Classification Approach for Intrusion Detection in Vehicle Systems

Alshammari, Abdulaziz; Zohdy, Mohamed; Debnath, Debatosh; Corser, George

doi:10.4236/wet.2018.94007

Cited by 80 publications

(30 citation statements)

References 41 publications

(34 reference statements)

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“…The LR, DT, K-NN and LR, recorded lower accuracy of 0.342, 0.969 and 0.568, respectively. Equally, our are BFF-IDS outperforms other models proposed in [13,[34][35][36] which recorded average accuracies of 0.928, 0.970, (0.974, 0.965) and 0.980, respectively. Although only [34] used the same set of data as us, others' datasets were equally collected and created from the same source.…”

Section: ) the Federated Forest Model Evaluation Against Other Modelsmentioning

confidence: 70%

A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System

et al. 2021

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In the modern transportation system, In-vehicle communication systems are managed by controllers know as controller area networks (CAN). The CAN facilitates the interaction of 20 to 100 Electronic Control Units (ECU) which coordinate, monitor and control loads of internal vehicle components such as engine system, brake system and telematics system through the exchange of information among them. CAN operates by broadcasting packets to its bus. This means that all nodes and ECUs attached to the bus can receive the packets, without an authentication mechanism for identifying the legitimacy/source of packets. This makes it vulnerable to attacks. An Intrusion Detection System (IDS) can be used to detect attacks on CAN. Machine learning for the IDS, in particular, would be useful for creating models to detect non-linear attack patterns. However, car manufacturers and owners are might not be willing to just share the sensitive information required for training the models. In this paper, we propose a Blockchain-based Federated Forest Software-Defined Networking (SDN)-enabled Intrusion detection system (BFF-IDS) for an In-vehicle network to address the problem of sharing the sensitive CAN data. Due to the limited scalability of blockchain, InterPlanetary File System (IPFS) was used to host the models, while a hash of the model and a pointer to its location was stored and shared via the blockchain. The SDN provides the dynamic routing of packets and model exchanges from IPFS through the blockchain. In the detection model system, a Federated Learning (FL) method creates a radom forest model in a distributed manner by aggregating partially trained models that were trained by individuals with their data kept confidential during the process. Using Fourier transform, we decomposed the CAN IDs cycle from CAN bus traffic in the frequency domain for better generalization in multiclass detection of attacks. Multiple statistical and entropy features were extracted to handle the high complexity and non-linearity in CAN bus traffic. With this proposed system, manufacturers and car owners may be willing to contribute to the training of the models, as their sensitive data is protected due to the use of FL. By storing hashes of the models on a blockchain, the risk of adversaries poisoning the models is reduced and a single point of failure is avoided. The evaluation was conducted by performing experiments in a testbed. We found that the proposed system has efficient use of memory and CPU resources, and that the detection rate of closely related attacks was high.

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Section: ) the Federated Forest Model Evaluation Against Other Modelsmentioning

confidence: 70%

A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System

et al. 2021

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“…In the existing automotive applications, the TML and DL-based IDSs have obvious advantages in detecting various malicious messages [ 18 , 19 , 20 ]. Bozdal et al [ 21 ] and Lokman et al [ 22 ] reviewed the security threats and challenges of the automotive CAN bus system, and discuss some potential security solutions.…”

Section: Background and Related Workmentioning

confidence: 99%

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Mehedi

Anwar

Rahman

et al. 2021

Sensors

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The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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“…• Fuzzy attack: The attacker in this type of attack sends messages by befooling the identifiers in any order using a constant data hampering the functioning of the system. The attacker needs to study a vehicle for a long time to observe the identifiers behavior in order to change its pattern [18].…”

Section: Security Threats and Attacks In Iov Environmentmentioning

confidence: 99%

“…They also explained bit optimization and link-NFA. Alshammari et al [18] explained an IDS based on controller area network protocol which provides a bus for the messages to flow without revealing the sender and receiver information. Various factors like rate of flow of messages over the bus can detect an attack.…”

Section: B Authenticationmentioning

confidence: 99%

Authentication Protocols in Internet of Vehicles: Taxonomy, Analysis, and Challenges

et al. 2020

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Internet of Vehicles (IoV) is treated as an extension of Vehicle-to-Vehicle (V2V) communication network. IoV helps in enhancing driving aids with the help of vehicle Artificial Intelligence (AI) awareness of other vehicles and their actions. IoV is connected in an adhoc networking environment which utilizes each vehicle in the network as a node, called Vehicular Ad Hoc Network (VANET), where the vehicles may be also connected to the public Internet. It is specifically important for the autonomous vehicles because they can instantaneously communicate with other vehicles surrounding them. In addition, safely avoiding accident prone zones is crucial in order to continue secure and smart transportation. Since the communication among various entities involved in the IoV environment is via open channel, it gives an opportunity to a passive/active adversary to intercept, modify, delete or even insert fake information during communication. It is then a serious concern for the vehicles users to determine whether the received information is genuine. In this survey paper, various security aspects, threats and attacks, network and threat models related to the IoV environment are discussed. Next, a taxonomy of security protocols is given that is essential to provide IoV data security. In particular, focus on various authentication protocols is given that is needed for mutual authentication among the involved entities in the IoV environment for secure communication. A detailed comparative analysis among various state-of-art authentication protocols proposed in the related IoV environment is provided to show their effectiveness as well as security and functionality features. Moreover, some testbeds are described that were designed and implemented for the IoV environment. In addition, some future challenges for IoV security protocols are also highlighted that are necessary to address in the future. INDEX TERMS Internet of Vehicles (IoV), vehicular adhoc networks (VANETs), authentication, batch verification, security.

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Classification Approach for Intrusion Detection in Vehicle Systems

Cited by 80 publications

References 41 publications

A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System

A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

Authentication Protocols in Internet of Vehicles: Taxonomy, Analysis, and Challenges

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