Deep Learning Application in Security and Privacy - Theory and Practice: A Position Paper
Deep Learning Application in Security and Privacy - Theory and Practice: A Position Paper
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DOI：
10.1007/978-3-030-20074-9_10
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Julia A. Meister;Raja Naeem Akram;K. Markantonakis
中科院分区：
文献类型：
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作者：
Julia A. Meister;Raja Naeem Akram;K. Markantonakis
技术正在以多种方式塑造我们的生活。这是由技术基础设施推动的，既有传统的，也有最先进的，由硬件，软件，服务和组织的异构组组成。这些基础设施的运营面临着各种各样的挑战，包括安全、隐私、弹性和服务质量。其中，网络安全和隐私占据了中心地位，特别是自《通用数据保护条例》（GDPR）生效以来。传统的安全和隐私技术已经捉襟见肘，敌对行为者已经发展到设计绕过保护的利用技术。随着技术基础设施的日益复杂，安全和隐私保护专家已经开始寻找适应性强且灵活的保护方法，这些方法可以随着敌对行为者改变其技术而演变（可能是自主的）。为此，人工智能（AI），机器学习（ML）和深度学习（DL）被提出作为救世主。在本文中，我们研究了学术和工业文献中陈述的AI，ML和DL的承诺，并评估它们的现实程度。我们还提出了基于DL的安全和隐私保护系统必须克服的潜在挑战。最后，我们总结了DL和安全和隐私保护社区必须采取的步骤，以确保DL不仅仅是炒作，而是建立一个安全，可靠和值得信赖的技术基础设施的机会，我们可以在生活中依赖它。
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.