SVM-based Detection of False Data Injection in Intelligent Transportation System

Cabelin, Joe Diether; Alpano, Paul Vincent S.; Pedrasa, Jhoanna Rhodette

doi:10.1109/icoin50884.2021.9333942

Cited by 16 publications

(13 citation statements)

References 15 publications

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“…The data set obtained from a one-way four-lane highway simulation using MATLAB and NS-3 was tested in the SVMbased IDS model and it was concluded that it has high accuracy in detecting malicious information. The system has also been shown to reduce traffic congestion by reducing malicious packets [24]. I. Bolodurina et al examined the efficiency of various ML algorithms in identifying new features to increase the efficiency of ML methods used in intrusion detection applications in VANETs.…”

Section: A Trust Managementmentioning

confidence: 99%

A Review on Machine Learning Techniques Used in VANET and FANET Networks

MUTİ

Ülkü

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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The widespread use of the Internet and the increase in the number and variety of devices connected to the internet have led to the emergence of new methods in wireless communication. Dynamic and temporary Ad-Hoc networks, which do not require a fixed infrastructure as in traditional wireless network communication, are one of these new methods. The fact that Ad-Hoc networks do not need a fixed infrastructure has revealed a network structure with a lower cost and less configuration. Mobile Ad-Hoc networks play an important role, especially in the communication of nodes on the move. FANET (Flying Ad-Hoc Networks) networks, which are called flying ad hoc networks, are mobile Ad-Hoc networks used for communication of unmanned aerial vehicles (UAV), and VANET (Vehicular Ad-Hoc Networks) networks, which are called vehicular ad hoc networks, are mobile Ad-Hoc networks used for communication of road vehicles. The development and dissemination of these networks make a significant contribution to the development of autonomous vehicles and UAVs. The increase in the use of FANET and VANET networks, which are specialized subnets of mobile Ad-Hoc networks, and the increase in the number of nodes in these networks have caused problems related to security, efficiency, and sustainability in these networks. Machine learning methods, one of today' s effective and common approaches, are one of the ways that are frequently used in solving the problems specified in FANET and VANET networks. The rapid topology change, which is one of the most important features of these networks, makes it difficult to provide traffic management, trust management, routing, and data transmission. In this direction, machine learning approaches play an active role. In this study, it is presented by examining which machine learning techniques are used in the literature to perform important tasks such as traffic management, trust management, routing, and data transfer. Thus, it is aimed for those who will work in these fields to acquire information about machine learning approaches that can be used. Since the FANET network type is a new approach, it has been observed that there are few studies using machine learning. In VANET systems, studies using machine learning methods are especially intense in 2021. This study was carried out to give the reader an idea about which machine learning methods can be used in which problems in FANET and VANET networks.

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Section: A Trust Managementmentioning

confidence: 99%

A Review on Machine Learning Techniques Used in VANET and FANET Networks

MUTİ

Ülkü

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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“…Considering the vulnerability of traditional communication, a state estimation and control algorithm against FDIAs was developed. 31 In Cabelin et al, 32 a machine learning-based detection and identification method against FDIAs was proposed. However, the above learning-based detection methods relay on the training of historical data.…”

Section: Introductionmentioning

confidence: 99%

Adaptive unknown input observer-based detection and identification method for intelligent transportation under malicious attack

Cheng

Pan

Zhang

2023

Measurement and Control

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This paper aims at developing a novel detection and identification method against malicious attacks in intelligent transportation. Due to the development and applications of communication and advanced sensor technologies, intelligent transportation has faced new safety risks. In particular, the emerging malicious attacks, such as false data injection attack, can mask the destruction of physical dynamic by tampering with information in layer to fool the current detection methods. Because of this reason, an adaptive unknown input observer-based detection and identification method is developed. Firstly, a physical dynamics model of vehicle networking system is established by considering the actual physical state. Considering the spoofing characteristics of false data injection attack, an unknown input observer-based detection method is proposed. Through the design of adaptive unknown input observer parameters, the detection performance, can be improved by cutting down the state estimation error. Compared with the UIO-based detection method, simulations demonstrate that the false positive rate can be reduced 0.1%. Based on the feature of state residuals that is not sensitive to the attacked ith residual, but sensitive to other residuals, a novel identification criterion is developed. At last, simulation experiments on the Matlab verify the performance of the proposed detection and identification algorithm in intelligent transportation system.

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“…To respond to the FDI attacks timely, scholars have carried out some research on the security of vehicle networking systems under FDI attacks [16][17][18][19][20]. In [16], L. Guo et al investigated the covert characteristics of FDI attacks and gave the optimal attack strategies with respect to the cyber-physical system of intelligent vehicle networking.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the FDI attack was modeled as an additive attack that affects the vehicle platoon, and a new assessment method was developed [17]. To detect and identify the FDI attacks, a machine learning-based detection method is proposed in [18]. In [19], a fuzzy theory and deep learning-based detection are developed by considering the impact of FDI attacks on the load frequency control system.…”

Section: Introductionmentioning

confidence: 99%

“…Taking the covert characteristics of FDI attacks into account, a partial differential equation model-based detection method was developed [20]. The above artificial intelligence-based detection method in [18,19] can effectively detect the FDI attacks but need to depend on the training of historical data in the vehicle networking system. Although the model-based detection work in [20] can reduce the complexity of attack detection, the computation of the precomputed threshold limits the detection performance of FDI attacks.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Isolation of False Data Injection Attack in Intelligent Transportation System via Robust State Observer

Huang

Wang

2022

Processes

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As the future of transportation systems, the intelligent transportation system is a promising technology to improve the increasingly serious traffic problems. However, the integration of cyber-physical systems makes them vulnerable to new cyber–physical attacks. To ensure the security of intelligent transportation systems, a novel robust state observer-based detection and isolation method against false data injection attacks is developed. Based on the constructed dynamic model of intelligent vehicle networking, the covert characteristic of a false data injection attack is analyzed. Then, a novel state residuals-based detection criterion is developed by using a real-time observed state. To shorten the detection time, an adaptive detection threshold is designed to replace the existing computed threshold. In addition, robust state observer banks are established to isolate multiple injected attacks. Finally, simulation results on the vehicle networking system demonstrate the effectiveness of the developed detection and isolation method against false data injection attacks.

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SVM-based Detection of False Data Injection in Intelligent Transportation System

Cited by 16 publications

References 15 publications

A Review on Machine Learning Techniques Used in VANET and FANET Networks

A Review on Machine Learning Techniques Used in VANET and FANET Networks

Adaptive unknown input observer-based detection and identification method for intelligent transportation under malicious attack

Detection and Isolation of False Data Injection Attack in Intelligent Transportation System via Robust State Observer

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