Background:

The next generation mobile networks (5G+) will become a critical hub for all critical sectors. Each service is handled by their own slice to meet the security and dependability requirements. The 5G and beyond technology is immature, these developments must be considered.

Objectives:

Increase knowledge and insight in using 5G and beyond technologies to build secure, resilient, and survivable critical infrastructures to provide critical services. 

Approach:

Holistic modelling and analysis on design of secure, resilient, and survivable services, with the goal of quantitvely assess risks and survivability. (Ex: Slice security isolation, Virtual security functionality, Adaptive security for 5G-enabled IoT.)

Contact:

Task Leader T3.1, Poul E. Heegaard

Integrated PhD, Trond Vatten

Objective: 

Establish the grounds for a digital ecosystem that facilitates cyber resilience in CrSec through joint understanding, interactions and reciprocity in strategizing and decision-making. 

Activities: 

• A joint framework for understanding and describing cyber resilience in CrSec
• A community of practitioners representing key actors of a digital ecosystem for electricity supply
• A framework for a digital ecosystem enabling polycentric cyber resilience
• Target aspects for influence and success criteria based on case studies
• Frameworks for resolving conflicting objectives on the path towards resilience
• Procedures for analysis, planning and orchestration of polycentric resilience
• Godd/best practices for maintaining focus on cyber resilience in the evolution (all life cycle phases) of CrSec
• Traiing and piloting of cases that are coordinated with T4.1 (Secure cyber-physical electricity system)

Contact:

Task Leader T3.2, Tor Olav Grøtan (tor.o.grotan@sintef.no)

Partners:

• SINTEF
• NTNU
• ELVIA

What?

• Establish a configurable and expandable cyber-physical range that uses real, simulated and emulated components of critical infrastructures cyber physical systems, integrating both IT and OT infrastructure.
• Develop suitable educational and training material.
• Develop physical testing systems as demonstrators for education and dissemination, but also as test-beds for activities related to research and training.

Why?

• Cyber physical systems constitute the core of Critical Infrastructures, yet their architectural and operational characteristics are not thoroughly captured by contamporary cyber ranges. 

How?

• Reference architecture for modular backbone infrastructure. 
• Attack scenarios, and simulation mechanisms for Cyber Physical Systems across the targeted sectors.
• Cyber physical models and demonstrators for the investigated sectors. 

Contact:

Task Leader T3.3, Vasileios Gkioulos

PhD, Vyron Kampourakis

Objective:

Improvement of (computational) intelligence capabilities to increase situational awareness in cyber-physical systems, and to prevent, interrupt, and explain incidents or malicious activities.

• Study algorithms and methods for machine-assisted (human) intelligence,
• provide relevant research data sets, and anonymized real-world data sets for training and evaluation,
• design algorithms for behaviour monitoring and event detection, and
• conduct data fusion and correlation of data in rest and motion from critical infrastructures in the digital and the physical domain. 

Contact:

Task Leader T3.4, Katrin Franke

PhD, Touseef Sadiq UiA Touseef Sadiq

Partners:

• NTNU CCIS
• Mnemonic
• Universitetet i Agder
• Sykehuset Innlandet HF
• Norsk Regnesentral
• Oslo Politidistrikt

5G and beyond networks need substantial breakthroughs in the below categories, to be able to fulfill the indispensable requirements og 6G or even beyond networks.

• Enhanced Mobile BroadBand (eMBB)
• Ultra Reliable Low Latency Communication (URLLC)
• Massive Machine Type Communication (mMTC).

In this direction, Arikan's (2008) construction of capacity-achieving Polar codes proved to be promising. However, they are not flexible in terms of encoding bloxk length.

• Improved SCL-based decoding of Polar Codes with non-Arikan kernels.
• Based on relative positions of the information and frozen bits. 
• An innovative technique with potential to be filed for patenting!

Contact:

Task Leader T3.5, Danilo Gligoroski

PhD, Sahana Sridhar

Objective:

Enhance the security of 5G-Enabled IoT systems through dynamics lens to systematically and adaptatively understand, characterize, quantify and manage cybersecurity.

Develop Autonomous adaptive security for 5G-IoT

• a framwork for security-related data collection, analytics and prediction of incidents and provision of response and mitigation measures autonomously. It will apply closed loop AI techniques (e.g. RNN, CNN, DNN) in a privacy preserving manner and adapt to security changes and contects in the 5G-IoT dynamics and characteristics

Establish Dynamic risk assessment framework for 5g-IoT

• dynamic risk assessment framework for identifying, measuring and evaluating dynamic risk in real-time in 5G-IoT using graph theory, and fuzzy logic system

Build 5G-SIM card for secure identity management and execution environments

• secure identity management and access control for 5G-enabled IoT systems
• secure isolated tamper proof execution environments for running sensitive 5G-enabled IoT applications using 5G-SIM Card

Contact:

Task Leader T3.6, Habtamu Abie (habtamu.abie@nr.no)

Sandeep Pirbhulal (sandeep@nr.no)

Partners:

• Norsk Regnesentral
• SINTEF
• Hydro
• YARA
• Politiet
• Oslo Politidistrikt
• NC-Spectrum
• Siemens
• Equinor
• Kongsberg
• Mnemonic
• Helgeland Kraft
• Sykehuset Innlandet HF

Objective:

To establish a reverse engineering lab as a national arena for knowledge development, research, innovation and education which can contribute towards improving the cybersecurity and resilience of the entire value chain in our digital society.

Potential Innovation and outcomes:

• Collaboration with potential industries/public institutions for research and development on use case scenarios
• Identify most vulnerable components/devices in a system and attack vectors in cooperation with industries
• Apply different reverse engineering techniques and analyze security mechanism failure
• Develop mitigations techniques, innovative methods and tools for addressing hardware security challenges

Contact:

Task Leader T3.7, Arvind Sharma

Partners:

• Siemens
• Politiet
• SINTEF
• Elvia
• NTNU
• Universitetet i Agder
• Sykehuset Innlandet HF

Objectives:

Develop improved methods for secure communication, data protection, and secure data analysis in cyber-critical systems

Propose new and improved cyber security methods for secure broadcasting in wireless critical systems

• Secure peer-to-peer communication pver wireless systems is expensive compared to one-to-many communication
• Advance the state-of-the-art of broadcast security by minimizing overhead and key management

Investigate secure data analysis using homomorphic cryptography

• Homomorphic encryption enables analysis and processing of encrypted data without revealing the data to anyone
• Big potential in areas with sensitive data, such as the electric grid and healthcare where data protction and privacy is important

Contact:

Task Leader T3.8, Sigurd Eskeland (sigurd@nr.no)

Objective:

Exploit modern security assurance methodologies and ontology-driven engineering approaches to develop a framework that can be used to make the scenario design aware of the needs of security assurance, menwhile, achieving scenario interoperability with cyber ranges. 

• To develop security assurance ontology that enables the systematic representation of the cyber systems (or organizations) and their relationships, potential threats, and ultimately, the corresponding security postures.
• To develop security scenario ontology to encode the description of cyber scenarios and integrate it with features of NCR to improve the efficiency of the development. 
• To define a meta-model with context-reasoning capabilities to connect the assurance modeling elements with the scenario elements, such as contexts, vulnerabilities, threats, and expected actions. 
• Based on the meta-model, to define an algorithm for automated transformation of the scenario into an emulated cyber security exercise environment.

Contact:

Task Leader T3.9, Basel Katt

Postdoctoral researcher, Shao-Fang Wen

Network softwarization changed the way networks are built, managed, and operated. Carefully crafted solutions, being developed, tested, and refined over years have been replaced by software-driven solutions being deployed on standard network and server hardware. Such solutions more and more follow the cloud-native paradigm to enable highly scalable, resilient, and manageable applications. This paradigm embraces incorrect operations, bugs, and failures, and focuses on robust automation to enable updates and changes frequently and predicably without further disturbances.

Due to the inherent dependability of networking applications on fast packet I/O, different realization paths to address this requirement have emerged. Techniques like hardware offloading and protocol-independent packet processing leverage novel, programmable network cards and switching devices. As an alternative to such dedicated hardware, fast packet processing frameworks like DPDK/VPP or kernel-specific solutions, i.e., uni-kernels have recently emerged. Besides, in-network computing has evolved aiming to provide computation on the data path. These different alternatives, while preserving easier implementation of solutions, result in increased complexity of the underlying system and the need to consider those differences during design and operation. While abstractions may enable interoperability between the mentioned frameworks, they will not reduce complexity and the risk of failures. Hence, we need to tackle this complexity and provide mechanisms assuring a secure and reliable operation of the underlying system.

Objective:

• Develop competence, i.e., extend the state of art, for network softwarization.
• Focus will be on mechanisms allowing to improve DevOps-like network management and identify inconsistencies between intended system configuration and actual system configuration, e.g., slicing configuration, resource allocation or degree of isolation, where solutions are immature and innovation potential largest.

Contact:

Task leader T3.10, Thomas Zinner

Integrated PhD,  Sebastian Gilje Grøsvik