You need Adobe Reader 7.0 or later in order to read PDF files on this site.
If Adobe Reader is not installed on your computer, click the button below and go to the download site.

Feature Articles: Network Technology for Digital Society of the Future―Research and Development of Competitive Network Infrastructure Technologies

Vol. 17, No. 6, pp. 33–36, June 2019. https://doi.org/10.53829/ntr201906fa10

Per-device Policy Control Technology Using Artificial Intelligence

Hidetaka Nishihara, Hiroki Iwahashi, Kaori Kurita,
Kazuhiro Matsuo, Hirofumi Noguchi, Takuma Isoda,
Misao Kataoka, and Yoji Yamato

Abstract

The number and variety of Internet of Things (IoT) devices such as network cameras and televisions connecting to networks has been increasing recently, and the network requirements for each of these devices are also diversifying. This article introduces policy control technology for controlling networks in the IoT era that is optimized for each device by automatically identifying the type and/or model of each device from its communication behavior.

Keywords: IoT, policy control, device identification

1. Introduction

The development of the Internet of Things (IoT) is continuing, with all kinds of objects connecting and communicating with networks. As this development progresses and 4K/8K video gains popularity, new usage scenarios and applications are emerging, and the devices being used are becoming increasingly diverse. The quality requirements and communication characteristics are also becoming more diverse, for example, high-speed and high-capacity transmission, or low-capacity but capable of transmitting many sessions.

NTT Network Service Systems Laboratories is conducting research and development on a policy control technology for the IoT era for implementing network control optimized to the type and characteristics of each device connected to the network that flexibly handles the network requirements as devices increase in capacity and diversity. We introduce here the overall system and two of its component technologies.

2. Policy control technology

The policy control technology identifies the traffic from an application or particular user and controls traffic according to individual control rules (policies) based on the results, to provide flexible, value added services that meet the diverse needs of users. Examples of types of control include filtering, quality of service, and bandwidth control.

The policy control technology is implemented by two main functions called the Policy and Charging Enforcement Function (PCEF) and the Policy and Charging Rules Function (PCRF), and these are located within the carrier network. The PCRF decides which policies to apply based on the user or application, and the PCEF executes control instructions. The PCEF identifies input traffic and applies appropriate control according to instructions from the PCRF.

3. Automatic device behavior analysis

We have been developing a means of automatic device behavior analysis. It identifies the type and model of a device based on communication data. For example, it can identify network cameras and displays on a home or office network.

Identifying the type of a device makes it possible to optimally operate networks and efficiently manage huge numbers of devices. The automatic device behavior analysis does not require high performance computers or special software because it only uses ordinary communication data from devices. The technology performs two stages of processing (Fig. 1): extraction of features from communication data, and classification based on the similarity of features.

Fig. 1. Overview of device identification.

In the first process—extraction of features—the system collects packets from a device at regular time intervals and creates abstracted data from it using information such as the packet length and the number of packets. The abstracted data are used as device feature data in the next process.

In the second process—classification based on similarity of features—the system compares feature data prepared for each device type and model with the feature data from devices to be identified, to determine which is most similar. This process uses machine learning to derive similarity of devices from large volumes of feature data.

This processing enables the system to identify the type and model of devices.

4. Overall system

With conventional policy control technologies, control is performed at the level of subscriber lines. For this reason, individual devices cannot be identified from the network side if a subscriber line accommodates multiple devices. Therefore, we added a way for the PCRF to automatically analyze behavior and select policies for each device from the device identification results. The overall architecture of this system is shown in Fig. 2. Specifically, an artificial intelligence (AI) component with the automatic device behavior analysis is placed on the carrier network, mirrored packets are input to the AI, and (1) device types and models are identified by the automatic device behavior analysis. After the identification by the AI is completed, the PCRF is notified of the subscriber line information (source IP (Internet protocol) address etc.) and corresponding results, and (2) the PCRF uses this information to automatically select optimal policies from per-device policies associated with the subscriber line prepared beforehand. The PCRF then instructs the PCEF to apply the relevant policies to communication from each of the devices.

Fig. 2. IoT era policy control system architecture.

Use of this technology allows individual devices on a subscriber line to be identified, and network control that is optimized for each device to be implemented.

5. Future prospects

With the increasing number and diversity of devices connected to networks, we have introduced automatic device behavior analysis, which identifies the types and models of devices from their communication behavior, and policy control technology, which implements network control optimized to each device, based on this information.

Various methods are possible for creating feature values from communication data and for determining similarity in the automatic device behavior analysis, so we will continue working to improve the technology and create services, keeping in mind the networks to which it will be applied.

For the policy control technology, service providers providing services to users will continue to study design and configuration of new per-device policies, and we are studying an application programming interface that will enable flexible provision of services.

	Hidetaka Nishihara Researcher, Transport Service Platform Innovation Project, NTT Network Service Systems Laboratories. He received a B.S. from Kyoto University in 2011 and an M.S. in physics from the University of Tokyo in 2013. He joined NTT EAST in 2013. He has been with NTT Network Service Systems Laboratories since 2015, where he is studying policy control technology to achieve flexible traffic control by identifying applications from traffic. He is currently engaged in research and development (R&D) of 5G (fifth-generation mobile communications) transport with network slicing.
	Hiroki Iwahashi Researcher, Transport Service Platform Innovation Project, NTT Network Service Systems Laboratories. He received an M.S. in information science and technology from Osaka University in 2014. He joined NTT Network Service Systems Laboratories in 2014, where he is studying policy control and charging technology that achieves flexible traffic control based on each user’s contract, amount of traffic, and applications. He is currently engaged in R&D of new edge router functions and architecture for the next core network.
	Kaori Kurita Research Engineer, Transport Service Platform Innovation Project, NTT Network Service Systems Laboratories. She received an M.E. from the Graduate School of Information Science, Nara Institute of Science and Technology in 2006. She joined NTT Network Service Systems Laboratories in 2006 and worked on the subscriber service edge router development project. She was in charge of maintaining and developing edge router products at NTT EAST from 2015 to 2017. She returned to NTT Network Service Systems Laboratories in 2017 and is currently researching policy and charging control methods.
	Kazuhiro Matsuo Senior Research Engineer, Transport Service Platform Innovation Project, NTT Network Service Systems Laboratories. He received a B.E. in systems engineering in 2002. He joined NTT Communications in 2002. He has been with NTT Network Service Systems Laboratories since 2016, where he is researching network traffic control technology using AI.
	Hirofumi Noguchi Researcher, Network Systems Planning & Innovation Project, NTT Network Service Systems Laboratories. He received a B.S. and M.S. in mechanical engineering from Waseda University, Tokyo, in 2010 and 2012. He joined NTT in 2012, where he has been engaged in developmental research of server virtualization and IoT.
	Takuma Isoda Researcher, Network Systems Planning & Innovation Project, NTT Network Service Systems Laboratories. He received a B.S. and M.S. in physical engineering from Nagoya University, Aichi, in 2016 and 2018. He joined NTT in 2018. He is currently involved in R&D of an IoT platform.
	Misao Kataoka Researcher, Network Systems Planning & Innovation Project, NTT Network Service Systems Laboratories. She received a B.S. and M.S. in informatics from Kyoto University in 2012 and 2014. She joined NTT EAST in 2014, where she was engaged in simplifying networks. She has been carrying out developmental research of a distributed processing platform and IoT platform at the NTT laboratories since 2016.
	Yoji Yamato Distinguished Researcher, Network Systems Planning & Innovation Project, NTT Network Service Systems Laboratories. He received a B.S. and M.S. in physics, and a Ph.D. in general systems studies from the University of Tokyo in 2000, 2002, and 2009. He joined NTT in 2002, where he has been conducting developmental research on a cloud computing platform, peer-to-peer computing, and an IoT platform. Dr. Yamato is a senior member of the Institute of Electrical and Electronics Engineers and the Institute of Electronics, Information and Communication Engineers, and a member of the Information Processing Society of Japan.

↑ TOP