Analysis and modelling of a ML-based NIDS for IoT networks

Karanfilovska, Martina; Kochovska, Teodora; Todorov, Zdravko; Cholakoska, Ana; Jakimovski, Goran; Efnusheva, Danijela

doi:10.1016/j.procs.2022.08.023

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…These components continuously gather data and enable data transmission through the use of numerous connectivity standards and protocols such as Bluetooth, Wi-Fi, and ZigBee. 23 Data exchange takes place in the network layer where the data packets are sent smoothly, as it is transporting data this layer is also called as transport layer. It achieves this by employing a range of communication standards.…”

Section: Network Layer Perception Layer Cloud/serversmentioning

confidence: 99%

“…In general, the physical layer which is the lowest layer consists of different connected sensors. These components continuously gather data and enable data transmission through the use of numerous connectivity standards and protocols such as Bluetooth, Wi‐Fi, and ZigBee 23 . Data exchange takes place in the network layer where the data packets are sent smoothly, as it is transporting data this layer is also called as transport layer.…”

Section: Network Intrusion Detection Systems For Iot Securitymentioning

confidence: 99%

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Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Habeeb,

Babu

2024

Trans Emerging Tel Tech

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Network Intrusion Detection Systems (NIDSs) are important in safeguarding networks from known and unknown attacks. Many research efforts have recently been made to create NIDS systems based on Machine Learning (ML) methods, addressing a significant challenge in designing standard NIDS the lack of standardized feature sets in the dataset. Given the recent development of the Internet of Things (IoT) in wireless communication, our proposed method introduces a novel solution to enhance intrusion detection systems. This proposed solution feature selection is carried out in two stages, coarse and fine selection. In the first stage of the coarse selection process, we conduct correlation analysis to identify relationships within the feature set. The second stage employs fine selection using the Whale Optimization Algorithm (WOA) with Genetic Algorithm hybridization (CFWOAGA). The fitness of each selected feature is assessed using the K‐Nearest Neighbors (KNN) algorithm. In our proposed work we integrate WOA with hybrid GA to extend the search space and avoid local optima problems via crossover and mutation operations. These selected features are critical for detecting any intrusion, we use an ML classifier to identify whether there is an attack or normal in the network and we evaluate the performance of each classifier. We evaluate the performance of our classifier using the BoT‐IoT 2020 standard dataset while limiting the selected features to 32 for reduced computational complexity, these selected 32 features are based upon considerations of system optimization and efficiency, making a balance between computational efficiency and model performance. The experimental findings show better model accuracy compared to the WOA technique and a significant drop in the False Alarm Rate (FAR). In conclusion, our proposed CFWOA method achieved an accuracy of 98.9%, while an updated version with the genetic algorithm demonstrated further improvement at 99.5%. Notably, there was a substantial improvement in FAR with our proposed method.

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Section: Network Layer Perception Layer Cloud/serversmentioning

confidence: 99%

Section: Network Intrusion Detection Systems For Iot Securitymentioning

confidence: 99%

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Habeeb,

Babu

2024

Trans Emerging Tel Tech

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“…They employed various ML-based models, with XGBoost outperforming all others on all datasets according to the numerous performance criteria they used to assess the suggested models. In [25], the authors applied supervised and unsupervised ML over the NF-ToN-IoT-v2 dataset to provide a thorough model of a network IDS (NIDS). It was demonstrated that the technique XGBoost Classifier, which obtained a F-Score of 98.8%, produced the best results when supervised learning was used, as implemented by Azure automated ML (AML).…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks

et al. 2023

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With less human involvement, the Industrial Internet of Things (IIoT) connects billions of heterogeneous and self-organized smart sensors and devices. Recently, IIoT-based technologies are now widely employed to enhance the user experience across numerous application domains. However, heterogeneity in the node source poses security concerns affecting the IIoT system, and due to device vulnerabilities, IIoT has encountered several attacks. Therefore, security features, such as encryption, authorization control, and verification, have been applied in IIoT networks to secure network nodes and devices. However, the requisite machine learning models require some time to detect assaults because of the diverse IIoT network traffic properties. Therefore, this study proposes ensemble models enabled with a feature selection classifier for Intrusion Detection in the IIoT network. The Chi-Square Statistical method was used for feature selection, and various ensemble classifiers, such as eXtreme gradient boosting (XGBoost), Bagging, extra trees (ET), random forest (RF), and AdaBoost can be used for the detection of intrusion applied to the Telemetry data of the TON_IoT datasets. The performance of these models is appraised based on accuracy, recall, precision, F1-score, and confusion matrix. The results indicate that the XGBoost ensemble showed superior performance with the highest accuracy over other models across the datasets in detecting and classifying IIoT attacks.

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Ensuring network security with a robust intrusion detection system using ensemble-based machine learning

Hossain

Islam

2023

Array

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Analysis and modelling of a ML-based NIDS for IoT networks

Cited by 7 publications

References 4 publications

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

Coarse and fine feature selection for Network Intrusion Detection Systems (IDS) in IoT networks

An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks

Ensuring network security with a robust intrusion detection system using ensemble-based machine learning

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