DeepADV: A Deep Neural Network Framework for Anomaly Detection in VANETs

Alladi, Tejasvi; Gera, Bhavya; Agrawal, Ayush; Chamola, Vinay; Yu, F. Richard

doi:10.1109/tvt.2021.3113807

Cited by 38 publications

(26 citation statements)

References 30 publications

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“…wherein they extend the detection to all attack types in VeReMi dataset. In particular, the work presented in [13] demonstrates overall superior performance against the proposed schemes in the original VeReMi paper [10]. We deem that the detection of all attack types is important in the case of assessing a practical application of misbehavior detection in V2X scenarios.…”

Section: B Baseline Schemes and Performance Metricsmentioning

confidence: 91%

“…A deep neural network (DNN) framework for anomaly detection has been recently proposed in [13]. By projecting high-dimensional V2X information into a lower-dimensional latent representation, the authors aim to differentiate data of genuine vehicles from misbehaving ones.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Although several research works [8], [9], [11], [12] are available in recent literature, most of the proposed schemes have been evaluated only with a few attack types, e.g., position falsification. We therefore compare our RL-based detection results with two recent works presented in [10] and [13],…”

Section: B Baseline Schemes and Performance Metricsmentioning

confidence: 99%

“…We deem that the detection of all attack types is important in the case of assessing a practical application of misbehavior detection in V2X scenarios. Therefore, the comparison is performed for all attacks scenario considered in [10], [13]. To achieve this, a single dataset was created by merging all data from each attack type, resulting in 19 attacks with labelled data points.…”

Section: B Baseline Schemes and Performance Metricsmentioning

confidence: 99%

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Reinforcement Learning Based Misbehavior Detection in Vehicular Networks

Sedar

Kalalas

Vázquez-Gallego

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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Vehicle-to-everything (V2X) communication is contributing towards the realization of futuristic vehicular networks such as Internet-of-Vehicles (IoV). The IoV is expected to usher in a new direction of intelligence and networking to achieve the goal of intelligent transport systems, which rely on the secure exchange of messages between vehicles and infrastructure. However, the transmission of false/incorrect data by malicious vehicles may cause serious damages on road safety. Therefore, it is crucial to detect safety-threatening incorrect information and mitigate potentially detrimental effects on road users. In this paper, we propose a reinforcement learning (RL)-based misbehavior detection approach for V2X scenarios. In our method, the RLbased detection model processes V2X data broadcast by vehicles as time-series at the roadside units, and classifies incoming data as misbehaving or genuine. We evaluate the proposed RL-based approach for detection of various attack types using an opensource dataset, and compare its performance against recent work in misbehavior detection. Our scheme is able to detect all types of misbehavior with a superior recall of 0.9970 and an F1 score of 0.9845, yielding a significant improvement over the benchmarks. Our research outcomes further reveal that misbehaving vehicles can be detected with a great accuracy of 0.9882 by exploiting real-time V2X information.

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Section: B Baseline Schemes and Performance Metricsmentioning

confidence: 91%

Section: Background and Related Workmentioning

confidence: 99%

Section: B Baseline Schemes and Performance Metricsmentioning

confidence: 99%

Section: B Baseline Schemes and Performance Metricsmentioning

confidence: 99%

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Reinforcement Learning Based Misbehavior Detection in Vehicular Networks

Sedar

Kalalas

Vázquez-Gallego

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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“…It is in close interaction with many scientific and technological fields. NNs support a range of technological fields including medical technology [1] as well as image processing [2], cloud computing [3], aerospace technology [4], meteorology [5], and especially in security-related technologies [6,7]. Moreover, several technologies and sciences such as chaos theory [8], frequency-domain transforms [9], genetic algorithms [10] and Digital Signal Processing (DSP) [1] are supporting neural networks as enablers.…”

Section: Introductionmentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.

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