Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

Erskine, Samuel; Elleithy, Khaled

doi:10.3390/electronics8070776

Cited by 16 publications

(10 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e GL10 object with relevant drawing parameters is configured for drawing. Finally, in the onDrawFrame method of the renderer class inherited from GLSurfaceView.Renderer, all virtual objects are traversed and drawn one by one at the designated position in the frustum, so as to be superimposed and displayed on the real picture of the mobile phone camera to realize virtual and real fusion [23]. is Intel ® Core ™ i7-6700K CPU @ 4.00 GHz × 8 (8 GB memory), and the GPU is NVIDIA GeForce GTX 1060 (6 GB video memory).…”

Section: Ar Intelligent Detectionmentioning

confidence: 99%

Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Wei

Tang

et al. 2021

Complexity

View full text Add to dashboard Cite

It aims to improve the degree of visualization of building data, ensure the ability of intelligent detection, and effectively solve the problems encountered in building data processing. Convolutional neural network and augmented reality technology are adopted, and a building visualization model based on convolutional neural network and augmented reality is proposed. The performance of the proposed algorithm is further confirmed by performance verification on public datasets. It is found that the building target detection model based on convolutional neural network and augmented reality has obvious advantages in algorithm complexity and recognition accuracy. It is 25 percent more accurate than the latest model. The model can make full use of mobile computing resources, avoid network delay and dependence, and guarantee the real-time requirement of data processing. Moreover, the model can also well realize the augmented reality navigation and interaction effect of buildings in outdoor scenes. To sum up, this study provides a research idea for the identification, data processing, and intelligent detection of urban buildings.

show abstract

Section: Ar Intelligent Detectionmentioning

confidence: 99%

Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Wei

Tang

et al. 2021

Complexity

View full text Add to dashboard Cite

show abstract

“…In particular, many activities have been done, such as dataset preparation, environmental noise injection, communication simulation, and misbehavior simulation. Similar to related studies [2,20,29,35,76,77], the Matlab tool [78] has been used for simulating the environmental noises, vehicle communication, and misbehaving vehicles.…”

Section: Methodsmentioning

confidence: 99%

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

et al. 2019

View full text Add to dashboard Cite

Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles' scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. IntroductionRoad collisions are increasing, and they are being expected to be the fifth leading cause of death by 2030 [1,2]. Annually, millions of people lose their lives on roads worldwide due to traffic accidents [1], with 40 times more suffering from injuries. These accidents are also the main cause of traffic congestion, which in turn has a great impact on the economy [3,4], and billions of dollars are lost due to the treatment of injuries, loss of property, lost working hours, and high fuel consumption [5]. Several studies reveal that more than 95%...

show abstract

“…A neural network (NN) is a complex network system that simulates the human brain. It consists of a large number of simple neurons connected with each other [36][37][38]. A feed forward neural network (FFNN) is a kind of neural network.…”

Section: Modeling Of Aircraft Parametersmentioning

confidence: 99%

Optimal Cruise Characteristic Analysis and Parameter Optimization Method for Air-Breathing Hypersonic Vehicle

Zhou

Wang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

There is an optimal cruise point with the lowest fuel consumption when a hypersonic vehicle performs steady-state cruise. The optimal cruise point is composed of the optimal cruise altitude and the optimal cruise Mach number, and its position is closely related to the aircraft parameters. This article aims to explore the relationship between the optimal cruise point and relevant aircraft parameters and establish a model to describe it, then an aircraft parameter optimization method of adjusting the optimal cruise point to the target position is explored with validation by numerical simulation. Firstly, a parameterized model of a hypersonic vehicle is obtained as a basis, then the optimal cruise point is obtained by the optimization method, and the influence of a single aircraft parameter on the optimal point is investigated. In order to model the relationship between the aircraft parameters and the optimal cruise point, a neural network is employed. Finally, the model is used to optimize the aircraft parameters under multiple constraints. The results show that, after aircraft parameters optimization, the optimal cruise point is located at the predetermined position and the fuel consumption is lower, which provides a new perspective for the design of aircraft.

show abstract

Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network

Cited by 16 publications

References 45 publications

Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Adoption of Convolutional Neural Network Algorithm Combined with Augmented Reality in Building Data Visualization and Intelligent Detection

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

Optimal Cruise Characteristic Analysis and Parameter Optimization Method for Air-Breathing Hypersonic Vehicle

Contact Info

Product

Resources

About