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In this paper an intrusion detection method based on dynamic growing neural network (DGNN) for wireless networking is presented. DGNN is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the(More)
With the increasing demands for vehicle-to-vehicle and vehicle-to-infrastructure communications in intelligent transportation systems, new generation of vehicular telematics inevitably depends on the cooperation of heterogeneous wireless networks. In heterogeneous vehicular telematics, the network selection is an important step to the realization of(More)
—This work investigates a robust energy-efficient solution for multiple-input-multiple-output (MIMO) transmissions in cognitive vehicular networks. Our goal is to design an optimal MIMO beamforming for secondary users (SUs) considering imperfect interference channel state information (CSI). Specifically, we optimize the energy efficiency (EE) of SUs, given(More)
As network traffic bandwidth is increasing at an exponential rate, it’s impossible to keep up with the speed of networks by just increasing the speed of processors. Besides, increasingly complex intrusion detection methods only add further to the pressure on network intrusion detection (NIDS) platforms, so the continuous increasing speed and throughput of(More)
Routing is a challenging task in the ad hoc networks, especially in vehicular ad hoc networks (VANETs) where the network topology changes fast and frequently. Since the nodes in VANETs are vehicles, which can easily provide the required power to run GPS receiver to get the accurate information of their position, the position-based routing is found to be a(More)