Deep Learning Application in Security and Privacy - Theory and Practice: A Position Paper

Julia A. Meister, Raja Naeem Akram, Konstantinos Markantonakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Technology is shaping our lives in a multitude of ways. This is fuelled by a technology infrastructure, both legacy and state of the art, composed of a heterogeneous group of hardware, software, services, and organisations. Such infrastructure faces a diverse range of challenges to its operations that include security, privacy, resilience, and quality of services. Among these, cybersecurity and privacy are taking the centre-stage, especially since the General Data Protection Regulation (GDPR) came into effect. Traditional security and privacy techniques are overstretched and adversarial actors have evolved to design exploitation techniques that circumvent protection. With the ever-increasing complexity of technology infrastructure, security and privacy-preservation specialists have started to look for adaptable and flexible protection methods that can evolve (potentially autonomously) as the adversarial actor changes its techniques. For this, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) were put forward as saviours. In this paper, we look at the promises of AI, ML, and DL stated in academic and industrial literature and evaluate how realistic they are. We also put forward potential challenges a DL based security and privacy protection system has to overcome. Finally, we conclude the paper with a discussion on what steps the DL and the security and privacy-preservation community have to take to ensure that DL is not just going to be hype, but an opportunity to build a secure, reliable, and trusted technology infrastructure on which we can rely on for so much in our lives.
Original languageEnglish
Title of host publicationInformation Security Theory and Practice
Subtitle of host publication12th IFIP WG 11.2 International Conference, WISTP 2018, Brussels, Belgium, December 10–11, 2018, Revised Selected Papers
EditorsOlivier Blazy, Chan Yeob Yeun
Place of PublicationBrussels, Belgium
PublisherSpringer Verlag
Pages129-144
Number of pages15
ISBN (Electronic)978-3-030-20074-9
ISBN (Print)978-3-030-20073-2
DOIs
Publication statusPublished - 7 Nov 2018
Event12th IFIP WG 11.2 International Conference: WISTP 2018 - Brussells, Belgium
Duration: 10 Dec 201811 Dec 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Print)0302-9743

Conference

Conference12th IFIP WG 11.2 International Conference
CountryBelgium
CityBrussells
Period10/12/1811/12/18

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  • Cite this

    Meister, J. A., Akram, R. N., & Markantonakis, K. (2018). Deep Learning Application in Security and Privacy - Theory and Practice: A Position Paper. In O. Blazy, & C. Yeob Yeun (Eds.), Information Security Theory and Practice: 12th IFIP WG 11.2 International Conference, WISTP 2018, Brussels, Belgium, December 10–11, 2018, Revised Selected Papers (pp. 129-144). (Lecture Notes in Computer Science). Springer Verlag. https://doi.org/10.1007/978-3-030-20074-9