Biblio

Network intrusion detection systems (NIDS) are widely used solutions targeting the security of any network device connected to the Internet and are taking the lead in the battle against intruders. This paper addresses the network security issues by implementing a hardware-based NIDS solution with a Naïve Bayes machine learning (ML) algorithm for classification using NSL Knowledge Discovery in Databases (KDD) dataset. The proposed FPGA implementation of the Naive Bayes classifier focuses on low latency and provides intrusion detection in just 240ns, with accuracy/precision of 70/97%, occupying 1 % of the Virtex7 VC709 FPGA chip area.

Recently, there has been a pronounced increase of interest in the field of renewable energy. In this area power inverters are crucial building blocks in a segment of energy converters, since they change direct current (DC) to alternating current (AC). Grid connected power inverters should operate in synchronism with the grid voltage. In this paper, the structure of a power system based on adaptive filtering is described. The main purpose of the adaptive filter is to adapt the output signal of the inverter to the corresponding load and/or grid signal. By involving adaptive filtering the response time decreases and quality of power delivery to the load or grid increases. A comparative analysis which relates to power system operation without and with adaptive filtering is given. In addition, the impact of variable impedance of load on quality of delivered power is considered. Results which relates to total harmonic distortion (THD) factor are obtained by Matlab/Simulink software.