ImageFed: Practical Privacy Preserving Intrusion Detection System for In-Vehicle CAN Bus Protocol

Taslimasa, Hamideh; Dadkhah, Sajjad; Neto, Euclides Carlos Pinto; Xiong, Pulei; Iqbal, Shahrear; Ray, Suprio; Ghorbani, Ali A.

doi:10.1109/bigdatasecurity-hpsc-ids58521.2023.00031

Cited by 5 publications

(14 citation statements)

References 21 publications

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“…The publicly accessible VeReMi dataset was explicitly developed for analyzing mechanisms to detect misbehavior in VANETs. The authors of [16,58,60] employed the Car-Hacking dataset derived from the "Car Hacking: Attack & Defense Challenge" competition held in 2020. Additionally, some papers used simulated datasets, such as [54], where a simulated dataset was employed to evaluate the effectiveness of their proposed approach in vehicle-to-vehicle and ve-hicle-to-infrastructure scenarios.…”

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%

“…IDSs based on FL in IoV not only demonstrate the adaptability and robustness of FL techniques but also illustrate the essential role these techniques play in protecting the future of connected vehicular systems against a wide array of cyberattacks. [47,54,58,60,64], among others. In this mode, clients engage in the process of training their models on a local level without sharing raw data.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…The examined literature suggested a range of aggregation models to improve the effectiveness and precision of distributed systems, particularly in the domain of FL. One of the most common aggregation models used in the reviewed papers is the federated averaging method, where local model updates are averaged to produce a global model [16,53,55,58,60,62]. This approach is simple yet impactful, particularly in situations involving non-identically and independently distributed (non-IID) data [32].…”

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%

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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IoV security and privacy survey: issues, countermeasures, and challenges

Haddaji,

Ayed,

Chaari Fourati

2024

J Supercomput

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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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ImageFed: Practical Privacy Preserving Intrusion Detection System for In-Vehicle CAN Bus Protocol

Cited by 5 publications

References 21 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

IoV security and privacy survey: issues, countermeasures, and challenges

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

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