NETWORK INTRUSION DETECTION USING AUTOENCODE NEURAL NETWORK

There is a wide range of applications for photodetectors in wireless sensing networks as well as environmental monitoring systems, while 3D materials contain gaps of different bands that can be used in different application fields. Graphene was used in this research as a semiconductor material to produce a photodetector operating with a wide range of wavelengths that useful in detecting waves of applied interest in the industrial field. The very small energy gap of a single Graphene layer under the grating in Gold based MSM nanostructure showed encourage behavior of electric field distribution upon the plasmonic surface. Also, the measured optical properties and detector parameters gave acceptable results. The achieved results show that the best plasmonic surface, responsivity about 94.48 A/W, and detectivity about 5.408×10¹³ with lowest NEP of about 1.849×10^-14 when using 1550 nm wavelength.