Options
Ylianttila, Mika
Preferred name
Ylianttila, Mika
Official Name
Ylianttila, Mika
Research Output
Now showing 1 - 5 of 5
- PublicationProxy re-encryption enabled secure and anonymous IoT data sharing platform based on blockchain(Elsevier, 2021-02-15)
; ; ; ; Data is central to the Internet of Things (IoT) ecosystem. With billions of devices connected, most of the current IoT systems are using centralized cloud-based data sharing systems, which will be difficult to scale up to meet the demands of future IoT systems. The involvement of such a third-party service provider requires also trust from both the sensor owner and sensor data user. Moreover, fees need to be paid for their services. To tackle both the scalability and trust issues and to automatize the payments, this paper presents a blockchain-based marketplace for sharing of the IoT data. We also use a proxy re-encryption scheme for transferring the data securely and anonymously, from data producer to the consumer. The system stores the IoT data in cloud storage after encryption. To share the collected IoT data, the system establishes runtime dynamic smart contracts between the sensor and data consumer without the involvement of a trusted third-party. It also uses a very efficient proxy re-encryption scheme which allows that the data is only visible by the owner and the person present in the smart contract. This novel combination of smart contracts with proxy re-encryption provides an efficient, fast and secure platform for storing, trading and managing sensor data. The proposed system is implemented using off-the-shelf IoT sensors and computer devices. We also analyze the performance of our hybrid system by using the permission-less Ethereum blockchain and compare it to the IBM Hyperledger Fabric, a permissioned blockchain.369Scopus© Citations 72 - PublicationFog Computing and Blockchain based Security Service Architecture for 5G Industrial IoT enabled Cloud ManufacturingRecent evolution of the Industrial Internet of Things (IIoT) empowers the classical manufacturing model with cloud computing integration for Industry 4.0. Cloud integration advances the capabilities of manufacturing systems with cloud-based controlling and real-time process monitoring which is renowned as Cloud Manufacturing(CM). However, cloud integration exposes the entire manufacturing ecosystem to a new set of security risks and increment in end-to-end latency. Moving security services towards the edge eradicates message routing latency towards the cloud and eliminates the central point of failure while leveraging the entire system performance. We propose a blockchain and fog computing enabled security service architecture that operates on fog nodes at the edge of manufacturing equipment clusters. The proposed service facilitates CM equipment authentication and Equipment-Cloud channel privacy protection while preserving anonymity and unlinkability over the blockchain. We implemented the proposed architecture with Hyperledger Fabric and compared the performance advantage over the state of art solutions.
38Scopus© Citations 38 - PublicationThe Roadmap to 6G Security and Privacy(IEEE, 2021-05-10)
; ; ; ; ; Although the fifth generation (5G) wireless networks are yet to be fully investigated, the visionaries of the 6th generation (6G) echo systems have already come into the discussion. Therefore, in order to consolidate and solidify the security and privacy in 6G networks, we survey how security may impact the envisioned 6G wireless systems, possible challenges with different 6G technologies, and the potential solutions. We provide our vision on 6G security and security key performance indicators (KPIs) with the tentative threat landscape based on the foreseen 6G network architecture. Moreover, we discuss the security and privacy challenges that may encounter with the available 6G requirements and potential 6G applications. We also give the reader some insights into the standardization efforts and research-level projects relevant to 6G security. In particular, we discuss the security considerations with 6G enabling technologies such as distributed ledger technology (DLT), physical layer security, distributed AI/ML, visible light communication (VLC), THz, and quantum computing. All in all, this work intends to provide enlightening guidance for the subsequent research of 6G security and privacy at this initial phase of vision towards reality.368Scopus© Citations 149 - PublicationFederated Learning based Anomaly Detection as an Enabler for Securing Network and Service Management Automation in Beyond 5G Networks(IEEE, 2022-06-10)
; ; ; ; Network automation is a necessity in order to meet the unprecedented demand in the future networks and zero touch network architecture is proposed to cater such requirements. Closed-loop and artificial intelligence are key enablers in this proposed architecture in critical elements such as security. Apart from the arising privacy concerns, machine learning models can also face resource limitations. Federated learning is a machine learning-based technique that addresses both privacy and com- munication efficiency issues. Therefore, we propose a federated learning-based model incorporating the ZSM architecture for network automation. The paper also contains the simulations and results of the proposed multi-stage federated learning model that uses the UNSW-NB15 dataset.305Scopus© Citations 11 - PublicationSurvey on Blockchain based Smart Contracts: Technical Aspects and Future Research(IEEE, 2021-03-23)
; ; ; ; Internet of Things (IoT) is an emerging technology that makes people’s lives smart by conquering a plethora of diverse application and service areas. In near future, the fifth-generation (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e. slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning(AI/ML) in network slicing and IoT integration567Scopus© Citations 61