Search

This paper discusses the possibility of analyzing the orchestration protocol used in gigabit-capable passive optical networks (GPONs). Considering the fact that a GPON is defined by the International Telecommunication Union Telecommunication sector (ITU-T) as a set of recommendations, implementation across device vendors might exhibit few differences, which complicates analysis of such protocols. Therefore, machine learning techniques are used (e.g., neural networks) to evaluate differences in GPONs among various device vendors. As a result, this paper compares three neural network models based on different types of recurrent cells and discusses their suitability for such analysis.

This work is focused on creating an open-source software-based solution for monitoring traffic transmitted through gigabit passive optical network. In this case, the data are captured by the field-programmable gate array (FPGA) card and reassembled using parsing software from a passive optical network built on the International Telecommunication Unit telecommunication section (ITU-T) G.984 gigabit-capable passive optical network GPON recommendation. Then, the captured frames are converted by suitable software into GPON frames, which will be further processed for analysis. Due to the high transfer rate of GPON recommendations, the work describes the issue of writing to the Mongo database system. In order to achieve the best possible results and minimal loss of transmitted frames, a series of tests were performed. The proposed test scenarios are based on different database writing approaches and are implemented in the Python and C# programming languages. Based on our results, it has been shown that the high processing speed is too high for Python processing. Critical operations must be implemented in the C# programming language. Due to rapid application development, Python can only be used for noncritical time-consuming data processing operations.

As gigabit passive optical networks (GPONs) are currently deployed, the future requires next-generation passive optical networks (NG-PONs) but the date of full replacement is not known yet. This work gives an overview of historical development of passive optical networks (PONs) and discusses the differences and implementation of the transmission convergence layer between GPON and NG-PON networks. The article provides a case study with the future design of storage for storing traffic from a real optical network based on database systems.

The paper presents the latest research results of gigabit passive optical network analysis using machine learning algorithms. TensorFlow has been used to learn the frames of GPON management traffic of the G.984.3 protocol and detect outliers to the standard. The frames are captured by FPGA programmable network card and processed with a software parser and further processed with the TensorFlow using JSON format. The proposed technique can bring a way how to test if vendors of network devices follow the standard and if the content of the frames can be treated as secure and trustworthy.

Passive optical networks (PONs) offer a high network capacity and densified coverage at the cost of high capital expenditure. That fact is why operators use a shared infrastructure with one fiber for more customers in a split ratio. Deploying a passive optical network requires capital, but the operators can use a simulation tool to verify the physical layer parameters. The higher layers are less relevant for operators than for researchers. In this paper, we provide a basic simulation topology with downstream and upstream services. In real networks, the services control optical line termination (OLT), but in a simulation, it requires a router after optical line termination. We demonstrate a comparison of the time and memory complexity for low-rate services in 10G passive optical networks with different split ratios in Network Simulator 3 (NS-3).

This article presents a method for data analysis of GPON frames transmitted over passive optical networks using the gigabit encapsulation method. First, the introduction is described. The following sections are focused on individual parts of our designed system, including capturing frames, storing frames, analysis and interpretation of results.

This work addresses security risks in a gigabit passive optical network. The completely passive infrastructure enables active monitoring of the distribution network in parallel with data communication. The specification of the gigabit passive optical network allows an occurrence of a risk called a modified end unit. Such a unit does not respect allocated time slots, and as a result, all communication becomes disrupted in both downstream and upstream directions. Generally, the problem can be specified as an attack on a specific port of the control unit. In this work, the modified unit is replaced by a laser with a wavelength of 1310 nm and tested at different power levels in the direction of degrading the port's operational capabilities.

This article deals with transmission convergence layer of gigabit passive optical networks. Transmission convergence layer takes the responsibility for transferring of frames from optical line termination to optical network units and vice versa. Gigabit passive optical networks do not transfer Ethernet frames but they use a proprietary encapsulation method. Each frame has the fixed time duration of 125 $\mu$s. In this article, we demonstrate an analysis of these frames in the downstream direction with our parser. Thanks to the parser, we are able to analyze control messages in downstream direction with bandwidth allocation and activation process of ONU.

This article is a summary of our participation in a project focused on the development of a programmable FPGA network card for gigabit-capable passive optical networks. The network card is constructed to analyze GPON frames and check the correctness of communication between an optical line terminator and an optical network unit directly in the optical domain. The GPON networks use a specific encapsulation method for Ethernet frames and control messages. Because of the network card operates directly in the optical domain, it is possible to provide real-time analysis of headers of GPON transmission convergence layer. Within this article, a basic schema and functionality of the FPGA network card are described, and proposals for the continuous research and development are presented at the end.

This article presents a design of a database model used to gather and analyze data frames transmitted over gigabit passive optical network (GPON) in the downstream direction. An issue with this kind of system is the difficulty in analyzing a transmission on the optical part which is caused by the difference among devices using Ethernet frames technology and passive optical network technology with usage gigabit encapsulation method. In this article, a principle of the downstream direction is described. Next, the design of the database model for the analysis of transmitted data is discussed. Based on the design and implementation of the database, a script capable of processing data gathered by a programmable network card is proposed. The script for physical layer operation, admission, and maintenance (PLOAM) messages analysis is written in the Python programming language.

Passive optical networks are a suitable and promising solution for the service delivery network, particularly for data, video, and voice communications. An optical distribution consists of passive elements (e.g., splitters, fibers), providing a cost-effective high bandwidth tree-like topology. This paper describes a new analysis system designed for an inspection of passive optical networks. The system uses an FPGA-accelerated network interface card to capture the communication from the optical network and machine learning models in order to evaluate the possible anomalies, errors, or attacks.