Applying Smart Security Model to Protect Client Services From the Threats of the Optical Network

Advancements in Security and Privacy Initiatives for Multimedia Images - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-2795-5.ch004 ◽

2021 ◽

pp. 80-113

Author(s):

Kamel H. Rahouma ◽

Ayman A. Ali

Keyword(s):

Signal To Noise Ratio ◽

Optical Network ◽

Optical Signal ◽

Software Defined Network ◽

Security Model ◽

Signal To Noise ◽

New Model ◽

Intelligent Software ◽

Optical Transport Network ◽

The Right

The chapter discusses the security of the client signals over the optical network from any wiretapping or loosing. The physical layer of the optical transport network (OTN) is the weakest layer in the network; anyone can access the optical cables from any location and states his attack. A security layer is proposed to be added in the mapping of OTN frames. The detection of any intrusion is done by monitoring the variations in the optical signal to noise ratio (OSNR) by using intelligent software defined network. The signal cryptographic is done at the source and the destination only. The chapter shows how the multi-failure restorations in the multi-domains could be done. A new model is introduced by slicing the multi-domains to three layers to fit the needs of 5G. The results show that the multi-failure restoration improved from 25% to 100%, the revenue from some OTN domains increased by 50%, the switching time enhanced by 50%, the latency reduced from 27 msec to 742 usec, and it will take many years to figure out the right keys to perform the decryption process.

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Simultaneous Monitoring of Chromatic Dispersion and Optical Signal to Noise Ratio in Optical Network Using Asynchronous Delay Tap Sampling and Convolutional Neural Network (Deep Learning)

2018 20th International Conference on Transparent Optical Networks (ICTON) ◽

10.1109/icton.2018.8473703 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tomasz Mrozek

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Signal To Noise Ratio ◽

Chromatic Dispersion ◽

Optical Network ◽

Optical Signal ◽

Signal To Noise ◽

Noise Ratio

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Entirely Passive Remote Node Design And System Architecture For Bus Topology Based 80Gbps Symmetrical NG-PON2

10.21203/rs.3.rs-625511/v1 ◽

2021 ◽

Author(s):

Ramandeep Kaur ◽

Simranjit Singh Tiwana ◽

Rajandeep Singh ◽

Simranjit Singh

Keyword(s):

Bit Error Rate ◽

System Architecture ◽

Signal To Noise Ratio ◽

Optical Network ◽

Optical Signal ◽

Passive Optical Network ◽

Optical Frequency ◽

Signal To Noise ◽

Broadcast Network ◽

Network Topologies

Abstract The next generation passive optical network stage 2 (NG-PON2) is a broadcast network in which a large number of optical network units (ONUs) can be served using various network topologies. In this paper, a purely passive remote node (RN) design based on fiber bragg grating (FBG) is presented for the bus topology of a high split NG-PON2 system. The performance of the proposed node has been analysed for a symmetric 80 Gbps NG-PON2 system serving 1024 ONUs in terms of bit error rate (BER) and optical signal to noise ratio (OSNR). The working of the proposed node has been explained with the optical frequency spectrums for downstream and upstream signals. The observed OSNR levels for the ONUs operating at the 1st RN is 84.8743dB which decreases to 75.7973dB for the ONUs connected to the 8th RN. The BER level < 10-9 for both upstream and downstream confirms the correct working of the proposed node in the NG-PON2 system.

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Simultaneous monitoring of chromatic dispersion and optical signal to noise ratio in optical links using convolutional neural network and asynchronous delay-tap sampling

Optica Applicata ◽

10.37190/oa200301 ◽

2020 ◽

Vol 50 (3) ◽

Author(s):

Tomasz Mrozek ◽

Krzysztof Perlicki ◽

Andrzej Jakubiak

Keyword(s):

Signal To Noise Ratio ◽

Chromatic Dispersion ◽

Optical Network ◽

Optical Signal ◽

Phase Portraits ◽

Signal To Noise ◽

Optical Links ◽

Binary Images ◽

Noise Ratio ◽

Amplified Spontaneous Emission Noise

The article presents a method for image analysis using asynchronous delay-tap sampling (ADTS) technique and convolutional neural networks (CNNs), allowing simultaneous monitoring of many phenomena occurring in the physical layer of the optical network. The ADTS method makes it possible to visualize the course of the optical signal in the form of characteristics (so-called phase portraits), which change their shape under the influence of phenomena (including chromatic dispersion, amplified spontaneous emission noise and other). Using the VPIphotonics software, a simulation model of the ADTS technique was built. After the simulation tests, 10 000 images were obtained, which after proper preparation were subjected to further analysis using CNN algorithms. The main goal of the study was to train a CNN to recognize the selected impairment (distortion); then to test its accuracy and estimate the impairment for the selected set of test images. The input data consisted of processed binary images in the form of two-dimensional matrices, with the position of the pixel. This article focuses on the analysis of images containing simultaneously the phenomena of chromatic dispersion and optical signal to noise ratio.

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