security environment

Al-Dhuhli,

Al-Mizaini,

Al-Braiki

et al. 2023

International Journal of Information Technology, Research and A

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Nowadays, with the technological improvement, communications with the things become easier. It helps people to live an easier life, live and work smarter as well as take back control of their lives completely. this smart communication is done in an environment that called Internet of Things (IoT) environment. The Internet of Thing is multiple physical objects that communicate using the internet, allowing sending, and receiving of data. Since it’s a data so it's prone to attack in a goal of steal it, change it and so many reasons. In addition, nowadays hackers are everywhere with so many types. So, it needs to protect those data and the devices, if Internet of Things devices doesn't have enough security to protect the system from being compromised, then many threats and attacks will occur. If the administrator does not apply strong security and develop a plan for system and device prevention, the Internet of Things environment will be weak, which will make the system and devices prone to attack. Unauthorized access will be prevented if the login system includes a signature matching system. This research aims to analyze the traffic security and analyze threats and risks using IoT devices from intruders by applying an IDS to the IoT environment. when the attacker will try to enter to the traffic or send any packets to any IoT devices, the intrusion detection system will send an alert to the administrator that there's something wrong need to check, the IDS will detect the attacker's name, type and from which device he tries to enter to the system, it will analyze the traffic system, as well as will prevent the devices and data from threats.

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“…In [8], the proposed system called MidSiot is used on the IoT. It consists of several stages, including identifying and classifying attacks and real network traffic, and achieved an average accuracy of 99.68%.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

A. Kadheem Hammood,

T. Sadiq

2023

ijci

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The rapid growth and development of the Internet of Things (IoT) have had an important impact on various industries, including smart cities, the medical profession, autos, and logistics tracking. However, with the benefits of the IoT come security concerns that are becoming increasingly prevalent. This issue is being addressed by developing intelligent network intrusion detection systems (NIDS) using machine learning (ML) techniques to detect constantly changing network threats and patterns. Ensemble ML represents the recent direction in the ML field. This research proposes a new anomaly-based solution for IoT networks utilizing ensemble ML algorithms, including logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting. The algorithms were tested on three different intrusion detection datasets. The ensemble ML method achieved an accuracy of 98.52% when applied to the UNSW-NB15 dataset, 88.41% on the IoTID20 dataset, and 91.03% on the BoTNeTIoT-L01-v2 dataset.

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MidSiot: A Multistage Intrusion Detection System for Internet of Things

Cited by 23 publications

References 43 publications

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models

A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models

Intrusion detection system to advance IoT security environment

Ensemble Machine Learning Approach for Iot Intrusion Detection Systems

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