Collaborative-trust approach toward malicious node detection in vehicular ad hoc networks

Sultan, Shahid; Javaid, Qaisar; Малик, Аднан; Al‐Turjman, Fadi; Attique, Muhammad

doi:10.1007/s10668-021-01632-5

Cited by 23 publications

(12 citation statements)

References 32 publications

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“…The estimation is performed by a voting strategy whose outcome relies on the majority concept. However, relying on node behavior is sub-optimal because the cITS is non-stationary and since nodes change their behavior as the topology changes [22,23]. Moreover, relying on a voting approach for the trustworthiness estimation is always biased towards the majority, which in some cases, can be compromised when the attacker gains a majority foothold.…”

Section: Related Workmentioning

confidence: 99%

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: Related Workmentioning

confidence: 99%

Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks

2022

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“…The Internet of Things (IoT) and machine learning have shown improved performance in many applications, such as facial recognition, biometrics, and surveillance [27,28]. Recently, the blockchain-based multi-IoT method was presented by Jeong et al [29].…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

Azhar

Sharif

Raza

et al. 2021

Sensors

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The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo–sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain.

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“…In the application scenario of opportunistic social networking, when people transfer data through portable mobile devices, considering that there is no suitable transmission target within a certain transmission range, or the transmission target does not respond, it will occupy a large amount of buffer in the data storage area of the device, which affected the data transmission rate [11]. On the one hand, the information has not been received and responded for a long time, and it is always in a waiting state, which causes a large amount of reserved information to be stored in the device, resulting in transmission delay [12]. On the other hand, when there is a large amount of buffer space when new urgent data information is received, the information release sequence needs to be updated to achieve real-time release [13].…”

Section: Introductionmentioning

confidence: 99%

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

Gou

Long

et al. 2022

Wireless Communications and Mobile Computing

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Billions of people around the world send and receive data over online networks daily. Sufficient and redundant data are transmitted over social platforms with AI-assisted in 5G networks. In opportunistic social networks, the main challenge faced by traditional methods is that numerous user nodes participate in data transmission, causing a lot of message copy redundancy and node cache consumption. As a result, the transmission delay of the algorithm is high, the node energy consumption is too large, and even information is lost. To solve these problems, this study establishes an artificial intelligence-based optimization multiple evaluation method. The main purpose of this method is to avoid information loss caused by data loss when reducing data noise, reasonably select communication nodes in opportunistic social network scenarios, optimize data transmission performance, and avoid network congestion. Moreover, our method can effectively identify and exclude potential malicious nodes, reducing the situation that packets are intercepted and discarded. The experiment confirms that the optimized transmission evaluation scheme can effectively reduce routing overheads and energy consumption of a user node, improve the delivery ratio of node data transmission, and ensure the reliability and security of data transmission.

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Collaborative-trust approach toward malicious node detection in vehicular ad hoc networks

Cited by 23 publications

References 32 publications

Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks

Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

Effective Data Optimization and Evaluation Based on Social Communication with AI-Assisted in Opportunistic Social Networks

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