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What is Threat Landscape?

The Threat Landscape is an innovative automated platform tailored for security analysts and SOC teams, providing them with dependable and actionable intelligence while removing the necessity for manual triage. This advanced system persistently collects and scrutinizes global open-source intelligence (OSINT) along with darknet data, adeptly extracting pertinent structured information and reducing the influx of irrelevant data prior to its delivery to the analysts. All collected intelligence is formatted in STIX 2.1, aligned with the MITRE ATT&CK framework, and cross-referenced with a variety of components such as threat actors, malware families, CVEs, TTPs, and IOCs, allowing teams to concentrate on leveraging intelligence rather than generating it themselves. Among its standout features, the platform offers interactive dashboards, visual depictions of STIX threat graphs, advanced search and filtering functionalities, monitoring capabilities for darknet claims related to leak sites and criminal dialogues, automated reporting on a daily and weekly basis, as well as a RESTful API that facilitates seamless integration with SIEM, SOAR, and TIP platforms. This comprehensive platform not only empowers security teams to respond promptly and effectively to newly emerging threats but also enhances their overall cybersecurity posture, ensuring they remain a step ahead in a constantly evolving threat landscape. By streamlining processes and centralizing critical information, security analysts can work more efficiently and effectively, thereby bolstering their organizational defenses.

What is Darknet?

Darknet is an open-source neural network framework crafted with C and CUDA, celebrated for its rapid performance and ease of installation, supporting both CPU and GPU processing. The source code is hosted on GitHub, where users can delve deeper into its functionalities. Installing Darknet is a breeze, needing just two optional dependencies: OpenCV for better image format compatibility and CUDA to harness GPU acceleration. While it operates efficiently on CPUs, it can exhibit an astounding performance boost of around 500 times when utilized with a GPU! To take advantage of this enhanced speed, an Nvidia GPU along with a CUDA installation is essential. By default, Darknet uses stb_image.h for image loading, but for those who require support for less common formats such as CMYK jpegs, OpenCV serves as an excellent alternative. Furthermore, OpenCV allows for real-time visualization of images and detections without the necessity of saving them. Darknet is capable of image classification using established models like ResNet and ResNeXt, and has gained traction for applying recurrent neural networks in fields such as time-series analysis and natural language processing. This versatility makes Darknet a valuable tool for both experienced developers and those just starting out in the world of neural networks. With its user-friendly interface and robust capabilities, Darknet stands out as a prime choice for implementing sophisticated neural network projects.

Media

Media

Integrations Supported

OpenCTI

Integrations Supported

OpenCTI

API Availability

Has API

API Availability

Has API

Pricing Information

$499/month
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Ecliptica Labs AB

Date Founded

2011

Company Location

Sweden

Company Website

threatlandscape.io

Company Facts

Organization Name

Darknet

Company Website

pjreddie.com/darknet/

Categories and Features

Categories and Features

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