Biblio

The Software Defined Network (SDN) provides higher programmable functionality for network configuration and management dynamically. Moreover, SDN introduces a centralized management approach by dividing the network into control and data planes. In this paper, we introduce a deep learning enabled intrusion detection and prevention system (DL-IDPS) to prevent secure shell (SSH) brute-force attacks and distributed denial-of-service (DDoS) attacks in SDN. The packet length in SDN switch has been collected as a sequence for deep learning models to identify anomalous and malicious packets. Four deep learning models, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Stacked Auto-encoder (SAE), are implemented and compared for the proposed DL-IDPS. The experimental results show that the proposed MLP based DL-IDPS has the highest accuracy which can achieve nearly 99% and 100% accuracy to prevent SSH Brute-force and DDoS attacks, respectively.

The study of spin waves (SW) excitation in magnetic devices is one of the most important topics in modern magnetism due to the applications of the information carrier and the signal processing. We experimentally realize a spin-wave generator, capable of frequency modulation, in a magnonic waveguide. The emission of spin waves was produced by the reversal or oscillation of nanoscale magnetic vortex cores in a NiFe disk array. The vortex cores in the disk array were excited by an out of plane radio frequency (rf) magnetic field. The dynamic behaviors of the magnetization of NiFe were studied using a micro-focused Brillouin light scattering spectroscopy (BLS) setup.