Intrusion-Based Attack Detection Using Machine Learning Techniques for Connected Autonomous Vehicle

Bhavsar, Mansi; Roy, Kaushik; Liu, Zhipeng; Kelly, John; Gokaraju, Balakrishna

doi:10.1007/978-3-031-08530-7_43

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…This paper aims to bridge this gap by proposing a Federated-LSTM approach tailored for network security in Intelligent Connected Vehicles. Bhavsar, M et al [24] delves into utilizing machine learning techniques for detecting intrusion-based attacks in CAVs. The specific focus on intrusion-based attacks targeting CAVs remains a relatively less-explored area in the literature.…”

Section: Related Workmentioning

confidence: 99%

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

Mahendran,

Rajendran,

Pandian

et al. 2024

IEEE Access

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Today's Internet of Vehicles (IoV) has soared by leveraging data gathered from transportation systems, yet it grapples with security concerns stemming from network vulnerabilities, exposing it to cyber threats. This study proposes an innovative method to anticipate anomalies and exploit IoV services related to road traffic. Using the Unceasement Conditional Random Field Dynamic Bayesian Network Model (U-CRF-DDBN), this approach predicts the impact of network attacks, strategically managing vulnerable nodes and attackers. Through experimentation and comparisons with existing methods, our model demonstrates its effectiveness in mitigating IoV vulnerabilities. The U-CRF-DDBN strikes a superior balance, outperforming other approaches in intrusion detection for Internet of Vehicles systems. Evaluating its performance on the NSL-KDD dataset reveals a promising average Detection Rate of 93.512% and a low False Acceptance Rate of 0.125% for known attacks, highlighting its robustness. However, with unknown attacks, while the Detection Rate remains at 74.157%, there is an increased FAR of 16.47%, resulting in a slightly lower F1score of 0.822.

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Section: Related Workmentioning

confidence: 99%

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

Mahendran,

Rajendran,

Pandian

et al. 2024

IEEE Access

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“…Thakkar et al [39] conducted a comprehensive survey focusing on machine learning and deep learning methods employed in intrusion detection systems for the Internet of Things (IoT). In one of our previous papers [40], we focused on the importance of IDS in CAVs. Using benchmarking datasets, we build the IDS using 5 different Machine learning (ML) techniques.…”

Section: Table 1 Cav Security Attacksmentioning

confidence: 99%

FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT

Bhavsar,

Bekele,

Roy

et al. 2024

IEEE Access

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A federated learning-based intrusion detection system (FL-IDS) is introduced in this paper to enhance the security of vehicular networks in the context of IoT edge device implementations. The FL-IDS system protects data privacy by using local learning, where devices share only model updates with an aggregation server. This server then generates an enhanced detection model. The FL-IDS system also incorporates machine learning (ML) and deep learning (DL) classifiers, namely logistic regression (LR) and convolutional neural networks (CNN), to prevent attacks in transportation IoT environments. The performance of the proposed IDS was evaluated using two different datasets, NSL-KDD and Car-Hacking. The model evaluation has been evaluated based on the accuracy and loss parameters. The results show that the FL-IDS system outperforms traditional centralized machine learning and deep learning approaches regarding accuracy and privacy protection.

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“…It has been recommended that NSL-KDD (Bhavsar et al, 2022;Louk and Tama, 2023) be used to address some of the inherent issues with the KDD'99 data set. Due to the dearth of publicly available data sets for network-based IDS (Bhavsar et al, 2022), even though this updated version of the KDD data set still has some of the issues raised by McHugh and may not be a perfect representation of real-world networks, we still think it can be used as a useful benchmark data set to aid researchers in comparing various IDS approaches. Similar to KDDcup99, NSL-training KDD's set consists of about 1,074,992 single linkage vectors, each of which has 41 characteristics.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The creation of a new data set known as the NSL-KDD data set was prompted by the inherent issues with the KDD (Bhavsar et al, 2022) data set. Many issues, such as duplicate instances, have been resolved with this fresh data collection.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The enormous amount of duplicative records in the KDD data set, which causes the learning algorithms to be biassed towards the frequent records and prevents them from learning irregular records that are typically more damaging to channels, such as U2R and R2L attacks (Bhavsar et al, 2022), is one of the data set's most significant flaws (Louk and Tama, 2023). Furthermore, the inclusion of these repeated records in the test set will bias the assessment findings in favour of approaches with higher rates of frequent record identification.…”

Section: Dataset Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Fog Computing Surveillance System for Crime and Vulnerability Identification and Tracing

Rawat¹,

Chakrawarti²,

Vyas

et al. 2023

International Journal of Information Security and Privacy

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IoT devices generate enormous amounts of data, which deep learning algorithms can learn from more effectively than shallow learning algorithms. The approach for threat detection may ultimately benefit fog computing or fog networking (fogging). The authors present a cutting-edge distributed DL method for detecting cyberattacks and vulnerability injection (CAVID) in this paper. In terms of the evaluation metrics tested in the tests, the DL model performs better than the SL models. They demonstrated a distributed DL-driven fog computing CAVID approach using the open-source NSL-KDD dataset. A pre-trained SAE was utilised for feature engineering, whereas Softmax was employed for categorization. They used parametric evaluation for system assessment to evaluate the model in comparison to SL techniques. For scalability, accuracy across several worker nodes was taken into consideration. In addition to the robustness, effectiveness, and optimization of distributed parallel learning among fog nodes for enhancing accuracy, the findings demonstrate DL models exceeding classic ML architectures.

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Intrusion-Based Attack Detection Using Machine Learning Techniques for Connected Autonomous Vehicle

Cited by 6 publications

References 11 publications

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

A Novel Constructive Unceasement Conditional Random Field and Dynamic Bayesian Network Model for Attack Prediction on Internet of Vehicle

FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT

Intelligent Fog Computing Surveillance System for Crime and Vulnerability Identification and Tracing

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