With the use of Convolutional Neural Network (CNN) in the application of sensor signal processing system, it usually faces the urgent requirements of system integration, high throughput, hardware resource and energy efficiency. The proposed scheme shows at least 4% improvement in classification accuracy over the other DL-based schemes. The simulation results show that the classification accuracy of proposed scheme is higher than the conventional DL-based schemes under various signal-to-noise ratio (SNR) conditions. A convolutional neural network (CNN) with six stages is employed for AMC. The combination of scaling factors, which maximize the classification accuracy is chosen. In this work, different scaling factors are selected for the generation of M-QAM frames. The classification accuracy of conventional DL-based AMC schemes drastically reduces, when different order QAM modulation schemes are accommodated. The next-generation networks use adaptive and higher-order quadrature amplitude modulation (QAM) schemes for higher spectral efficiency. Most of the conventional models proposed are tested for the limited set of modulation schemes transmitted over additive white Gaussian noise (AWGN) channels without considering the effect of multipath fading and Doppler shift. Because of its powerful feature extraction ability and promising performance over the conventional schemes, deep learning (DL) models are employed to automatic modulation classification (AMC) problems. This enables enhanced security in vehicular networks.ĭue to stochastic wireless environment, the process of modulation classification has become a challenging task. The proposed AHDNN framework has a very low false negative rate of 0.012 ensuring a very low rate of missing an intrusion in normal communication. This research paper presents an Attention-enabled Hierarchical Deep Neural Network (AHDNN) as a solution to detect intrusion and ensure autonomous vehicles’ security both at the nodes and at the network level. This motivated us to develop an intrusion detection model that can be run in low-end devices with low processing and memory capacity and can prevent security threats and protect the connected vehicle network. Nevertheless, wirelessly coupled cars on the network are in constant peril. Artificial Intelligence is being used by both scientists and hackers for protecting and attacking the networks, respectively. Researchers are developing several approaches to combat security threats in connected and autonomous vehicles. These threats jeopardize the safety of vehicles, riders, and the entire system. The dynamic topology of this network, connecting a large number of vehicles, makes it vulnerable to several threats like authentication, data integrity, confidentiality, etc. Intelligent vehicle systems can exchange seamless information to assist cars to ensure better traffic control and road safety. The usage of the Internet of Things (IoT) in the field of transportation appears to have immense potential.
0 kommentar(er)
