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Nanocorp

Overview

Nanocorp provides network observability and security events, offering centralized visibility and enhanced detection capabilities across the infrastructure.

  • Vendor: Nanocorp
  • Supported environment: Cloud/On Premise
  • Detection based on: Telemetry
  • Supported application or feature: Network events

Warning

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

Configure

Prerequisites

Before starting, ensure you have:

  • Access to the Brain Machine
  • Sufficient permissions to create an external connector
  • The Intake Key provided by the Sekoia platform

Configuration Procedure

1. Access the Panopticon Page

  1. Log in to the Brain Machine.
  2. Navigate to the Panopticon page.
  3. Click the "Add external connector" button.

2. Create a Sekoia Connector

  1. From the list of available connectors, select "Sekoia".
  2. Fill in the required fields.

3. Mandatory Parameter

  • INTAKE KEY : The ingestion key provided by the Sekoia platform. This key is used to authenticate and forward events to your Sekoia tenant.

Validation

  1. Review the configuration settings.
  2. Save the connector.
  3. Verify that events are successfully forwarded to Sekoia.

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.

{
    "event_id": 7077685489580260926,
    "event_type": "External connection",
    "source": 8903162277747742819,
    "seen_at": 1764596280793,
    "source_ip": "192.0.2.0",
    "source_mac": "00:1A:2B:3C:4D:5E",
    "dest_ip": "198.51.100.0",
    "dest_mac": "00:1A:2B:3C:4D:5E",
    "proto_path": "/Ethernet/Ipv4/Tcp/Http/",
    "network_protocol": "http"
}

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 Nanocorp [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 Nanocorp [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
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

Event Categories

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

Name Values
Kind ``
Category network
Type connection

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": "{\n  \"event_id\": 7077685489580260926,\n  \"event_type\": \"External connection\",\n  \"source\": 8903162277747742819,\n  \"seen_at\": 1764596280793,\n  \"source_ip\": \"192.0.2.0\",\n  \"source_mac\": \"00:1A:2B:3C:4D:5E\",\n  \"dest_ip\": \"198.51.100.0\",\n  \"dest_mac\": \"00:1A:2B:3C:4D:5E\",\n  \"proto_path\": \"/Ethernet/Ipv4/Tcp/Http/\",\n  \"network_protocol\": \"http\"\n}\n",
    "event": {
        "action": "External connection",
        "category": [
            "network"
        ],
        "outcome": "Success",
        "start": "2025-12-01T13:38:00.793000Z",
        "type": [
            "connection"
        ]
    },
    "@timestamp": "2025-12-01T13:38:00.793000Z",
    "destination": {
        "address": "198.51.100.0",
        "ip": "198.51.100.0",
        "mac": "00:1A:2B:3C:4D:5E"
    },
    "network": {
        "protocol": "http"
    },
    "observer": {
        "product": "Nanocorp",
        "vendor": "Nanocorp"
    },
    "related": {
        "ip": [
            "192.0.2.0",
            "198.51.100.0"
        ]
    },
    "source": {
        "address": "192.0.2.0",
        "ip": "192.0.2.0",
        "mac": "00:1A:2B:3C:4D:5E"
    }
}

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.
destination.ip ip IP address of the destination.
destination.mac keyword MAC address of the destination.
event.action keyword The action captured by the event.
event.category keyword Event category. The second categorization field in the hierarchy.
event.outcome keyword The outcome of the event. The lowest level categorization field in the hierarchy.
event.start date event.start contains the date when the event started or when the activity was first observed.
event.type keyword Event type. The third categorization field in the hierarchy.
network.protocol keyword Application protocol name.
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.
source.mac keyword MAC address of the source.

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