Pervasive IoT applications enable us to perceive, analyze, control, and optimize the traditional physical systems. Recently, security breaches in many IoT applications have indicated that IoT applications may put the physical systems at risk. Severe resource constraints and insufficient security design are two major causes of many security problems in IoT applications. As an extension of the cloud, the emerging edge computing with rich resources provides us a new venue to design and deploy novel security solutions for IoT applications. Although there are some research efforts in this area, edge-based security designs for IoT applications are still in its infancy. This paper aims to present a comprehensive survey of existing IoT security solutions at the edge layer as well as to inspire more edge-based IoT security designs. We first present an edge-centric IoT architecture. Then, we extensively review the edge-based IoT security research efforts in the context of security architecture designs, firewalls, intrusion detection systems, authentication and authorization protocols, and privacy-preserving mechanisms. Finally, we propose our insight into future research directions and open research issues.
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Generative Adversarial Networks: A Survey Toward Private and Secure Applications
Generative Adversarial Networks (GANs) have promoted a variety of applications in computer vision and natural language processing, among others, due to its generative model’s compelling ability to generate realistic examples plausibly drawn from an existing distribution of samples. GAN not only provides impressive performance on data generation-based tasks but also stimulates fertilization for privacy and security oriented research because of its game theoretic optimization strategy. Unfortunately, there are no comprehensive surveys on GAN in privacy and security, which motivates this survey to summarize systematically. The existing works are classified into proper categories based on privacy and security functions, and this survey conducts a comprehensive analysis of their advantages and drawbacks. Considering that GAN in privacy and security is still at a very initial stage and has imposed unique challenges that are yet to be well addressed, this article also sheds light on some potential privacy and security applications with GAN and elaborates on some future research directions.
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- Award ID(s):
- 1741338
- NSF-PAR ID:
- 10315435
- Date Published:
- Journal Name:
- ACM Computing Surveys
- Volume:
- 54
- Issue:
- 6
- ISSN:
- 0360-0300
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3459992
