The Impact of Different Feature Scaling Methods on Intrusion Detection for in-Vehicle Controller Area Network (CAN)

Lokman, Siti-Farhana; Othman, Abu Talib; Bakar, Muhamad Husaini Abu; Musa, Shahrulniza

doi:10.1007/978-981-15-2693-0_14

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…MMS, defined by Equation 1 is known to be the most commonly used normalization technique, but its drawback is hinged on its inability to handle outliers completely; thus, it may lead to some values being extremely small. For this reason, we adopted the QT method proposed in [44].…”

Section: B Database Description Transformation and Image Generationmentioning

confidence: 99%

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

et al. 2023

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Data centralization can potentially increase Internet of Things (IoT) usage. The trend is to move IoT devices to a centralized server with higher memory capacity and a more robust management interface. Hence, a larger volume of data will be transmitted, resulting in more network security issues. Cloud IoT offers more advantages for deploying and managing IoT systems through minimizing response delays, optimal latency, and effective network load distribution. As a result, sophisticated network attack strategies are deployed to leverage the vulnerabilities in the extensive network space and exploit user information. Several attempts have been made to provide network intrusion detection systems (IDS) to the cloud IoT interface using machine learning and deep learning approaches on dedicated IDS datasets. This paper proposes a transfer learning IDS based on the Convolutional Neural Network (CNN) architecture that has shown excellent results on image classification. We use five pre-trained CNN models, including VGG16, VGG19, Inception, MobileNet, and EfficientNets, to train on two selected datasets: CIC-IDS2017 and CSE-CICIDS2018. Before the training, we carry out preprocessing, imbalance treatment, dimensionality reduction, and conversion of the feature vector into images suitable for the CNN architecture using Quantile Transformer. Three best-performing models (InceptionV3, MobileNetV3Small, and EfficientNetV2B0) are selected to develop an ensemble model called efficient-lightweight ensemble transfer learning (ELETL-IDS) using the model averaging approach. On evaluation, the findings show that the ELETL-IDS outperformed existing state-of-the-art proposals in all evaluation metrics, reaching 100% in accuracy, precision, recall, and F-score. We use Matthew's Correlation Coefficient (MCC) to validate this result and compared it to the AUC-ROC, which maintained an exact value of 0.9996. To this end, our proposed model is lightweight, efficient, and reliable enough to be deployed in cloud IoT systems for intrusion detection.

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Section: B Database Description Transformation and Image Generationmentioning

confidence: 99%

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

et al. 2023

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“…Among the normalization techniques, minmax and quantile normalization are the two commonly used methods that can convert data values to the same range. As min-max normalization does not handle outliers well and may cause most data samples to have extremely small values, quantile normalization is used in the proposed framework [15]. The quantile normalization method transforms the feature distribution to a normal distribution and re-calculates all the features values based on the normal distribution.…”

Section: B Data Description and Transformationmentioning

confidence: 99%

“…The quantile normalization method transforms the feature distribution to a normal distribution and re-calculates all the features values based on the normal distribution. Therefore, the majority of variable values are close to the median values, which is effective in handling outliers [15].…”

Section: B Data Description and Transformationmentioning

confidence: 99%

A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

Yang¹,

Shami²

2022

Preprint

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Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

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“…Results on all datasets revealed that when the z-score technique is applied, the ANN produces the best performance. Also (Lokman et al, 2019), have shown the effect of different normalization techniques on the accuracy performance of anomaly detection in cyberattacks.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Preparation in Machine Learning for Condition-based Maintenance

Masmoudi¹,

Jaoua²,

Jaoua³

et al. 2021

Journal of Computer Science

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Using Machine Learning (ML) prediction to achieve a successful, cost-effective, Condition-Based Maintenance (CBM) strategy has become very attractive in the context of Industry 4.0. In other fields, it is well known that in order to benefit from the prediction capability of ML algorithms, the data preparation phase must be well conducted. Thus, the objective of this paper is to investigate the effect of data preparation on the ML prediction accuracy of Gas Turbines (GTs) performance decay. First a data cleaning technique for robust Linear Regression imputation is proposed based on the Mixed Integer Linear Programming. Then, experiments are conducted to compare the effect of commonly used data cleaning, normalization and reduction techniques on the ML prediction accuracy. Results revealed that the best prediction accuracy of GTs decay, found with the k-Nearest Neighbors ML algorithm, considerately deteriorate when changing the data preparation steps and/or techniques. This study has shown that, for effective CBM application in industry, there is a need to develop a systematic methodology for design and selection of adequate data preparation steps and techniques with the proposed ML algorithms.

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The Impact of Different Feature Scaling Methods on Intrusion Detection for in-Vehicle Controller Area Network (CAN)

Cited by 10 publications

References 19 publications

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

Transfer Learning Approach to IDS on Cloud IoT Devices Using Optimized CNN

A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

Data Preparation in Machine Learning for Condition-based Maintenance

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