Big Data Analytics-as-a-Service: Bridging the gap between security experts and data scientists

Claudio A. Ardagna, Valerio Bellandi, Ernesto Damiani, Michele Bezzi, Cedric Hebert

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We live in an interconnected and pervasive world where huge amount of data are collected every second. Fully exploiting data through advanced analytics, machine learning and artificial intelligence, becomes crucial for businesses, from micro to large enterprises, resulting in a key advantage (or shortcoming) in the global market competition, as well as in a strong market driver for business analytics solutions. This scenario is deeply changing the security landscape, introducing new risks and threats that affect security and privacy of systems, on one side, and safety of users, on the other side. Many domains that can benefit from novel solutions based on data analytics have stringent security requirements to fulfill. The Energy domain's Smart Grid is a major example of systems at the crossroads of security and data-driven intelligence. The Smart Grid plays a crucial role in modern energy infrastructure. However, it must face two major challenges related to security: managing front-end intelligent devices such as power assets and smart meters securely, and protecting the huge amount of data received from these devices. Starting from these considerations, setting up proper analytics is a complex problem because security controls could have the undesired side effect of decreasing the accuracy of the analytics themselves. This is even more critical when the configuration of security controls is let to the security expert, who often has only basic skills in data science. In this paper, we propose a solution based on the concept of Model-Based Big Data Analytics-as-a-Service (MBDAaaS) that bridges the gap between security experts and data scientists. Our solution acts as a middleware allowing a security expert and a data scientist to collaborate to the deployment of an analytics addressing their needs.

Original languageBritish English
Article number107215
JournalComputers and Electrical Engineering
Volume93
DOIs
StatePublished - Jul 2021

Keywords

  • Artificial intelligence
  • Big Data Analytics
  • Machine learning
  • Security and privacy

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