Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Mehedi, Sk. Tanzir; Anwar, Adnan; Rahman, Zia-ur; Ahmed, Kawsar

doi:10.3390/s21144736

Cited by 72 publications

(38 citation statements)

References 52 publications

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“…Mehedi et al suggested an IDS model based on deep migration learning with IVN, claiming the IDS was equivalent to a number of other current models. Compared to several other current models, its performance is superior 22 . Fernando 23 proposed a class rebalancing strategy based on a class balancing dynamic weighted loss function for the problem of uneven distribution of network attacks, claiming that experiments conducted using this method on highly unbalanced data demonstrated robust generalization, but the method did not include machine learning.…”

Section: Related Workmentioning

confidence: 97%

ID-RDRL: A Deep Reinforcement Learning-based Feature Selection Intrusion Detection Model

Ren

Zeng

Cao

et al. 2022

Preprint

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Network assaults pose significant security concerns to network services; hence, new technical solutions must be used to enhance the efficacy of intrusion detection systems. Existing approaches pay insufficient attention to data preparation and inadequately identify unknown network threats. This paper presents a network intrusion detection model (ID-RDRL) based on RFE feature extraction and reinforcement learning. ID-RDRL filters the optimum subset of features using the RFE feature selection technique, feeds them into a neural network to extract feature information and then trains a classifier using DRL to recognize network intrusions. We utilized CSE-CIC-IDS2018 as a dataset and conducted tests to evaluate the model's performance, which is comprised of a comprehensive collection of actual network traffic. The experimental results demonstrate that the proposed ID-RDRL model can select the optimal subset of features, remove approximately 80 percent of redundant features, and learn the selected features through DRL to enhance the IDS performance for network attack identification. In a complicated network environment, it has promising application potential in IDS.

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Section: Related Workmentioning

confidence: 97%

ID-RDRL: A Deep Reinforcement Learning-based Feature Selection Intrusion Detection Model

Ren

Zeng

Cao

et al. 2022

Preprint

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“…The experiments use two datasets of the public dataset AWID [ 18 ] and they achieve 96% detection accuracy, improving traditional schemes up to 8%. In the same area, but with a different objective, to speed up the training process, Mehedi et al [ 19 ] propose a deep-TL-based ID model to classify normal traffic and attacks. The TL model makes use of two CNNs and the datasets used for the source and target domain are two different subsets of the new-generation labeled dataset for in-vehicle network [ 20 ], which considers three different types of attacks: flooding, fuzzing, and spoofing.…”

Section: Related Workmentioning

confidence: 99%

Transfer-Learning-Based Intrusion Detection Framework in IoT Networks

Rodríguez

Valls

Otero

et al. 2022

Sensors

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Cyberattacks in the Internet of Things (IoT) are growing exponentially, especially zero-day attacks mostly driven by security weaknesses on IoT networks. Traditional intrusion detection systems (IDSs) adopted machine learning (ML), especially deep Learning (DL), to improve the detection of cyberattacks. DL-based IDSs require balanced datasets with large amounts of labeled data; however, there is a lack of such large collections in IoT networks. This paper proposes an efficient intrusion detection framework based on transfer learning (TL), knowledge transfer, and model refinement, for the effective detection of zero-day attacks. The framework is tailored to 5G IoT scenarios with unbalanced and scarce labeled datasets. The TL model is based on convolutional neural networks (CNNs). The framework was evaluated to detect a wide range of zero-day attacks. To this end, three specialized datasets were created. Experimental results show that the proposed TL-based framework achieves high accuracy and low false prediction rate (FPR). The proposed solution has better detection rates for the different families of known and zero-day attacks than any previous DL-based IDS. These results demonstrate that TL is effective in the detection of cyberattacks in IoT environments.

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“…There are special protocols that facilitate the functioning of IVNs. These protocols include the controller area network (CAN), FlexRay, and Ethernet [ 3 ]. CAN is the most common network topology used for controlling the automotive and the industrial system.…”

Section: Introductionmentioning

confidence: 99%

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Aldhyani

Alkahtani

2022

Sensors

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Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security.

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Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Cited by 72 publications

References 52 publications

ID-RDRL: A Deep Reinforcement Learning-based Feature Selection Intrusion Detection Model

ID-RDRL: A Deep Reinforcement Learning-based Feature Selection Intrusion Detection Model

Transfer-Learning-Based Intrusion Detection Framework in IoT Networks

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

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