| 1. |
- Pirayesh, Jamshid, et al.
(author)
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A PLS-HECC-based device authentication and key agreement scheme for smart home networks
- 2022
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In: Computer Networks. - : Elsevier. - 1389-1286 .- 1872-7069. ; 216
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Journal article (peer-reviewed)abstract
- IoT devices permeate our society, collect personal data, and support critical infrastructures such as the healthcare. Therefore, there is a critical need for authentication and authorization schemes for IoT devices to meet privacy requirements, such as mutual authentication and user anonymity, as well as robustness against security attacks. In this paper, we propose a device authentication and key agreement scheme for IoT networks. Our proposal takes as a model the scheme proposed by Rezai et al., and combines it with a physical layer security technique and a hyper-elliptic curve cryptosystem. Our results show that not only our authentication scheme provides anonymity, mutual authentication, and efficiency, but it also provides resilience to various attacks, including man-in-the-middle, replay, and de-synchronization attacks. Our comparison shows that our scheme performs better than the state-of-the-art in terms of security properties, while adding a small overhead of ≈ 10(ms).
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| 2. |
- Tavella, Federico, et al.
(author)
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A machine learning-based approach to detect threats in bio-cyber DNA storage systems
- 2022
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In: Computer Communications. - : Elsevier. - 0140-3664 .- 1873-703X. ; 187, s. 59-70
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Journal article (peer-reviewed)abstract
- Data storage is one of the main computing issues of this century. Not only storage devices are converging to strict physical limits, but also the amount of data generated by users is growing at an unbelievable rate. To face these challenges, data centres grew constantly over the past decades. However, this growth comes with a price, particularly from the environmental point of view. Among various promising media, DNA is one of the most fascinating candidate. In our previous work, we have proposed an automated archival architecture which uses bioengineered bacteria to store and retrieve data, previously encoded into DNA. The similarities between biological media and classical ones can be a drawback, as malicious parties might replicate traditional attacks on the former archival system, using biological instruments and techniques. In this paper, first we analyse the main characteristics of our storage system and the different types of attacks that could be executed on it. Then, aiming at identifying on-going attacks, we propose and evaluate detection techniques, which rely on traditional metrics and machine learning algorithms. We identify and adapt two suitable metrics for this purpose, namely generalized entropy and information distance.
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