Deep Learning Application in Security and Privacy – Theory and Practice: A Position Paper - Information Security Theory and Practice Access content directly
Conference Papers Year : 2019

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

Julia A. Meister
  • Function : Author
  • PersonId : 1054598
Raja Naeem Akram
  • Function : Author
  • PersonId : 1036545

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.
Fichier principal
Vignette du fichier
484602_1_En_10_Chapter.pdf (218.75 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02294604 , version 1 (23-09-2019)

Licence

Attribution

Identifiers

Cite

Julia A. Meister, Raja Naeem 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⟩
60 View
23 Download

Altmetric

Share

Gmail Facebook X LinkedIn More