A machine learning-based approach to detect threats in bio-cyber DNA storage systems

Federico Tavella, Alberto Giaretta, Mauro Conti, Sasitharan Balasubramaniam

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)59-70
Number of pages12
JournalComputer Communications
Volume187
DOIs
Publication statusPublished - 01 Apr 2022
Externally publishedYes

Keywords

  • DNA encoding
  • DoS
  • Machine learning
  • Metrics
  • Storage system

Fingerprint

Dive into the research topics of 'A machine learning-based approach to detect threats in bio-cyber DNA storage systems'. Together they form a unique fingerprint.

Cite this