| 1. |
- Pérez, Salvador, et al.
(författare)
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Application Layer Key Establishment for End-to-End Security in IoT
- 2019
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Ingår i: IEEE Internet of Things Journal. - 2372-2541. ; 7:3, s. 2117-2128
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Tidskriftsartikel (refereegranskat)abstract
- In most IoT deployments, intermediate entities are usually employed for efficiency and scalability reasons. These intermediate proxies break end-to-end security when using even the state-of-the-art transport layer security (TLS) solutions. In this direction, the recent Object Security for Constrained RESTful Environments (OSCORE) has been standardized to enable end-to-end security even in the presence of malicious proxies. In this work, we focus on the key establishment process based on application layer techniques. In particular, we evaluate the Ephemeral Diffie-Hellman over COSE (EDHOC), the de facto key establishment protocol for OSCORE. Based on EDHOC, we propose CompactEDHOC, as a lightweight alternative, in which negotiation of security parameters is extracted from the core protocol. In addition to providing end-to-end security properties, we perform extensive evaluation using real IoT hardware and simulation tools. Our evaluation results prove EDHOC-based proposals as an effective and efficient approach for the establishment of a security association in IoT constrained scenarios.
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| 2. |
- Wang, Han, et al.
(författare)
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Machine Learning for Security at the IoT Edge-A Feasibility Study
- 2019
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Ingår i: Proceedings - 2019 IEEE 16th International Conference on Mobile Ad Hoc and Smart Systems Workshops, MASSW 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728141213 - 9781728141220 ; , s. 7-12
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Konferensbidrag (refereegranskat)abstract
- Benefits of edge computing include reduced latency and bandwidth savings, privacy-by-default and by-design in compliance with new privacy regulations that encourage sharing only the minimal amount of data. This creates a need for processing data locally rather than sending everything to a cloud environment and performing machine learning there. However, most IoT edge devices are resource-constrained in comparison and it is not evident whether current machine learning methods are directly employable on IoT edge devices. In this paper, we analyze the state-of-the-art machine learning (ML) algorithms for solving security problems (e.g. intrusion detection) at the edge. Starting from the characteristics and limitations of edge devices in IoT networks, we assess a selected set of commonly used ML algorithms based on four metrics: computation complexity, memory footprint, storage requirement and accuracy. We also compare the suitability of ML algorithms to different cybersecurity problems and discuss the possibility of utilizing these methods for use cases.
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