Secure Devices in the Age of IoT and Edge-Computing
Modern sensors and actors are connected to collect valuable data or to carry out actions. Paradigms such as autonomous driving, smart cities or industry 4.0 are realized on the basis of the data. However, the IoT devices make them attractive targets for attacks. Since critical infrastructures (power plants, power grids, water supply, …), airplanes, cars, etc. are controlled on the basis of sensor values, the consequences of a successful attack can be severe. This is particularly critical as many sensors are installed in the field and are only weakly protected against physical attacks. For this reason, manipulations of the end devices must be detected at all times and the embedded software must be protected. — PHYSEC Integrity solves these problem.
Protection against physical attacks
through individual device fingerprints.
Reduced system costs due to reduced
requirements for other components.
Protection against IP theft through the
device binding of the software.
Enclosure-PUF – Innovative System-level Security
The Enclosure PUF is designed as a Tamper Proof (detection and response) to protect software from hardware tampering. The technology detects even the smallest changes in the physics of the protected object, even if they occur offline. Since an Internet connection is not required, autonomous devices can also be protected in the field. Since the technology is fundamental to sustainable digitization, it has already received several awards, including the German IT Security Award in 2018.
! Nice to know !
A unique fingerprint of physical objects can be created with the help of electromagnetic waves. If the fingerprint is then checked against an initial pattern, changes in the object’s condition can be deduced.
The physical feedback of an object to EM waves is a unique property.
This response is measured and transformed into digital data.
We obtain a unique fingerprint of the object inside its specific environment.
From there, we derive a secret key correlated to the physical properties of the object.
PHYSEC Remote Assessment – Intelligentes Real-time Monitoring
With Managed Virtual Proof of Reality (VPoR) we offer the Enclosure-PUF as online monitoring and remote monitoring in real time. Our modular solution is connected to existing management systems via standard interfaces. The automated recording enables personnel, time and cost savings.
- Combination of electromagnetic measurement methods with cryptographic protocols
- Detects physical changes in the environment (currently from 10 cm3 bis 50 m3)
- Unique fingerprints and cryptographic keys from the environment
- Connection of OT and IT
- patent pending
- Lifecycle security: Protects against manipulation during distribution and operation
- End users can verify the originality and integrity of their systems
- Protection of local data and embedded software
- Real-time detection of hardware manipulations and initiation of countermeasures
- Automated Monitoring Solution
- Designed as an online (managed) as well as offline solution
- Easy retrofitting of existing systems
- Application-specific adaptation and parameterization possible
Technical details were presented at the the hardwear.io conference 2019:
Applications for the Evaluation of Physical Integrity
- Edge Computing
- Hardware Security Module
- VPN Boxes
- Cloud Server
- Network Periphery (Router, Switches, …)
- Machine Controlling Modules
- KRITIS Systems
- Power Plants Controlling Systems
- Base Stations
- Smart Meter
- Charging Stations for Electric Cars
- Chain of Custody
- Cargo Holds
- Storages / Silos
! Nice to know !
Artificial neural networks (ANN) account for a large part of the current success of certain AI applications. They make it possible to learn from experience, i.e. data, and to derive a general rule from a large number of individual cases and apply it to future cases. They thus not only provide the basis for machine learning, but also the reason for groundbreaking achievements in the field of artificial intelligence.
Adversarial examples represent a new and particularly dangerous class of attackers in the context of ANN and sensor technology. Here, neural networks are deceived by imperceptibly altering individual data. — Further information about the criticality of the attacker class can be found here.
We as PHYSEC answer your questions.
You can talk directly to our executives.
Dr. Christian Zenger, CEO
Dr. Heiko Koepke, CFO