Internet of vehicles supports to transfer of safety-related messages, which help to mitigate road accidents. Internet of vehicles allows vehicle to cooperative communicate, share position and speed data among vehicles and road side units. The vehicular network become prone to large number of attacks including false warnings, mispositioning of vehicles etc. The authentication of messages to identify the normal message packet from attack messages packet and its prevention is a major challenging task. This paper focuses on applying deep learning approach using binary classification to classify the normal packets from malicious packets. The process starts with preparing the training dataset from the open-source KDD99 and CICIDS 2018 datasets, consisting of 1,20,223 network packets with 41 features. The one-dimensional network data is preprocessed using an autoencoder to eliminate the unwanted data in the initial stage. The valuable features are then filtered as 23 out of 41, and the model is trained with structured deep neural networks, then combined with the Softmax classifier and Relu activation functions. The proposed Intrusion prevention model is trained and tested with google Colab, an open platform cloud service, and the open-source tensor flow. The proposed prevention classifier model was validated with the simulation dataset generated in network simulation. The experimental results show 99.57% accuracy, which is the highest among existing RNN and CNN-based models. In the future, the model can be trained on different datasets, which will further improve the model's efficiency and accuracy.
The present paper introduces a Convolutional Neural Network (CNN) for the assessment of image quality without a reference image, which comes under the category of Blind Image Quality Assessment models. Edge distortions in the image are characterized as input feature vectors. This approach is in justification of the fact that subjective assessment focusses on image features that emanate from the edges and the boundaries present in the image. The earlier methods were found to use complex transformations on the image to extract the features before training or as a part of the training. The present work uses Prewitt kernel approach to extract the horizontal and vertical edge maps of the training images. These maps are then input to a simple CNN for extracting higher level features using non-linear transformations. The resultant features are mapped to image quality score by regression. The network uses Spatial Pyramid Pooling (SPP) layer to accommodate input images of varying sizes. The present proposed model was tested on popular datasets used in the domain of Image Quality Assessment (IQA). The experimental results have shown that the model competes with the earlier proposed models with simplicity of feature extraction and involvement of minimal complexity.
The increasing rate of road fatalities has demanded the attention of the researchers, scientists, Industry and government organizations and technologies. The impact of accidents is simulated by rear-end collision with parameters such as vehicle position, direction, speed, inter-vehicle distance, and relative speeds, etc. Open source simulators have to be adopted to study and analyze various collision scenarios in vehicular networks. Safety mechanism proposed to minimize the possibility of accidents and mitigate the effect of the escalating incident. The proposed mechanism estimates the point of intersection, time to collision, and time to avoid accidents. Using parameters, the proposed mechanism able to determine accidents with 92.6% accuracy. The remaining 7.4% cases enable the passive safety system to help the people to stay alive, minimize the damage in case an accident.
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