Application Recognition

Application Recognition (PDF)

NVIDIA DOCA Application Recognition Reference Guide

This document provides application recognition implementation on top of NVIDIA® BlueField® DPU.

Application Recognition (AR) allows identifying applications that are in use on a monitored networking node.

AR enables the security administrator to generate consolidated reports that show usage patterns from the application perspective. AR is also used as a corner stone of many security applications such as L7-based firewalls.

Due to the massive growth in the number of applications that communicate over Layer 7 (HTTP), effective monitoring of network activity requires looking deeper into Layer 7 traffic so individual applications can be identified. Different applications may require different levels of security and service.

This document describes how to build AR using the deep packet inspection (DPI) engine, which leverages NVIDIA® BlueField®-2 DPU capabilities such as regular expression (RXP) acceleration engine, hardware-based connection tracking, and more.

The AR application is designed to run as "bump-on-the-wire" on the BlueField-2 instance, it intercepts the traffic coming from the wire, and passes it to the Physical Function (PF) representor connected to the host.

system_design.png

AR runs on top of Data Plan Development Kit (DPDK) based Stateful Flow Tracking (SFT) to identify the flow that each packet belongs to, then uses DPI to process L7 classification.

application_architecture.png

  1. Signatures are compiled by DPI compiler and then loaded to DPI engine.
  2. Ingress traffic is identified using the stateful table module in the DPDK libs which utilizes the connection tracking hardware offloads. This allows flow classifications to be done in the hardware level and be forwarded to the hairpin queue without being processed by the software, which increases performance dramatically.
  3. Traffic is scanned against DPI engine compiled signature DB.
  4. Post processing is performed for match decision.
  5. Matched flows are identified, and actions can be offloaded to the hardware to increase performance as no further inspection is needed.
  6. Flow termination is done by the aging timer set in the SFT to 60 seconds. When a flow is offloaded it cannot be tracked and destroyed.

  1. Parse application argument.
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    arg_parser_init();

    1. Initialize arg parser resources.
    2. Register DOCA general flags.
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      register_ar_params();

    3. Register AR application flags.
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      arg_parser_start();

    4. Parse DPDK flags and call rte_eal_init() function.
    5. Parse app flags.
  2. DPDK initialization.
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    dpdk_init();

    1. Initialize SFT.
    2. Initialize DPDK ports, including mempool allocation.
  3. AR initialization
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    ar_init();

    1. Initialize NetFlow using default configuration /etc/doca_netflow.conf.
    2. Initialize signature database.
    3. Initialize DPI engine.
    4. Load signatures to DPI.
  4. Configure DPI packet processing.
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    dpi_worker_lcores_run();

    1. Configure DPI enqueue packets.
    2. Send jobs to RegEx engine.
    3. Configure DPI dequeue packets.
  5. Send statistics and write database.
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    sig_database_write_to_csv(); send_netflow();

    1. Send statistics to the collector.
    2. Write CSV file with signature statistics.
  6. AR destroy.
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    ar_destroy();

    1. Clear thread.
    2. Stop DPI worker.
    3. Stop DOCA DPI.
  7. DPI destroy
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    doca_dpi_destroy();

  1. Please refer to the DOCA Installation Guide for details on how to install BlueField related software.

  2. The application recognition binary is located under /opt/mellanox/doca/examples/application_recognition/bin/doca_application_recognition.
  3. To build the application:
    1. Run:
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      cd /opt/mellanox/doca/examples/application_recognition/src meson /tmp/build ninja -C /tmp/build

      doca_application_recognition will be created under tmp/build.
    2. The build process depends on the PKG_CONFIG_PATH environment variable to locate the DPDK libraries. If the variable was accidently corrupted, and the build fails, please run the following command.
      • For Ubuntu:
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        export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/opt/mellanox/dpdk/lib/aarch64-linux-gnu/pkgconfig

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        export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/opt/mellanox/dpdk/lib64/pkgconfig

  4. Pre-run setup:
    1. The application recognition example is based on DPDK libraries. Therefore, the user is required to provide DPDK flags, and allocate huge pages. Run:
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      echo 2048 > /sys/kernel/mm/hugepages/hugepages-2048kB/nr_hugepages

    2. Make sure the regex engine is active:
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      systemctl status mlx-regex

      If the status is inactive ("Active: failed"), run:
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      systemctl start mlx-regex


  5. To run the application:
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    Usage: doca_application_recognition [DPDK Flags] -- [DOCA Flags] [Program Flags] DOCA Flags: -h, --help Print a help synopsis -l, --log-level Set the log level for the app <CRITICAL=0, DEBUG=4> Program Flags: -p, --print-match Prints FID when matched in DPI engine -n, --netflow Collect netflow statistics and send according to conf file -i, --interactive Adds interactive mode for blocking signatures -o, --output-csv <path> Path to the output of the CSV file -c, --cdo <path> Path to CDO file compiled from a valid PDD

    For example:
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    /opt/mellanox/doca/examples/application_recognition/bin/doca_application_recognition -a 0000:03:00.0,class=regex -a auxiliary:mlx5_core.sf.4,sft_en=1 -a auxiliary:mlx5_core.sf.5,sft_en=1 -- -c /tmp/ar.cdo -p


    Note:

    The SFT supports a maximum of 64 queues, thus the application cannot be run with more than 64 cores. To limit the number of cores, run:

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    /opt/mellanox/doca/examples/application_recognition/bin/doca_application_recognition -a 0000:03:00.0,class=regex -a auxiliary:mlx5_core.sf.4,sft_en=1 -a auxiliary:mlx5_core.sf.5,sft_en=1 -l 0-64 -- -c /tmp/ar.cdo -p

    This limits the application to using 65 cores (core-0 to core-64) with 1 core for the main thread and 64 others to serve as workers.


    To run doca_application_recognition using a JSON file:
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    doca_application_recognition --json [json_file]


    For example:
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    /opt/mellanox/doca/examples/application_recognition/bin/doca_application_recognition –-json /root/ar_params.json


    Note:

    Subfunctions must be enabled according to Scalable Function Setup Guide.

    Note:

    The flags -a 0000:03:00.0,class=regex -a auxiliary:mlx5_core.sf.4,sft_en=1 -a auxiliary:mlx5_core.sf.5,sft_en=1 are necessary for proper usage of the application. Modifying these flags results in unexpected behavior as only 2 ports are supported. The SF numbers are arbitrary and configurable. The RegEx device, however, is not and must be initiated on port 0.

    For additional information on available flags for DPDK, use -h before the -- separator:
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    /opt/mellanox/doca/examples/application_recognition/bin/doca_application_recognition -h


    For additional information on the app, use -h after the -- separator:
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    /opt/mellanox/doca/examples/application_recognition/bin/doca_application_recognition -- -h


    The application will periodically dump a .csv file with the recognition results containing statistics about the recognized apps in the format SIG_ID, APP_NAME, MATCHING_FIDS, and DROP.

    As per the example above, a file called ar_stats.csv will be created.

    Additional features can be triggered by using the shell interaction. This allows blocking and unblocking specific signature IDs using the following commands:

    • block <sig_id>
    • unblock <sig_id>

    The TAB key allows autocompletion while the quit command terminates the application.

    NetFlow collector UI example:

    netflow-collector-ui-example.png

  6. To use the supplied signature file (suricata_rules_example), which is installed to the bin directory, the DPI compiler must be used, as the RegEx engine accepts only .cdo files. The CDO files are constructed by compiling a signature file written in the Suricata open-source format. To compile the signature file, run the following:
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    doca_dpi_compiler -i /opt/mellanox/doca/examples/application_recognition/bin/ar_suricata_rules_example -o /tmp/ar.cdo -f suricata


    A .cdo will be created in the output path flagged as the -o input path of the compiler. That file can be used when executing the reference application using the -c flag as can be seen in previous bullet.

Refer to NVIDIA DOCA Arg Parser User Guide for more information.

Flag Type Short Flag Long Flag/JSON Key Description JSON Content
DPDK flags a devices Add a PCI device into the list of devices to probe
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"devices": [ {"device": "regex",” id": "0000:03:00.0"}, {"device": "sf", "id": “4”,"sft": true}, {"device": "sf", "id": “5”,"sft": true}, ]

l core-list List of cores to run on
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"core-list": "0-4"

General flags l log-level Sets the log level for the application:
  • CRITICAL=0
  • ERROR=1
  • WARNING=2
  • INFO=3
  • DEBUG=4
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"log-level": 4

h help Print a help synopsis N/A
Program flags p print-match Prints FID when matched in DPI engine
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"print-match": true

n netflow Collect netflow statistics and send according to conf file
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"netflow": false

i interactive Adds interactive mode for blocking signatures
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"interactive": false

o output-csv Path to the output of the CSV file
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"output-csv": "/tmp/ ar_stats.csv"

c cdo Path to CDO file compiled from a valid PDD
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"cdo": "/tmp/ar.cdo"

Host execution example:

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doca_application_recognition -a 0000:04:00.0,class=regex -a 04:00.3 -a 04:00.4 -v -- -c suricata_rules_example.cdo -o /tmp/check.csv -p


Refer to section "Running DOCA Application on Host" in NVIDIA DOCA Virtual Functions User Guide.

Refer to NVIDIA DOCA gRPC Infrastructure User Guide for instructions on running the gRPC application server on the BlueField.

managing-grpc-enabled-app-from-host.png

To run the Python client of the gRPC-enabled application:

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./doca_application_recognition_gRPC_client.py -d/--debug <server address[:server port]>


For example:

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/opt/mellanox/doca/examples/application_recognition/bin/grpc/client/doca_application_recognition_gRPC_client.py 192.168.104.2


Note:

Refer to known issue 2872883 in the NVIDIA DOCA Release Notes regarding the execution of the gRPC Python client.


The application recognition example supports a container-based deployment:

  1. Refer to the NVIDIA DOCA Container Deployment Guide for details on how to deploy a DOCA container to the BlueField.
  2. Application-specific configuration steps can be found on NGC under the application's container page.

  • /opt/mellanox/doca/examples/application_recognition/src/application_recognition.c
  • /opt/mellanox/doca/examples/application_recognition/src/grpc/application_recognition.proto
  • /opt/mellanox/doca/examples/application_recognition/bin/ar_suricata_rules_example

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