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.


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.

Inno­va­tion

 

  • 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
Added val­ue

 

  • 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
ben­e­fits

 

  • 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

IT Systems

  • Edge Com­put­ing
  • Hard­ware Secu­ri­ty Mod­ule
  • VPN Box­es
  • Cloud Serv­er
  • ATMs
  • Net­work Periph­ery (Router, Switch­es, …)

OT Systems

  • Machine Con­trol­ling Mod­ules
  • KRITIS Sys­tems
  • Pow­er Plants Con­trol­ling Sys­tems
  • Base Sta­tions
  • Smart Meter
  • Charg­ing Sta­tions for Elec­tric Cars

Logistics

  • Chain of Cus­tody
  • Con­tain­ers
  • Car­go Holds
  • Stor­ages / Silos
  • Safes
  • Bar­rels

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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.