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

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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [47 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02294604
Contributor : Hal Ifip <>
Submitted on : Monday, September 23, 2019 - 3:49:45 PM
Last modification on : Monday, September 23, 2019 - 3:52:08 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2022-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Julia Meister, Raja Akram, Konstantinos Markantonakis. Deep Learning Application in Security and Privacy – Theory and Practice: A Position Paper. 12th IFIP International Conference on Information Security Theory and Practice (WISTP), Dec 2018, Brussels, Belgium. pp.129-144, ⟨10.1007/978-3-030-20074-9_10⟩. ⟨hal-02294604⟩

Share

Metrics

Record views

7