Fastly Next-Gen WAF
Overview
Fastly Next-Gen WAF is a web application firewall solution designed to protect online assets by mitigating web application threats, ensuring security, and enhancing application performance with its edge-based, real-time protection capabilities.
- Vendor: Fastly
- Supported environment: SaaS
- Version compatibility:
- Detection based on: Alert
- Supported application or feature: WAF Alerts
Warning
Important note - This format is currently in beta. We highly value your feedback to improve its performance.
Step-by-Step Configuration Procedure
Instructions on the 3rd Party Solution
Creating API access tokens
- Go to the Fastly WAF and log in.
- From the My Profile menu, select API access tokens.
- Click Add API access token.
- In the Token name field, enter a name to identify the access token.
- Click Create API access token.
- Record the token in a secure location for your use. Then, click Continue to finish creating the token.
Warning
This is the only time the token will be visible. Record the token and keep it secure.
Instruction on Sekoia
Configure Your Intake
This section will guide you through creating the intake object in Sekoia, which provides a unique identifier called the "Intake key." The Intake key is essential for later configuration, as it references the Community, Entity, and Parser (Intake Format) used when receiving raw events on Sekoia.
- Go to the Sekoia Intake page.
- Click on the
+ New Intake
button at the top right of the page. - Search for your Intake by the product name in the search bar.
- Give it a Name and associate it with an Entity (and a Community if using multi-tenant mode).
- Click on
Create
.
Note
For more details on how to use the Intake page and to find the Intake key you just created, refer to this documentation.
Configure Your Playbook
This section will assist you in pulling remote logs from Sekoia and sending them to the intake you previously created.
- Go to the Sekoia playbook page.
- Click on the
+ New playbook
button at the top right of the page. - Select
Create a playbook from scratch
, and clickNext
. - Give it a Name and a Description, and click
Next
. - Choose a trigger from the list by searching for the name of the product, and click
Create
. - A new Playbook page will be displayed. Click on the module in the center of the page, then click on the Configure icon.
- On the right panel, click on the
Configuration
tab. - Select an existing Trigger Configuration (from the account menu) or create a new one by clicking on
+ Create new configuration
. - Configure the Trigger based on the Actions Library (for instance, see here for AWS modules), then click
Save
. - Click on
Save
at the top right of the playbook page. - Activate the playbook by clicking on the "On / Off" toggle button at the top right corner of the page.
Note
For this procedure, you will need the Intake key
created on the previous step, and User's email
, API token
, Corporation name
and Site name
from the Fastly WAF dashboard
Raw Events Samples
In this section, you will find examples of raw logs as generated natively by the source. These examples are provided to help integrators understand the data format before ingestion into Sekoia.io. It is crucial for setting up the correct parsing stages and ensuring that all relevant information is captured.
{
"id": "54de69dcba53b02fbf000018",
"timestamp": "2015-02-13T21:17:16Z",
"source": "162.245.23.109",
"remoteCountryCode": "AU",
"remoteHostname": "",
"userAgents": [
"Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 5.0)"
],
"action": "flagged",
"type": "attack",
"reasons": {
"SQLI": 99
},
"requestCount": 1,
"tagCount": 1,
"window": 60,
"expires": "2015-02-14T21:17:16Z",
"expiredBy": ""
}
Detection section
The following section provides information for those who wish to learn more about the detection capabilities enabled by collecting this intake. It includes details about the built-in rule catalog, event categories, and ECS fields extracted from raw events. This is essential for users aiming to create custom detection rules, perform hunting activities, or pivot in the events page.
Related Built-in Rules
The following Sekoia.io built-in rules match the intake Fastly Next-Gen WAF Alerts [BETA]. This documentation is updated automatically and is based solely on the fields used by the intake which are checked against our rules. This means that some rules will be listed but might not be relevant with the intake.
SEKOIA.IO x Fastly Next-Gen WAF Alerts [BETA] on ATT&CK Navigator
Cryptomining
Detection of domain names potentially related to cryptomining activities.
- Effort: master
Dynamic DNS Contacted
Detect communication with dynamic dns domain. This kind of domain is often used by attackers. This rule can trigger false positive in non-controlled environment because dynamic dns is not always malicious.
- Effort: master
Exfiltration Domain
Detects traffic toward a domain flagged as a possible exfiltration vector.
- Effort: master
Nimbo-C2 User Agent
Nimbo-C2 Uses an unusual User-Agent format in its implants.
- Effort: intermediate
Potential Bazar Loader User-Agents
Detects potential Bazar loader communications through the user-agent
- Effort: elementary
Potential Lemon Duck User-Agent
Detects LemonDuck user agent. The format used two sets of alphabetical characters separated by dashes, for example "User-Agent: Lemon-Duck-[A-Z]-[A-Z]".
- Effort: elementary
Remote Access Tool Domain
Detects traffic toward a domain flagged as a Remote Administration Tool (RAT).
- Effort: master
Remote Monitoring and Management Software - AnyDesk
Detect artifacts related to the installation or execution of the Remote Monitoring and Management tool AnyDesk.
- Effort: master
SEKOIA.IO Intelligence Feed
Detect threats based on indicators of compromise (IOCs) collected by SEKOIA's Threat and Detection Research team.
- Effort: elementary
Sekoia.io EICAR Detection
Detects observables in Sekoia.io CTI tagged as EICAR, which are fake samples meant to test detection.
- Effort: master
TOR Usage Generic Rule
Detects TOR usage globally, whether the IP is a destination or source. TOR is short for The Onion Router, and it gets its name from how it works. TOR intercepts the network traffic from one or more apps on user’s computer, usually the user web browser, and shuffles it through a number of randomly-chosen computers before passing it on to its destination. This disguises user location, and makes it harder for servers to pick him/her out on repeat visits, or to tie together separate visits to different sites, this making tracking and surveillance more difficult. Before a network packet starts its journey, user’s computer chooses a random list of relays and repeatedly encrypts the data in multiple layers, like an onion. Each relay knows only enough to strip off the outermost layer of encryption, before passing what’s left on to the next relay in the list.
- Effort: master
WAF Correlation Block actions
Detection of multiple block actions (more than 30) triggered by the same source by WAF detection rules
- Effort: master
Event Categories
The following table lists the data source offered by this integration.
Data Source | Description |
---|---|
Web application firewall logs |
Fastly WAF protects web application with its web application firewall |
In details, the following table denotes the type of events produced by this integration.
Name | Values |
---|---|
Kind | alert |
Category | network |
Type | `` |
Transformed Events Samples after Ingestion
This section demonstrates how the raw logs will be transformed by our parsers. It shows the extracted fields that will be available for use in the built-in detection rules and hunting activities in the events page. Understanding these transformations is essential for analysts to create effective detection mechanisms with custom detection rules and to leverage the full potential of the collected data.
{
"message": "{\"id\": \"54de69dcba53b02fbf000018\", \"timestamp\": \"2015-02-13T21:17:16Z\", \"source\": \"162.245.23.109\", \"remoteCountryCode\": \"AU\", \"remoteHostname\": \"\", \"userAgents\": [\"Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 5.0)\"], \"action\": \"flagged\", \"type\": \"attack\", \"reasons\": {\"SQLI\": 99}, \"requestCount\": 1, \"tagCount\": 1, \"window\": 60, \"expires\": \"2015-02-14T21:17:16Z\", \"expiredBy\": \"\"}",
"event": {
"action": "flagged",
"category": [
"network"
],
"kind": "alert",
"module": "fastly.waf",
"type": [
"denied"
]
},
"@timestamp": "2015-02-13T21:17:16Z",
"fastly": {
"waf": {
"expires": "2015-02-14T21:17:16Z",
"reasons": {
"SQLI": 99
},
"request_count": 1,
"tag_count": 1,
"user_agents": [
"Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 5.0)"
],
"window": 60
}
},
"host": {
"geo": {
"country_iso_code": "AU"
}
},
"observer": {
"product": "Fastly Next-Gen WAF",
"vendor": "Fastly"
},
"related": {
"ip": [
"162.245.23.109"
]
},
"source": {
"address": "162.245.23.109",
"ip": "162.245.23.109"
},
"user_agent": {
"device": {
"name": "Other"
},
"name": "IE",
"original": "Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 5.0)",
"os": {
"name": "Windows",
"version": "2000"
},
"version": "5.5"
}
}
Extracted Fields
The following table lists the fields that are extracted, normalized under the ECS format, analyzed and indexed by the parser. It should be noted that infered fields are not listed.
Name | Type | Description |
---|---|---|
@timestamp |
date |
Date/time when the event originated. |
event.action |
keyword |
The action captured by the event. |
event.category |
keyword |
Event category. The second categorization field in the hierarchy. |
event.kind |
keyword |
The kind of the event. The highest categorization field in the hierarchy. |
event.module |
keyword |
Name of the module this data is coming from. |
fastly.waf.expired_by |
keyword |
Email of the user if the event is expired manually |
fastly.waf.expires |
keyword |
Expires RFC3339 date time |
fastly.waf.reasons |
object |
Key attack type - value number of |
fastly.waf.request_count |
long |
Total number of requests |
fastly.waf.tag_count |
long |
Total number of tags |
fastly.waf.user_agents |
keyword |
|
fastly.waf.window |
long |
Time window in seconds where the items were detected |
host.geo.country_iso_code |
keyword |
Country ISO code. |
host.name |
keyword |
Name of the host. |
observer.product |
keyword |
The product name of the observer. |
observer.vendor |
keyword |
Vendor name of the observer. |
source.ip |
ip |
IP address of the source. |
user_agent.original |
keyword |
Unparsed user_agent string. |
For more information on the Intake Format, please find the code of the Parser, Smart Descriptions, and Supported Events here.