Federated-LSTM based Network Intrusion Detection Method for Intelligent Connected Vehicles

Yu, Tianqi; Guo-dong, Hua; Wang, Huaisheng; Yang, Jianfeng; Hu, Jianling

doi:10.1109/icc45855.2022.9838655

Cited by 15 publications

(8 citation statements)

References 14 publications

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“…The authors of [56] employed a simulated attack dataset consisting of simulated Sybil attack flows and normal traffic flows in their experimental analysis. Meanwhile, the simulations in [59] were conducted using the authors' proprietary dataset. Although the NSL-KDD and CIC-IDS 2017 datasets are not dedicated to IoVs and are primarily general intrusion detection datasets, the authors of [47,64] conducted their experiments on these datasets to evaluate the performance of their proposed methods.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…• Attacks detected: Within the domain of FL-based IDSs for IoV, numerous research papers have put forth methodologies to identify a diverse range of cyber threats. DoS attacks [47,52,[57][58][59][60]63] and constant attacks [53,55,61] are the most frequently discussed types of attacks in the literature. In addition, some authors emphasized specific attacks, such as the Sybil assault [56] and the black hole attack [54].…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…In addition, some authors emphasized specific attacks, such as the Sybil assault [56] and the black hole attack [54]. Several papers also explored detecting advanced attacks in in-vehicle networks, including adversarial attacks like fuzzy attacks [16,57,58,60,63], flooding attacks [16,62], and spoofing attacks [16,52,[58][59][60]. These studies highlighted the diverse and persistent nature of cyber threats in the IoV environment, underscoring the critical need for robust IDS solutions.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…• Real-time processing: A critical aspect of FL-based IDSs is their ability to process data in real time, ensuring timely detection and response to potential threats. Our review found several papers that proposed IDSs designed for real-time operation [52,[58][59][60].…”

mentioning

confidence: 99%

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Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Alsamiri,

Alsubhi

2023

Future Internet

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In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Analysis and Discussionmentioning

confidence: 99%

Section: Analysis and Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Alsamiri,

Alsubhi

2023

Future Internet

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“…Vehicle-level model testing and actual research, on the other extreme, should be carried out to confirm the practicality of IDSs in real-world automobiles. Yu, t et al [23] focuses on leveraging Federated LSTM networks for intrusion detection in ICVs. The study capitalizes on Federated Learning techniques, distributing LSTM models across ICVs to detect and prevent network intrusions effectively.…”

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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CVAR-FL IoV Intrusion Detection Framework

Zhao,

Rao,

Liu

et al. 2023

Lecture Notes in Computer Science

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Federated-LSTM based Network Intrusion Detection Method for Intelligent Connected Vehicles

Cited by 15 publications

References 14 publications

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions

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

CVAR-FL IoV Intrusion Detection Framework

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