Vehicle in vehicular ad hoc networks (VANETs) broadcasts beacons about their traffic status wirelessly for improving traffic safety and efficiency. Before deployment of the VANET system, problems related to security and privacy should be carefully addressed. In this paper, we propose a lightweight authentication with conditional privacy-preserving scheme for guaranteeing secure communication in VANET. The proposed scheme is suitable for addressing issues related to security and privacy because it combines the tamper-proof device (TPD) based schemes with the roadside unit (RSU) based schemes. Based on elliptic curve cryptography, the proposed scheme preloads the initial public parameters and keys of the system in each TPD of RSU instead of the TPD of the on-border unit (OBU). Furthermore, the proposed scheme not only achieve security and privacy requirements but also resists common security attacks. The performance evaluation shows that the proposed scheme has a lower cost compared with other existing schemes in terms of computation cost and communication cost. INDEX TERMS Authentication, tamper proof device (TPD), privacy-preserving, vehicular ad-hoc networks (VANETs). I. INTRODUCTION Recently, the intelligent transportation system (ITS) has attracted more deliberate attention from the motor industry, academia, and even government in recent years since it is reducing traffic congestion, enhancing driving efficiency, improving traffic safety, minimizing environmental pollution and providing convenience [1], [2]. Vehicular ad hoc networks (VANETs) are an entity of ITS with a fully a selforganizing wireless ad hoc communication system containing vehicles equipped with onboard unit (OBU), a trusted authority (TA) which preloads the initial public parameters of the VANET, and a road side unit (RSU) deployed at intersections in country, as presented in Figure 1. The communications types in VANETs contain two main modes: vehicle-tovehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication [3], [4]. Dedicated short-range communication (DSRC) protocol is an open wireless technology which allows the vehicle for FIGURE 1: A typical VANET scenario. processing, receiving, broadcasting and communicating with each other or nearby RSU and exchanging messages such as
Cancer is considered one of the most aggressive and destructive diseases that shortens the average lives of patients. Misdiagnosed brain tumours lead to false medical intervention, which reduces patients’ chance of survival. Accurate early medical diagnoses of brain tumour are an essential point for starting treatment plans that improve the survival of patients with brain tumours. Computer-aided diagnostic systems have provided consecutive successes for helping medical doctors make accurate diagnoses and have conducted positive strides in the field of deep and machine learning. Deep convolutional layers extract strong distinguishing features from the regions of interest compared with those extracted using traditional methods. In this study, different experiments are performed for brain tumour diagnosis by combining deep learning and traditional machine learning techniques. AlexNet and ResNet-18 are used with the support vector machine (SVM) algorithm for brain tumour classification and diagnosis. Brain tumour magnetic resonance imaging (MRI) images are enhanced using the average filter technique. Then, deep learning techniques are applied to extract robust and important deep features via deep convolutional layers. The process of combining deep and machine learning techniques starts, where features are extracted using deep learning techniques, namely, AlexNet and ResNet-18. These features are then classified using SoftMax and SVM. The MRI dataset contains 3,060 images divided into four classes, which are three tumours and one normal. All systems have achieved superior results. Specifically, the AlexNet+SVM hybrid technique exhibits the best performance, with 95.10% accuracy, 95.25% sensitivity, and 98.50% specificity.
Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.
The vehicles in the fifth-generation (5G)-enabled vehicular networks exchange the data about road conditions, since the message transmission rate and the downloading service rate have been considerably brighter. The data shared by vehicles are vulnerable to privacy and security issues. Notably, the existing schemes require expensive components, namely a road-side unit (RSU), to authenticate the messages for the joining process. To cope with these issues, this paper proposes a provably secure efficient data-sharing scheme without RSU for 5G-enabled vehicular networks. Our work included six phases, namely: TA initialization (TASetup) phase, pseudonym-identity generation (PIDGen) phase, key generation (KeyGen) phase, message signing (MsgSign) phase, single verification (SigVerify) phase, and batch signatures verification (BSigVerify) phase. The vehicle in our work has the ability to verify multiple signatures simultaneously. Our work not only achieves privacy and security requirements but also withstands various security attacks on the vehicular network. Ultimately, our work also evaluates favourable performance compared to other existing schemes with regards to costs of communication and computation.
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