Cloud, Networking and Security
The Cloud, Network and Security (CNS) team is BTH’s Center-of -Excellence (CoE) for researching and teaching novel methods and techniques for a secure and efficient design, implementation and operation of next generation Cloud systems, Next Generation Internet (NGI) systems and applications, and networks (including the Future Internet, FI). Hereby, the CNS cluster focuses on:
- Functionalities and infrastructures for future network, the NGI and Cloud computing, such as virtualisation, autonomic operation, self-protection, Software-Defined Networking (SDN), Network Function Virtualisation (NFV), and Internet-of- Things (IoT).
- An Internet for the People and its applications, which fulfil user and societal requirements such as reliability, safety, security and privacy, including EU’s GDPR.
- Machine learning techniques and Anomaly Detection for Security, i.e. finding patterns in data that do not conform to an established “normal” behaviour. are used to analyse irregularities in large sets of data.
The CNS team applies a wide range of methods, comprising techniques for distributed systems, Clouds and networks, distributed algorithms and protocols, software engineering, performance evaluation, Big Data analysis, social network analysis, computer system forensics, malware analysis, and penetration testing.
The team is constantly growing. It comprises two full professors, one associated professor, four assistant professors, two university adjuncts, and four Ph.D. students. The CNS team hosts currently four larger and funded research and teaching projects in security and privacy.
- ENGENSEC – Educating the Next generation experts in Cyber Security: the new EU-recognized Master’s program (ENGENSEC)
- COST IC 1304 ACROSS – Autonomous Control for a Reliable Internet of Services (ACROSS)
- CONVINcE – Consumption OptimizatioN in VIdeo NEtworks
- CCC – Center for Computational Crimonology
- XI-FI – eXperimental Infrastructures for the Future Internet
BTH is Sweden’s only university which offers a Swedish Master of Science in Engineering (Civilingenjör) in Computer and IT security. The CNS team is responsible for teaching the core modules of this educational program.
The programs offered by BTH and taught by the members of the CNS team are:
Civilingenjörsutbildningar (Swedish Master of Science in Engineering)
Högskoleingenjörsutbildningar (Swedish Bachelor in Engineering)
Two-Year MSc Programmes
Selected Publications (To be completed)
- T. Llewellyn, et al.: BONSEYES: Platform for Open Development of Systems of Artificial Intelligence
- V. Ahmadi Mehri, K. Tutschku: Privacy and trust in cloud-based marketplaces for AI and data resources, IFIP Advances in Information and Communication Technology, Springer New York LLC, 2017
- M. Boldt, A. Jacobsson, D. Baca and B. Carlsson, ”Introducing a novel security-enhanced agile software development process”, in International Journal of Secure Software Engineering, to appear, volume 8, issue 2, 2017.
- A. Jacobsson, M. Boldt and B. Carlsson, ”A risk analysis of a smart home automation systems”, in Future Generation Computer Systems 56 (2016): 719-733.
- Upcoming: 4th IEEE Conference on Network Function Virtualization and Software Defined Networks (IEEE NFV-SDN2018), Oct/Nov 2018 – Verona, Italy.
- Upcoming: 14th Swedish National Computer Networking Workshop (SNCNW 2018)
- 1st Workshop on Autonomic Management of Large Scale Container-Based Systems (Co-located with the 2017 IEEE International Conference on Cloud and Autonomic Computing, ICCAC)
- Remote Security Lab (ReSeLa)
- Key features
- Open virtual testbed for research and teaching security method
- Permit concurrent security experiments in slices
- Based on OpenStack
- Cybersecurity education in Sweden, Poland, Russia and Ukrain
- Part of Testbed Blekinge
- Key features
- Network Performance Lab
- Key features
- Open platform for precise network performance management
- Test area for next generation software defined infrastructure
- Emulation of user devices (desktop, laptop, smartphones) and different network/system conditions
- Developed within and contributed to over five EU and national research projects
- Supported of three Phd thesis and more than 30 MSc thesis
- Key features