Welcome to Parcoor

Monitoring and ML-enhanced protection for your embedded devices

Embedded systems and IoT objects are spreading at an always faster pace. The attacks targeting them are spreading even faster. At Parcoor we are ongoingly developing innovative threat detection solutions. Those solutions are based on a novel approach combining system core data monitored in real-time + cutting-edge, lightweight, fast and explainable machine learning algorithms. Our solutions greatly enhance the capabilities of your devices and provide them a strong, flexible security layer.

Trusted by

Minalogic
Safety-security Minalogic directory 2021
Wavestone
Startups cybersecurity radar Wavestone 2021
Grand Defi
Laureate of the "Grand Défi Cybersécurité" 2021

Our solutions

Bring Intelligence into your Embedded Device

Machine learning (ML) embedded on small devices, also called TinyML, paves the way for a wide spectrum of new applications using and leveraging many information directly on-device.

At Parcoor, our on-device solutions consist in independant blocks: system monitoring and/or ML signal classification. Put together, those blocks can act as a powerful real-time embedded cybersecurity layer.

Core Monitoring

The first block of our technology consists in in-depth system monitoring. Provides you actionable insights from the deep core of your systems, giving you access to a new dimension of information. High precision and non-intrusive.

This block packages a solution for real-time, precise and non-intrusive fetching of a variety of micro-events at the deep heart of your system.
Beyond cybersecurity, this block can assist you identifying and killing bottlenecks in performance critical applications.
Use cases : High performance computing, High frequency trading, Monitoring execution of time-critical complex computations, etc.
time illustration
data process illustration

Signal Processing

The second block of our technology is an in-house cutting-edge machine learning layer specially and directly designed for embedded systems. Low overhead, reliable and explainable: You not only know what decisions it takes, but also why.

TinyML is a relative recent area of machine learning.
Other than classical embedded machine learning solutions (developed with heavy frameworks and later fitted for IoT devices) our ML block was designed from the beginning for devices with resource constraints, focussing on high performance and with explainability in mind. Because you should not have to rely on a black-box for vital decisions.
Use cases : Anomaly detection, Hardware failure detection, Attack detection, etc.

Threat Detection

Combined together, our monitoring and our machine-learning blocks form a powerful attack detection and mitigation layer, protecting your fleet of embedded devices from threats and vulnerabilities, and ensuring embedded cybersecurity and autonomous real-time detection, including "0 day" malwares.

  • Surface of attack reduced
  • Reaction speed
  • High-precision detection

Novel innovative cyber-threats require novel protection approaches, beyond the current signature-based or static analysis ones.
IoT devices are increasingly becoming a weak security part exploited by malicious and hostile actors for disruption, data-leaking etc. against targetted organizations, causing significant risks and losses.
Our approach is inspired by Academic researches that demonstrate excellent results. With Parcoor, your industry can benefit from our industry-grade improvements of those researches.
Use cases : Malware detection, Attack vector detection.
security illustration

Demo

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Interested in our solutions ? Sign up for a free demo and see how Parcoor's plateform can help you secure your connected devices.


Next steps

  • After receiving your request, we will contact you to arrange a time slot for the demo

  • During the demo, we will answer all the questions you may have

  • If you are convinced, we would happily address and solve your need.

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