Machine Learning for Cognitive Optical Network Security Management

This talk surveys the security threats pertinent to the optical network and outlines the progress and challenges in developing machine learning approaches for cognitive management of optical network security

Transmitting audio via fiber optics under nonlinear effects and optimized tuning parameters based on Co-simulation of matlab and optisystemTM

Limitations of conventional wires such as copper wires are causing dispersion and distortion of the message signal for long distances communication especially for the wide bandwidths. The ability of fiber optic to overcome this problem is making it a dominant transmission medium. Despite of this major positive attribute of optic fibers, there is still a downside for using the fiber optic communication; that is the nonlinearity problem especially at the very high frequency bandwidth. For the first time, a desigen of an audio signal is suggested and executed in MatLab with an integration with OptiSystemTM software to discuss and solve this issu. The audio signal is then transmitted in different shapes of modulation signals (NRZ, RZ & RC) for different distances (100 km & 75 km) via a fiber optic media to be received in a receiving part of the simulated system. Three tests are used to do so. The first is the Quality-factor (Q-Factor) against the received power, second test is eye diagram performance and finally is the measuring of the amplitude of output (received) signal for each modulation signal shape using the Oscilloscope Visualizer. The NZR modulation signal was found to be the best one of the three used signals’ types in all three tests. The Q-factor for NRZ pulse shape (=12) was higher than that for RZ (=10) and RC (=8) for a 100 km distance at the same received power level

Improvements on the performance of subcarrier multiplexing/wavelength division multiplexing based radio over fiber system

Radio over fiber (RoF) techniques are good candidates to create the backbone of the next generation of wireless networks. Many parameters affect RoF communications such as amplified spontaneous emission noise (ASE), four-wave mixing nonlinearity (FWM), the modulation, channel spacing, switching voltage, and phase shifter. In this paper, we propose an improved model of RoF communication systems using subcarrier multiplexing/wavelength division multiplexing (SCM/WDM) technique with unequal channel spacing and 1-km Erbium-doped fiber amplifier (EDFA). Simulation results confirmed that we could obtain the lowest bit error rate and noises when the EDFA is placed at 1 km from the transmitter by using optical single-sideband (OSSB) modulation at frequencies 193.1 THz, 193.2 THz, 193.35 THz, and 193.6 THz

Remote optical powering using fiber optics in hazardous environments

Potential niches for a power-over-fiber (PoF) technique can be found in hazardous areas that require controlling unauthorized access to risk areas and integration of multiple sensors, in scenarios avoiding electromagnetic interference, and the presence of ignition factors. This paper develops a PoF system that provides galvanic isolation between two ends of a fiber for remotely powering a proximity sensor as a proof of concept of the proposed technology. We analyze scalability issues for remotely powering multiple sensors in a specific application for the hazardous environment. The maximum number of remote sensors that can be optically powered and the limiting factors are also studied; considering different types of multimode optical fibers, span lengths, and wavelengths. We finally address the fiber mode field diameter effect as a factor that limits the maximum power to be injected into the fiber. This analysis shows the advantages of using step-index versus graded-index fibers.This work was supported in part by the Spanish Ministerio de Economía, Industria y Competitividad, Comunidad de Madrid and H2020 European Union Programme under Grants TEC2015-63826-C3-2-R and S2013/MIT-2790, in part by FSE, and in part by 5G PPP BlueSpace Project under Grant 762055.Publicad

Design of AXI4-Stream based Modulator IP Core for Visible Light Communication System-on-Chip

In this paper, the design of AXI4-Stream based modulator IP core for Visible Light Communication is reported. The modulator IP core conforms to the AXI4-Stream protocol standard, which is widely used in System-on-Chip (SoC) design. There are three modulation types in this IP core namely, Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), and Quadrature Amplitude Modulation-16 (QAM-16). These modulation types are commonly used in DCO-OFDM system. The modulation types can be selected programmatically from software that runs in main processor by accessing the control register. The output of the modulator is designed for DCO-OFDM modulation using 64-point IFFT. According to the simulation results, this modulator IP core can achieve a throughput of 95.36 Mb/s, 184.77 Mb/s, and 347.81 Mb/s for BPSK, QPSK, and QAM-16, respectively. This modulator IP core is reusable in DCO-OFDM system, so it increases productivity in DCO-OFDM system design

Optical Network Security Management: Requirements, Architecture and Efficient Machine Learning Models for Detection of Evolving Threats [Invited]

As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing a novel functional block called Security Operation Center (SOC), describing its architecture, specifying key requirements on the supported functionalities and providing guidelines on its integration with optical layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between ML accuracy and run time complexity

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This paper focuses on the challenges related to the performance of ML-based approaches for detectionand localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL) and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation\ua0guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date