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ThinkMind // CLOUD COMPUTING 2018, The Ninth International Conference on Cloud Computing, GRIDs, and Virtualization // View article cloud_computing_2018_3_30_28006


Intruder Detection through Pattern Matching and Provenance Driven Data Recovery

Authors:
Anthony Chapman

Keywords: Cloud Security; Audit; Provenance; Tamper Detection; Data Recovery; GDPR

Abstract:
Intruder detection and recovering tampered data is challenging enough without the added complexity of the cloud or the forthcoming EU General Data Protection Regulation (GDPR), which will put greater pressure on companies to strengthen their cyber security or potentially face large fines. Intruder breach reporting and forensic analysis needs to drastically improve in order to avoid these potentially catastrophic fines. We conducted a conceptual exploration of intruder detection and data recovery methods. This paper aims to encourage further research for effective cloud security assurance with a focus on increased protection from tough legislation, such as complying with the forthcoming GDPR. We propose a framework which uses pattern matching to identify tampered data, provenance models for data assurance and audit trails to recover original data.

Pages: 59 to 64

Copyright: Copyright (c) IARIA, 2018

Publication date: February 18, 2018

Published in: conference

ISSN: 2308-4294

ISBN: 978-1-61208-607-1

Location: Barcelona, Spain

Dates: from February 18, 2018 to February 22, 2018

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