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======Configuring DDoS and BotNet Detector Based on QoE======
The detector allows automatic monitoring of incoming traffic, identifying DDoS attacks, and quickly taking measures to protect the network. When a threat is detected, the system can fully isolate the malicious stream or clean the traffic while maintaining service availability for users.\\
The solution requires SSG version BASE, COMPLETE, or BNG with additional options and is deployed on an existing server with QoE.
<note>
[[en:dpi:dpi_options:opt_ddos:ddos_description|More about tools for DDoS protection and BotNet detection]]
</note>

=====1. Updating QoE=====
**On the QoE server.**

[[en:dpi:qoe_analytics:implementation_administration:installation_update:update|Update QoE]] to the latest version, stopping the receivers beforehand. Before starting receivers, patch ClickHouse: 
<code>dnf --refresh install clickhouse-patched</code>
Start the receivers.

=====2. Updating GUI=====
**On the GUI server.**

[[en:dpi:dpi_components:dpiui:install_and_update:update|Update GUI]] to the latest version. Connect GUI to VAS Cloud if not already connected. Enable the aniddos license option.

In the file ''/var/www/html/dpiui2/frontend/env.js'' set the option ''AppEnv.DDoSAttack_isVisible = 1;''

=====3. Installing the Detector=====
**On the QoE server.** 

Install the mitigator package ''fastm_qoe'' on all nodes: <code>dnf install fastm_qoe</code>

Switch Python version: <code>dnf install -y python39 python39-devel -y
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 60
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 70
sudo update-alternatives --config python3</code>

Select version 3.9: <code>python3 --version</code>

=====4. Configuring the Detector=====
**On the QoE server.** 

On all nodes, or on selected ones:

  - Edit the file ''/var/fastm_qoe/etc/.env''.\\ It should contain the following:<code>ANALYZER=avg-based-z-score
ANALYZER_RULES_KEY=avg-based-z-score-any

IDLE_MODE=1
FORCE_MODE=0
DB_DROP_TABLES=1

FM_ATTACKS_METRICS_BY_SUBS_FILTER="and has_attack = 0"
FM_ATTACKS_METRICS_BY_SUBS_LIMIT=1
FM_ATTACKS_METRICS_BY_SUBS_COLLAPSE=1
FM_ATTACKS_METRICS_BY_SUBS_DAY='day_'</code>
  - Update schema: <code>fastm-db-scheme</code>
  - Enable metrics collection\\ Add to file ''/var/qoestor/backend/.env'' the following: <code>FM_FULLFLOW_HOOK_ENABLE=1
GEO_IP_DIC_AUTOLOAD_ENABLED=1</code> Execute the daily cron: <code>sh /var/qoestor/backend/app_bash/cron_daily.sh</code> Collect metrics for several hours, ideally 24 hours. Then edit ''/var/fastm_qoe/etc/.env'' again and change 2 parameters:<code>IDLE_MODE=0
DB_DROP_TABLES=0</code> This activates the detector.

=====5. Trigger Thresholds=====
In the file ''/var/fastm_qoe/lib/rules/config.json'', edit the section ''avg-based-z-score-any'' as follows:
<code>
"avg-based-z-score-any": {
        "octets": { "th": 100, "weight": 0.1 },
        "octets_dropped": { "th": 1000, "weight": 0.3 },
        "packets": { "th": 100, "weight": 0.3 },
        "packets_dropped": { "th": 1000, "weight": 0.3 },
        "flows": { "th": 100, "weight": 0.4 },
        "sessions": { "th": 100, "weight": 0.4 },
        "duration": { "th": 100, "weight": 0.01 },
        "host_ips": { "th": 100, "weight": 0.3 },
        "protos": { "th": 100, "weight": 0.3 },
        "bits_sec": { "th": 100, "weight": 0.05 },
        "bits_dropped_sec": { "th": 1000, "weight": 0.05 },
        "packets_sec": { "th": 100, "weight": 0.05 },
        "packets_dropped_sec": { "th": 1000, "weight": 0.05 }
    },
</code>

=====6. Metrics Storage (DDoS Attack Logs)=====
In the GUI web interface, configure storage of raw and aggregated metrics, as well as raw and aggregated attack logs. 

In Admin → GUI Configuration → QoE Stor: set DB retention time values as follows:
  * ''QOESTOR_FM_ATTACKS_MAIN_LOG_PARTITIONS_LIFE_TIME_HOUR'' = ''720''
  * ''QOESTOR_FM_ATTACKS_AGG_LOG_PARTITIONS_LIFE_TIME_DAYS'' = ''30''
  * ''QOESTOR_FM_METRICS_MAIN_LOG_PARTITIONS_LIFE_TIME_HOUR'' = ''72''
  * ''QOESTOR_FM_METRICS_AGG_LOG_PARTITIONS_LIFE_TIME_DAYS'' = ''7''

{{:en:dpi:dpi_options:opt_ddos:qoe_ddos_1.png?direct&1100|}}

=====7. Attack Analysis=====
Detected attacks can be examined in the DDoS attack sections in QoE Analytics.

  - Start with the "TOP Attacks" section for the last 24 hours.\\ Sort attacks by number of sessions and note a few IPs with the highest session count.\\ {{:en:dpi:dpi_options:opt_ddos:qoe_ddos_3.png?direct&700|}}
  - Check the "TOP Attacks by Protocols" section\\ Also sort by session count. Note these protocols.
  - Check the "TOP Attacking IP Addresses" section, note a few IPs with the highest session count\\ {{:en:dpi:dpi_options:opt_ddos:qoe_ddos_2.png?direct&700|}}
  - Analyze the Attack Log with a filter by previously selected subscribers and protocol.\\ You can extract attack details and make additional conclusions to take appropriate actions.\\ For example, in the screenshot below it is clearly visible that there is port scanning on the same address using the UDP protocol. In this case, it is sufficient to assign a ''drop'' policy for the application protocol ''udp unknown'' to the attacking subscriber profile using session policing (service 18).\\ \\ This means that for the selected subscriber, all traffic matching this protocol will be completely blocked, i.e., both UDP flood and legitimate UDP traffic identified by DPI as udp unknown.

<note tip>More details about session policing configuration: [[en:dpi:dpi_options:opt_shaping:shaping_session]]</note>

{{:en:dpi:dpi_options:opt_ddos:qoe_ddos_4.png?direct&1100|}}\\