A Machine Learning-based Approach to Detect Threats in Bio-Cyber DNA Storage Systems

• URL: http://arxiv.org/abs/2009.13380v1
• Date: Mon, 28 Sep 2020 14:55:20 GMT
• Title: A Machine Learning-based Approach to Detect Threats in Bio-Cyber DNA Storage Systems
• Authors: Federico Tavella, Alberto Giaretta, Mauro Conti, Sasitharan Balasubramaniam
• Abstract summary: We have proposed an automated archival architecture which uses bioengineered bacteria to store and retrieve data, previously encoded into DNA. The similarities between these biological media and classical ones can also be a drawback, as malicious parties might replicate traditional attacks on the former archival system. 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.
• Score: 20.27498894606937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. This storage technique is one example of how biological media can deliver power-efficient storing solutions. The similarities between these biological media and classical ones can also 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. Moreover, our trained models achieve an AUROC over 0.99 and AUPRC over 0.91.

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