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Network Machine Learning Research Group                    S. Jiang, Ed.
Internet-Draft                                                    B. Liu
Intended status: Informational              Huawei Technologies Co., Ltd
Expires: December 5, 2016                                 P. Demestichas
                                                   University of Piraeus
                                                             J. Francois
                                                                   Inria
                                                             G. M. Moura
                                                               SIDN Labs
                                                               P. Barlet
                                                       Network Polygraph
                                                            June 3, 2016


 Use Cases of Applying Machine Learning Mechanism with Network Traffic
             draft-jiang-nmlrg-traffic-machine-learning-00

Abstract

   This document introduces a set of use cases in which machine learning
   technologies are applied to network traffic relevant activities,
   including machine learning based traffic classification, traffic
   management, etc.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
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   Internet-Drafts are draft documents valid for a maximum of six months
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   This Internet-Draft will expire on December 5, 2016.

Copyright Notice

   Copyright (c) 2016 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents



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   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Methodology of Learning from Traffic  . . . . . . . . . . . .   4
     3.1.  Data of the Network Traffic . . . . . . . . . . . . . . .   4
     3.2.  Data Source and Storage . . . . . . . . . . . . . . . . .   5
     3.3.  Architecture Considerations . . . . . . . . . . . . . . .   5
     3.4.  Closed Control Loop . . . . . . . . . . . . . . . . . . .   6
   4.  Use Cases Study of Applying Machine Learning in Network . . .   6
     4.1.  HTTPS Traffic Classification  . . . . . . . . . . . . . .   6
     4.2.  Malicious Domains: Automatic Detection with DNS Traffic
           Analysis  . . . . . . . . . . . . . . . . . . . . . . . .   9
     4.3.  Machine-learning based Policy Derivation and Evaluation
           in Broadband Networks . . . . . . . . . . . . . . . . . .  10
     4.4.  Traffic Anomaly Detection in the Router . . . . . . . . .  11
     4.5.  Applications of Machine Learning to Flow Monitoring . . .  12
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  15
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  15
   7.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  15
   8.  Change log [RFC Editor: Please remove]  . . . . . . . . . . .  16
   9.  Informative References  . . . . . . . . . . . . . . . . . . .  16
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  17

1.  Introduction

   Machine learning technology has been successful in solving
   complicated issues.  It helps to make predictions or decisions based
   on large datasets.  It could also dynamically adapt to varying
   situations and response to real-time issues.  Therefore, more and
   more research starts on applying machine learning in the network
   area.

   Among many aspects of networks, the network traffic is one of the
   most complicated managed objectives.  Its volume is rapidly growing
   along with the Internet explosion.  It is always dynamically
   changing.  Most network traffic flows only last a few minutes, or
   even shorter.  And the user contents within traffic is becoming more
   diverse due to the development of various network services, and
   increasing use of encryption.  Consequently, it is more and more



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   challenging for administrators to get aware of the network's running
   status and efficiently manage the network traffic flows.  Although
   more and more data regarding network traffics are generated,
   traditional mechanisms based on pre-designed network traffic patterns
   become less and less efficient.

   It is natural to utilize powerful machine learning technology to
   analyze the large mount of data regarding network traffic, to
   understand the network's status, such as performance, failures,
   security, etc.  It is a big advantage that machines can measure and
   analyse the network traffic, then report the results and predictions
   to humans for further decision.  The machines could handle vast
   amounts of data which is almost impossible for humans to deal with,
   in close to real time.  Even more, if the speed and accuracy of the
   prediction is high enough, it is possible that the subsequent action
   based on the prediction result could form a closed control loop to
   achieve autonomic management.  However, the maturity of latter might
   be far in the future.  Today, the traditional control programs still
   look more reliable than machine learning based control mechanisms.

   This document firstly analyzes the data of the network traffic from
   various perspectives; and also discusses several important practical
   considerations, including the training data source, data storage and
   the learning system architecture.  It then introduce a set of use
   cases, which have been shown to work well although there is large
   scope for improvements, including ML-based traffic classification,
   traffic management, interface failure prediction, etc.

   Editor notice: this document is in the primary stage.  It collects
   the use cases presented in the proposed Network Machine Learning
   Research Group (NMLRG) session in IETF95 meeting.

2.  Terminology

   The terminology defined in this document.

   Machine Learning  A computational mechanism that analyzes and learns
      from data input, either historic data or real-time feedback data,
      following a set of designed features and algorithms.  It can be
      used to make analysis, predictions or decisions, rather than
      following strictly static program instructions.

   Network Traffic  The amount of data moving across a network at a
      given point of time.  They are mostly encapsulated in network
      packets.

   Traffic Flow  A sequence of packets from a source computer to a
      destination [RFC6437].  It is the unit of network traffic.



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   Feature (machine learning)  In machine learning and pattern
      recognition, a feature is an individual measurable property of a
      phenomenon being observed.  Choosing informative, discriminating
      and independent features is a crucial step for effective
      algorithms in pattern recognition, classification and regression.

   Algorithm (machine learning)  Machine learning algorithms operate by
      building a model from example inputs in order to make data-driven
      predictions or decisions expressed as outputs, rather than
      following strictly static program instructions.  A incomplete list
      of machine learning algorithms includes supervised learning,
      unsupervised learning, semi-supervised learning, reinforcement
      learning, deep learning, etc.

3.  Methodology of Learning from Traffic

3.1.  Data of the Network Traffic

   There is plenty of valuable data related to the network traffic.
   These data are raw features in learning process.  Following is a
   simple classification of network traffic data.

   Measurable properties  There are many measurable properties of
      network traffic, such as latency, number of packets, duration,
      etc.  These properties are also very essential features,
      especially for use cases relevant to performance, QoS (Quality of
      Service), etc.

   Data within communication protocols  The user contents are
      encapsulated in layered communication protocols.  Many information
      are contained within the protocol headers, for example the source
      and destination IP addresses in the IP header, the port numbers in
      the TCP/UDP header, etc.  Transport layer protocols are often
      related to the type of applications, such as FTP (File Transfer
      Protocol) for file transfer, HTTP (Hyper Text Transfer Protocol)
      for web, etc; and many application-relevant data are embedded
      within these protocols.  These could also be essential data for
      classification or application-oriented analysis.  However, some
      traffic will not provide transport or application information, due
      to unknown protocols or encryption.

   User content  User contents are the payload of packets, which might
      be obtained by DPI (Deep Packet Inspection) within the transit
      network if the packets are unencrypted, or they could be analyzed
      by the source or destination nodes.

   Data in network signaling protocols  Traffic flows are managed or
      indirectly influenced by various network signaling protocols.  For



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      example, the routing protocols determine the next hop of a
      specific network traffic flow, or even the traffic path (by some
      sophisticated routing protocol such as MPLS-TE (Multi-Protocol
      Label Switching - Traffic Engineering), segment routing, etc.);
      the P2P (Peer to Peer) protocol can even decide the destination of
      a specific content traffic.  They are relevant and are potential
      features for traffic analysis.  Furthermore, the traffic of these
      signaling protocols themselves may also be learning objectives.

3.2.  Data Source and Storage

   Within networks, forwarding devices such as routers, switches,
   firewalls, etc., are the entities that directly handle the network
   traffic.  Thus, they could collect network traffic data, such as
   measurable properties, protocol information, etc.  Source nodes or
   destination nodes, particularly servers, could also be the source of
   network traffic data.  They could either report the collected data to
   a central repository for storage and learning, or collect and store
   the data by themselves for local learning.  This depends on the
   learning architecture, which is discussed in the following section.

3.3.  Architecture Considerations

   Global learning vs. local learning

      *  Global learning refers to the tasks that are mostly network-
         level, so that they need to be done in a global viewpoint.  In
         this case, the learning entity is normally centralized and is
         different from the data source entities.

      *  Local learning is more applicable to the tasks that are only
         relevant to one or a limited group of devices, and they could
         be done directly within that one node or that limited group of
         nodes.  In this case of grouped nodes, the data may also need
         to be transited from the data source entity to learning entity.

   Offline & online learning

      *  Co-located mode: training (offline, based on historic data) and
         prediction (online, based on real-time data) are both done
         within the same entity.  The entity could be a central
         repository or a specific node.

      *  De-coupled mode: training is done in the central repository,
         and prediction is made by the routers/switches/firewalls or
         other devices that directly process the network traffic.





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   Central learning & distributed learning  Central learning means the
      learning process is done at a single entity, which is either a
      central repository or a node.  Distributed learning refer to
      ensemble learning that multiple entities do the learning
      simultaneously and ensemble the results together to sort out a
      final results.  Since network devices are naturally distributed,
      it could be foreseen that ensemble learning is a good approach for
      a certain of use cases.

3.4.  Closed Control Loop

   The prediction made by machine learning mechanism could be directly
   used on manipulating the network traffic, or other relevant actions,
   such as changing the device configuration, etc.

   However, as the introduction section said, this kind of utilization
   might be suitable only for a small set of the use cases, due to the
   limited accuracy of machine learning technologies.  Besides, some
   critical usages simply cannot tolerate any false decision.

4.  Use Cases Study of Applying Machine Learning in Network

   Editor notes: This section is a collection of the work presented in
   the proposed NMLRG session in IETF95 meeting.  More contributions on
   use cases are welcome.

4.1.  HTTPS Traffic Classification

   Managing network traffic requires a good understanding of the content
   of traffic flows for various purposes.  Indeed, enhancing the QoS by
   prioritizing or scheduling the flows or enforcing security policies
   by filtering some of them cannot solely on rely protocol headers like
   IP, TCP or UDP headers.  Analyzing the user content with DPI is so
   necessary.  However, this poses serious concerns regarding the user
   privacy.  In addition, OTT (Over-the-Top) actors would prefer to
   fully control their network traffic rather than being subject to any
   intermediaries policies.  As a result, encrypting the traffic has
   been widely adopted in last years.

   In that context, traffic management is facing to severe difficulties
   since DPI is not efficient anymore.  Using an intermediary service or
   proxy are the only ways to analyze the content of encrypted traffic
   but it requires a high trustfulness in the intermediaries and so not
   always guaranteed, for example with end-users of an operator
   networks.

   Therefore, new techniques wit the ability to extract knowledge and
   insight from encrypted flows is necessary.  Especially HTTPS



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   [RFC2818] is now a major protocol use over Internet because it
   provides secure Web communication while Web is now embracing various
   services which have been provided apart in the past: email, video
   streaming, chat, VoIP, file sharing, etc.  It relies on TLS
   (Transport Layer Security) [RFC5246], [RFC6066] to encapsulate HTTP
   requests.

   Being able to identify the service and the providers of an HTTPS
   connection would help in applying different strategies for managing
   the corresponding flow.  For instance, VoIP (Voice over IP) and email
   do not require the same QoS or some service use might be prohibited
   like file sharing to avoid data leakage in a company.

   As a concrete example, Google, Facebook or Amazon are service
   providers while maps, drive, gmail are services of Google.  To
   identify them when they are accessed by a user, IP addresses and DNS
   (Domain Name System) names based identification is not reliable as
   the users can relies on intermediates to respectively serve as proxy
   or resolve DNS requests.  The SNI (Server Name Indication) [RFC5246]
   is an extension of HTTPS which is indicated by the user when
   initiating the TLS handshake (Client Hello).  SNI actually contains
   the hostname to which the request is addressed.  Such an hostname is
   significative of the service and service provider name.  However, SNI
   is an optional field and can be easily forged to circumvent HTTPS
   filtering without impacting service use [bypasssni].  More advanced
   mechanisms are hence necessary to improve the robustness of
   identification even in the case of non collaborative users.

   Because the objective is to automatically label an HTTPS connection
   by the service and service provider associated with.  The TLS
   handshake is not encrypted but data exchanged during this phase
   (random number, selected ciphers,...) is not distinctive of the
   accessed service.  However, the nature of accessed service directly
   impacts on user content transmitted through the secure channel
   especially on the type, size and way to transmit those data.  Such
   metadata are still measurable properties.















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     HTTPS Connection
           +
           |(1)
   +-------v------+
   |TLS Connection|
   |Reconstruction|
   +-------+------+
           |(2)
   +-------v------+    (3')                    (4')
   |  Features    +-------------+----------------------------+
   |  Extraction  |             |                            |
   +-------+------+     +-------v---------+             +----v----+
           |            |Service Provider +------------->Services |
           |(3)         |L1 model         |   Load      |L2 model |
           |            +-------^---------+   services  +----^----+
   +-------v------+             |             model X        |
   |SNI Labelling |             +----------------------------+
   +-------+------+                         |(5)
           |            +-----------------------------------------+
           +------------>              Training and               |
                   (4)  |              Models building            |
                        +-----------------------------------------+

   Two-levels HTTPS traffic classification

   In figure above, step(1) consists in reconstructing the HTTPS
   connection and retrieving packets on top of which the following
   metrics are observed (2):

   o  Inter Arrival Time

   o  Packet size

   o  Encrypted data size: this feature has the advantage to be strongly
      related to the service accessed instead of the packet size which
      is biased by other lower layer headers

   Based on these values, aggregated features are computed: average,
   minimum, maximum, 25th percentile, median, 75th percentile.

   Because different providers may offer a similar service, a single
   classifier could fail to to distinguish them.  A multi-level machine
   learning approach has been proposed.  For learning, a dataset without
   forged SNI is used (3) to build the classifiers (4).  The result is
   (5):

   o  a first level model (L1 model) whose the goal is to identify the
      service provider,



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   o  a set of second level models (L2 models), one for each service
      provider to identify specific service of a service provider

   Once all classifiers are trained, a new unknown HTTPS connection is
   first matched against the LV1 model (3').  The output is the
   predicted service provider but also leads to load the corresponding
   LV2 model (4') to determine the specific service of this service
   provider.

   This framework is independent of the ML technique. being used.  Each
   model could be also built with a different technique but our study
   have shown that best results are obtained with Random Forest.

   The HTTPS classification framework has been tested over 288,901
   connections from lab users.  Standard evaluation procedure have been
   applied.  Less representative features have been automatically
   discarded.  Using a ten-fold cross-validation, each tested connection
   has been marked as perfect identification (both the service provider
   and the service name are rightly identified), partial identification
   (only the service provider is identified) or invalid (none of them).
   93.1% falls in the first category, 2.9% in the second and the rest in
   the third.  Full results are available in [httpsframework].

   Although results are promising, the current method can only be
   applied at the end once the HTTPS connection, i.e. after being
   reconstructed.  This avoids to apply any kind of policies to the
   corresponding traffic flow.  Future challenge is thus to classify the
   connection before it ends in order to apply.

4.2.  Malicious Domains: Automatic Detection with DNS Traffic Analysis

   Since their inception, domain names have been used to provide a
   simple identification label for hosts, services, applications, and
   networks on the Internet [RFC1034].  In the same way, domains and the
   DNS infrastructure have also been misused in various types of abuses,
   such as phishing, spam, malware distribution, among others.

   Newly registered malicious domain names are well-know to a very
   distinct initial DNS lookup pattern than legitimates ones: typically,
   they exhibit an abnormally higher number of lookups [Hao2011].  One
   of the reasons is that malicious domains tend to rely upon spam
   campaigns within the first ours after the registration of these
   domains in order to maximize the number of victims before the domain
   is detected and taken down.

   In order to protect users from such domains, nDEWS (New Domains Early
   Warning System) [Moura2016], a tool that classifies the newly
   registered domains based on their initial lookup pattern, has been



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   proposed.  To perform that, it is required to have access to (i) a
   domains registration database and (ii) authoritative DNS server
   traffic data, which is typically the case for Top-Level Domains (TLD)
   registries.  These domains are classified using k-means as a
   clustering method into two clusters using four features extracted
   from the analyzed DNS traffic: # DNS queries, # IP addresses, #
   Autonomous Systems (ASes), and # Countries, which were chosen
   empirically.

   As a result, in an automated fashion, a large variety of suspicious
   domains can be detected, including phishing, malware, but also other
   types, such as fake pharmaceutical shops as well as counterfeit
   sneakers.  In this particular case, the responsible registrars are
   notified in this pilot study about these websites.  Ultimately, it
   allows these websites to be taken down, minimizing the potential
   number of victims.

4.3.  Machine-learning based Policy Derivation and Evaluation in
      Broadband Networks

   Service provisioning is becoming more complex.  For instance, there
   are services having diverse quality requirements, there is variance
   of the requirements in time and space, and there is the need for
   utmost resource efficiency.  Moreover, full agility in time and space
   (in order to accomplish resource efficient service provisioning)
   requires the solution of computationally intensive tasks.  In this
   respect, policies can play a role: specify the network behaviour in
   time periods and service area regions.

   In this direction, machine learning can have a fundamental role,
   e.g., for learning situations encountered and "good" ways (policies)
   for handling them.  The contribution addresses the role that machine
   learning can play for policy derivation and evaluation.  In more
   detail it addresses the requirements on the role of machine learning,
   including potential inputs and outputs.

   Knowledge and machine learning can be an important aspect of wireless
   networks.  Knowledge is created both regarding the contexts and their
   occurrence, as well as on the association of the context with
   specific actions and its scoring.  The latter encompasses development
   of knowledge on how to handle acquired contexts; this knowledge will
   include the contexts encountered, the corresponding handlings done
   (decisions applied), the potential alternative handlings, and the
   respective efficiency of each handling (actually applied or
   alternate).

   Reinforcing "good" solutions per each encountered context (e.g.
   reinforcement learning) can be a vital and unique element of a



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   knowledge-based management system.  Machine learning can be realized
   through clustering to discover underlying structures in data,
   regression to identify patterns and predict values in cell and
   network usage, classification to classify first-seen unknown users,
   and density estimation to model complex user behavior and network
   usage.  Several deep architectures and techniques (such as pre-
   training) can be utilized, in order to generalize better on complex
   data with underlying information and be able to make accurate
   predictions, even on unseen data.

   As a result, depending on what we want to achieve, the proper machine
   learning approach can be used.

   Through machine learning it will be possible to provide faster and
   targeted solutions to specific network problems.  Moreover, it is
   possible cluster various usage profiles and prioritize the traffic
   according to the criticality level.  For instance, mission critical
   services need special attention with respect to latency and
   prioritization, compared to plain services which may tolerate a bit
   of delay without jeopardizing the overall quality.  In addition,
   machine learning can lead to improved results in KPIs (Key
   Performance Indicator) such as end-user throughput, latency, energy
   consumption and overall cost effectiveness.  Moreover, reliability
   can be increased since certain problematic situations may be
   predicted before happening, hence it will be possible to act pro-
   actively and alleviate the negative impact of a problem in the
   network.

   It is evident that machine learning can have significant importance
   in policy derivation and evaluation in broadband networks, especially
   towards in 5G infrastructures which will be complex, heterogeneous
   and need to accommodate multi-services ranging from mobile broadband
   to massive machine type, mission critical and vehicular
   communications.

4.4.  Traffic Anomaly Detection in the Router

   Modern routers usually have the capability that makes alarms of high
   bandwidth usage rate of a specific interface.  When network traffic
   exceeds a certain threshold, the router will consider it as an
   anomaly event and report it to the NMS (Network Management System).
   For instance, in some routers/switches, there exists configuration
   such as "trap-threshold { input-rate | output-rate }" to trigger
   traffic alarms, which is statically configured by experienced
   administrators.  However, network traffic is usually not static and
   even changes significantly due to the changes of carried services,
   residential situation, and etc.  Thus, static configuration could not
   effectively identify the traffic anomaly events.



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   To address above issue, machine learning technologies are applied for
   routers/switches to learn local traffic pattern and detect the
   traffic anomaly events based on the learning results.

   Wavelets are employed to analyze time-series network traffic for
   anomaly detection.  In some certain interval, the routers measure,
   record, and analyze the input and output traffic rates respectively,
   or in the form of rate sums.  (The former is recommended for a finer
   granularity analysis.)

   Running for some time, the router would get a set of "time-rate"
   data, collected as time-series waves for further wavelet analysis.
   Besides wavelets, this use case proposes other machine learning
   techniques such as outlier detection.  For this way, features are to
   be extracted from wavelets for supervised or unsupervised learning.

   After data collection, the router would sort up the data and figure
   out the alarm threshold statistically based on data distribution, to
   discriminate the normal and outlier traffic rates.  When interface
   traffic exceeds the threshold, the router would make alarms to the
   NMS.  The router could dynamically adjust the alarm threshold with
   new coming data, by periodical anomaly analysis.  This approach helps
   devices detect traffic anomaly more efficiently and effectively,
   compared to traditional way of learning at the central repository
   that collects traffic information from various devices.

   This use case could be extended from single interface to multiple
   ones, that is, device scope of multiple traffic waves, and even wider
   scope of multiple devices in a certain domain.  Thus would make the
   analysis more comprehensive.

   Besides wavelet analysis, there might be more techniques to explore,
   such as correlation analysis of traffic anomaly events among multiple
   devices.

4.5.  Applications of Machine Learning to Flow Monitoring

   A commercial cloud-based flow monitoring service from Network
   Polygraph [polygraph] has used Machine Learning analysis as a cost-
   effective alternative to DPI for traffic classification, which
   identifies the application responsible for each network traffic flow.

   Nowadays, DPI is considered as the standard technology for traffic
   classification.  However, DPI is generally expensive as it requires
   the analysis of the payload of every single packet.  This usually
   involves the use of powerful, specialized hardware appliances, which
   need to be deployed in every link to obtain full coverage of the
   network.  In the case of Network Polygraph, the use of DPI is



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   impractical, because the volume of data to be exported to the cloud
   would be overwhelming (i.e., all traffic should be replicated).  A
   more viable alternative is the use flow-based monitoring
   technologies, such as NetFlow [RFC3954] or IPFIX [RFC7011], where the
   volume of exported data is significantly lower.  Flow-based
   monitoring technologies provide summarized information (e.g.,
   duration, traffic volume) for every connection (or "traffic flow")
   handled by a router.  The information available in flow records is
   more limited compared to DPI (e.g., packet payloads are not
   available).  As a result, most flow-based monitoring tools base their
   classification on the port numbers or simple heuristics, which are
   known to be highly unreliable.

   To address this problem, Network Polygraph uses a traffic
   classification approach based on ML.  Several studies showed that
   supervised learning can achieve similar classification accuracy to
   DPI at a fraction of its cost.  However, supervised methods suffer
   from some practical limitations that make them very difficult to
   deploy and maintain in production environments.  For example, they
   require a costly training phase prior to its deployment and need to
   be frequently retrained, every time there is a change in the network
   or in the network applications.

   This section describes the ML approach used by Network Polygraph for
   online classification of NetFlow/IPFIX traffic.  To solve the
   practical limitations of supervised learning, Network Polygraph
   incorporates an automatic retraining system.  Figure 1 shows the
   components and data flow of the classification engine, which is
   divided in two parts:

   o  The classification path (Figure 1, top) is in charge of the
      classification of the traffic online using ML.  The input of the
      classification path are the NetFlow/IPFIX flows exported by the
      routers, while the output are the classified flows.  Several
      traffic features are extracted from each flow, including the
      information directly available in the flow records (e.g.,
      addresses, ports, packet and byte counts) together with some
      features we construct (e.g., average packet size, rate and
      interarrival time).  The traffic features are the input of the
      traffic classification algorithm, whose function is to identify
      the application that generated the flow.  Among the different
      supervised algorithms, a C5.0 decision tree was selected, because
      it has been shown to present the best accuracy/cost ratio for
      traffic classification.  Other supervised methods, e.g., Support
      Vector Machine (SVM) and Artificial Neural Network (ANN), obtain
      similar accuracy, but classification and training times are faster
      with decision trees.  In Network Polygraph, training times are




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      critical as the training path is continuously updating the
      classification model in the background.

   o  The training path (Figure 1, bottom) implements the automatic
      retraining system, which is responsible of automatically updating
      the classification model when it becomes obsolete.  To that end, a
      random packet-level sample of the network traffic is continuously
      collected using flow-based sampling.  Sampled flows are then
      labeled using DPI.  It is possible to use DPI in the training path
      because training can be performed only with a small data sample
      (e.g., 1/1000 flows).  This significantly reduces the
      computational overhead and volume of data to be exported.  The
      labeled sample is used to verify the accuracy of the
      classification model.  The system accuracy is estimated by
      comparing the output of DPI (training path) and C5.0
      (classification path) for those flows sampled in the training
      path.  If the estimated accuracy falls below a configurable
      threshold, the labeled sample is used to generate an updated model
      using only those features available in NetFlow/IPFIX (IP Flow
      Information Export) records.  This training process can also be
      performed in few vantage points, and use it for other networks
      where only NetFlow/IPFIX monitoring data is available.

CLASSIFICATION PATH

  NetFlow/ +----------+                         +----------+ Classified
   IPFIX   | Feature  |                         |   C5.0   |   flows
 +-------->|Extraction+------------------------>|Classifier+----------->
           |          |                         |          |
           +----------+                         +----------+
                                                      ^
                                                      |
TRAINING PATH         +----------+   +----------+     |
                      | NetFlow/ |   | Feature  |     | Retraining
                  +-->|  IPFIX   +-->|Extraction+--+  |
   Packet stream  |   |Generation|   |          |  |  |
  (flow sampling) |   +----------+   +----------+  |  |
 +--------------->|                                +--+ DPI-labeled
                  |           +----------+         |      NetFlow/
                  |           |   DPI    |         |       IPFIX
                  +---------->|   App.   +---------+
                              | Labeling |
                              +----------+

             Network Polygraph classification engine data flow

                                 Figure 1




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   In order to validate the performance of the described ML approach,
   the accuracy of Network Polygraph was measured using a complete
   14-day trace from the 10-Gigabit link that connects the Catalan
   Research and Education Network (Anella Cientifica) to its Spanish
   counterpart (RedIRIS).  The trace contained about 70 million flows
   with a flow sampling rate of 1/400.  The experimental results showed
   that, with a 96% retraining threshold, the system sustained an
   average classification accuracy of 97.5%, needing only 15 retrainings
   during the 14 days, which were performed automatically without
   requiring any human intervention.  When the retraining threshold was
   decreased to 94%, the accuracy was slightly reduced to 96.76% with
   only 5 retrainings.

   The target objective is to progressively reduce the dependence on DPI
   technologies, which are expensive, difficult to deploy, not scalable,
   and not robust against encryption, in favor of flow-based machine
   learning approaches that are more cost-effective and can be easily
   offered as a cloud service.  In this direction, some research
   challenges include the classification of web services and CDN traffic
   from flow-based measurements, and the combination of multiple ground
   truths obtained from vantage points in different networks.

5.  Security Considerations

   This document is focused on applying machine learning in network,
   including of course applying machine learning in network security, on
   higher-layer concepts.  Therefore, it does not itself create any new
   security issues.

6.  IANA Considerations

   This memo includes no request to IANA.

7.  Acknowledgements

   The authors would like to acknowledge Josep Sanjuas, Andreas
   Georgakopoulos, Kostas Tsagkaris, Valentin Carela, Wazen M.  Shbair,
   Thibault Cholez, and Isabelle Chrisment for their contributions.

   The author would like to acknowledge the valuable comments made by
   participants in the IRTF Network Machine Learning Research Group,
   particular thanks to Lars Eggert, Brian Carpenter, Albert Cabellos,
   Shufan Ji, Susan Hares, Rudra Saha, and Dacheng Zhang.

   Jerome Francois was partly funded by Flamingo, a Network of
   Excellence project (ICT-318488) supported by the European Commission
   under its 7th Framework Programme.




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   This document was produced using the xml2rfc tool [RFC7749].

8.  Change log [RFC Editor: Please remove]

   draft-jiang-nmlrg-traffic-machine-learning-00: original version,
   2016-06-03.

9.  Informative References

   [bypasssni]
              Shbair, W., Cholez, T., Goichot, A., and I. Chrisment,
              "Efficiently Bypassing SNI-based HTTPS Filtering", IFIP/
              IEEE International Symposium on Integrated Network
              Management (IM2015) , 2015.

   [Hao2011]  Hao, S., Feamster, N., and R. Pandrangi, "Monitoring the
              Initial DNS Behavior of Malicious Domains", Proceedings of
              the 2011 ACM SIGCOMM Conference on Internet Measurement
              Conference (IMC 2011) , Nov 2011.

   [httpsframework]
              Shbair, W., Cholez, T., Francois, J., and I. Chrisment, "A
              Multi-Level Framework to Identify HTTPS Services", IEEE/
              IFIP Network Operations and Management Symposium , 2016.

   [Moura2016]
              M. Moura, G., Mueller, M., Wullink, M., and C. Hesselman,
              "nDEWS: a New Domains Early Warning System for TLDs",
              IEEE/IFIP International Workshop on Analytics for Network
              and Service Management (AnNet 2016), co-located with IEEE/
              IFIP Network Operations and Management Symposium (NOMS
              2016) , 04 2016.

   [polygraph]
              "Network Polygraph", <https://polygraph.io>.

   [RFC1034]  Mockapetris, P., "Domain names - concepts and facilities",
              STD 13, RFC 1034, DOI 10.17487/RFC1034, November 1987,
              <http://www.rfc-editor.org/info/rfc1034>.

   [RFC2818]  Rescorla, E., "HTTP Over TLS", RFC 2818,
              DOI 10.17487/RFC2818, May 2000,
              <http://www.rfc-editor.org/info/rfc2818>.

   [RFC3954]  Claise, B., Ed., "Cisco Systems NetFlow Services Export
              Version 9", RFC 3954, DOI 10.17487/RFC3954, October 2004,
              <http://www.rfc-editor.org/info/rfc3954>.




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   [RFC5246]  Dierks, T. and E. Rescorla, "The Transport Layer Security
              (TLS) Protocol Version 1.2", RFC 5246,
              DOI 10.17487/RFC5246, August 2008,
              <http://www.rfc-editor.org/info/rfc5246>.

   [RFC6066]  Eastlake 3rd, D., "Transport Layer Security (TLS)
              Extensions: Extension Definitions", RFC 6066,
              DOI 10.17487/RFC6066, January 2011,
              <http://www.rfc-editor.org/info/rfc6066>.

   [RFC6437]  Amante, S., Carpenter, B., Jiang, S., and J. Rajahalme,
              "IPv6 Flow Label Specification", RFC 6437,
              DOI 10.17487/RFC6437, November 2011,
              <http://www.rfc-editor.org/info/rfc6437>.

   [RFC7011]  Claise, B., Ed., Trammell, B., Ed., and P. Aitken,
              "Specification of the IP Flow Information Export (IPFIX)
              Protocol for the Exchange of Flow Information", STD 77,
              RFC 7011, DOI 10.17487/RFC7011, September 2013,
              <http://www.rfc-editor.org/info/rfc7011>.

   [RFC7749]  Reschke, J., "The "xml2rfc" Version 2 Vocabulary",
              RFC 7749, DOI 10.17487/RFC7749, February 2016,
              <http://www.rfc-editor.org/info/rfc7749>.

Authors' Addresses

   Sheng Jiang (editor)
   Huawei Technologies Co., Ltd
   Q 22, Huawei Campus, No.156 Beiqing Road
   Hai-Dian District, Beijing, 100095
   P.R. China

   Email: jiangsheng@huawei.com


   Bing Liu
   Huawei Technologies Co., Ltd
   Q 22, Huawei Campus, No.156 Beiqing Road
   Hai-Dian District, Beijing, 100095
   P.R. China

   Email: leo.liubing@huawei.com








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   Panagiotis Demestichas
   University of Piraeus
   Piraeus
   Greece

   Email: pdemestichas@gmail.com


   Jerome Francois
   Inria
   615 rue du jardin botanique
   54600 Villers-les-Nancy
   France

   Email: jerome.francois@inria.fr


   Giovane C. M. Moura
   SIDN Labs
   Meander 501
   Arnhem, 6825 MD
   The Netherlands

   Email: giovane.moura@sidn.nl


   Pere Barlet
   Network Polygraph
   Edifici K2M - Parc UPC
   Jordi Girona, 1-3, Barcelona  08034
   Spain

   Email: pbarlet@polygraph.io


















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