Detecting Sybil Node in Intelligent Transport System

Akshaya, K; Sarath, T. V.

doi:10.1007/978-981-16-7167-8_43

Cited by 4 publications

(3 citation statements)

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“…The feature sizes range between 5 and 25 incremented by 3. The results show that the proposed ADBN-IDS achieved higher accuracy over the other two classifiers (i.e., SVM and LR) [28,29]. This is attributed to the ability of the BiMAV method (incorporated into ADBN-IDS) to detect the degradation in the model's performance and trigger the training on the right time.…”

Section: Resultsmentioning

confidence: 93%

“…The x axis represents the number of features used for training, and the y axis represents the value of performance measure achieved. The comparison was conducted between the ADBN-IDS that employed the BiMAV for adaptation and the conventional approach used in the existing studies [28,29]. As depicted in the figures, the proposed ADBN-IDS outperformed the related techniques in terms of accuracy, detection rate, false positive rate, and the F measure.…”

Section: Resultsmentioning

confidence: 99%

“…BSM combines CAM and DENM messages. The first part of BSM, as well as CAM in the European standard, carries information about position, heading, speed, acceleration, steering wheel angle, vehicle role, vehicle size, and the status of vehicle lights [4,27,28]. Unlike the first part of BSM that is included in all BSM messages, the second part of BSM (which corresponds to DENM in the European standard) is only included when an event happens, to carry information about such an event.…”

Section: The Datasetmentioning

confidence: 99%

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Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks

2022

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The adoption of cooperative intelligent transportation systems (cITSs) improves road safety and traffic efficiency. Vehicles connected to cITS form vehicular ad hoc networks (VANET) to exchange messages. Like other networks and systems, cITSs are targeted by attackers intent on compromising and disrupting system integrity and availability. They can repeatedly spoof false information causing bottlenecks, traffic jams and even road accidents. The existing security infrastructure assumes that the network topology and/or attack behavior is static. However, the cITS is inherently dynamic in nature. Moreover, attackers may have the ability and resources to change their behavior continuously. Assuming a static IDS security model for VANETs is not suitable and can lead to low detection accuracy and high false alarms. Therefore, this paper proposes an adaptive security solution based on deep learning and contextual references that can cope with the dynamic nature of the cITS topologies and increasingly common attack behaviors. In this study, deep belief networks (DBN) modeling was used to train the detection model. Binary cross entropy was used as a loss function to measure the prediction error. Two activation functions were used, Relu and Softmax, for input–output mapping. The Relu was used in the hidden layers, while the Sigmoid was used in the last layer to map the real vector to output between 0 and 1. The adaptation mechanism was incorporated into the detection model using a moving average that monitors predicted values within a time window. In this way, the model can readjust the classification thresholds on-the-fly as appropriate. The proposed model was evaluated using the Next Generation Simulation (NGSIM) dataset, which is commonly used in such related works. The result is improved accuracy, demonstrating that the adaptation mechanism used in this study was effective.

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Section: Resultsmentioning

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: The Datasetmentioning

confidence: 99%

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