Aleph Alerts

Overview

Aleph Alert immediately warns you of any data leak on the Deep & Dark Webs, which expose your company to cyber attacks.

Vendor: Aleph Networks
Supported environment: SaaS

Warning

Important note - This format is currently in beta. We highly value your feedback to improve its performance.

High-Level Architecture Diagram

Type of integration: Outbound (PUSH to Sekoia.io)

Specification

Prerequisites

Resource:
- Aleph Alerts
Permissions:
- Licence for Aleph Alerts

Transport Protocol/Method

HTTP Receiver

Logs details

Supported functionalities: Daily results
Supported type(s) of structure: JSON

Step-by-Step Configuration Procedure

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.

Instructions on the 3rd Party Solution

To configure Aleph Alerts to send daily generated results, you need to follow these steps.

Retrieve the intake key generated during the creation of the intake into Sekoia platform (refer to Instruction on Sekoia)
Contact Aleph technical team
Give your intake key to the Aleph technical team

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.

test_alert_with_domaintest_alert_with_url

{
    "alertName": "Alert Name",
    "content": "content",
    "domain": "domain",
    "score": "1.0",
    "timestamp": "2026-03-24T10:09:27Z",
    "title": "Title",
    "url": "url"
}

{
    "alertName": "Alert Name",
    "content": "content",
    "domain": "",
    "score": "1.0",
    "timestamp": "2026-03-24T10:09:27Z",
    "title": "Title",
    "url": "url"
}

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.

The following Sekoia.io built-in rules match the intake Aleph 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 Aleph Alerts [BETA] on ATT&CK Navigator

Bazar Loader DGA (Domain Generation Algorithm)

Detects Bazar Loader domains based on the Bazar Loader DGA

Effort: elementary

Cryptomining

Detection of domain names potentially related to cryptomining activities.

Effort: master

Discord Suspicious Download

Discord is a messaging application. It allows users to create their own communities to share messages and attachments. Those attachments have little to no overview and can be downloaded by almost anyone, which has been abused by attackers to host malicious payloads.

Effort: advanced

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

EvilProxy Phishing Domain

Detects subdomains potentially generated by the EvilProxy adversary-in-the-middle phishing platform. Inspect the other subdomains of the domain to identify the landing page, and determine if the user submitted credentials. This rule has a small percentage of false positives on legitimate domains.

Effort: intermediate

Exfiltration Domain

Detects traffic toward a domain flagged as a possible exfiltration vector.

Effort: master

Koadic MSHTML Command

Detects Koadic payload using MSHTML module

Effort: intermediate

Potential Azure AD Phishing Page (Adversary-in-the-Middle)

Detects an HTTP request to an URL typical of the Azure AD authentication flow, but towards a domain that is not one the legitimate Microsoft domains used for Azure AD authentication.

Effort: intermediate

Remote Access Tool Domain

Detects traffic toward a domain flagged as a Remote Administration Tool (RAT).

Effort: master

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

Event Categories

The following table lists the data source offered by this integration.

Data Source	Description
`Third-party application logs`	Aleph logs data leak alerts from the Deep & Dark Webs

In details, the following table denotes the type of events produced by this integration.

Name	Values
Kind	`alert`
Category	`threat`
Type	`info`

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.

test_alert_with_domain.jsontest_alert_with_url.json

{
    "message": "{\"alertName\":\"Alert Name\",\"content\":\"content\",\"domain\":\"domain\",\"score\":\"1.0\",\"timestamp\":\"2026-03-24T10:09:27Z\",\"title\":\"Title\",\"url\":\"url\"}",
    "event": {
        "action": "alert-triggered",
        "category": [
            "threat"
        ],
        "kind": "alert",
        "type": [
            "info"
        ]
    },
    "@timestamp": "2026-03-24T10:09:27Z",
    "alert": {
        "document_content": "content",
        "document_score": 1.0,
        "document_title": "Title",
        "name": "Alert Name"
    },
    "observer": {
        "product": "ALEPH Alerts",
        "type": "threat-intel",
        "vendor": "ALEPH"
    },
    "related": {
        "hosts": [
            "domain"
        ]
    },
    "url": {
        "domain": "domain",
        "path": "url"
    }
}

{
    "message": "{\"alertName\":\"Alert Name\",\"content\":\"content\",\"domain\":\"\",\"score\":\"1.0\",\"timestamp\":\"2026-03-24T10:09:27Z\",\"title\":\"Title\",\"url\":\"url\"}",
    "event": {
        "action": "alert-triggered",
        "category": [
            "threat"
        ],
        "kind": "alert",
        "type": [
            "info"
        ]
    },
    "@timestamp": "2026-03-24T10:09:27Z",
    "alert": {
        "document_content": "content",
        "document_score": 1.0,
        "document_title": "Title",
        "name": "Alert Name"
    },
    "observer": {
        "product": "ALEPH Alerts",
        "type": "threat-intel",
        "vendor": "ALEPH"
    },
    "url": {
        "path": "url"
    }
}

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.
`alert.document_content`	`text`	The content of the related document found
`alert.document_score`	`float`	The score of the related document found
`alert.document_title`	`keyword`	The title of the related document found
`alert.name`	`keyword`	The name of the concerned Aleph Alert
`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.type`	`keyword`	Event type. The third categorization field in the hierarchy.
`observer.product`	`keyword`	The product name of the observer.
`observer.type`	`keyword`	The type of the observer the data is coming from.
`observer.vendor`	`keyword`	Vendor name of the observer.
`related.hosts`	`keyword`	All the host identifiers seen on your event.
`url.domain`	`keyword`	Domain of the url.
`url.path`	`wildcard`	Path of the request, such as "/search".

For more information on the Intake Format, please find the code of the Parser, Smart Descriptions, and Supported Events here.