Artificial Intelligence/ Machine Learning in IoT for Authentication and Authorization of Edge Devices

Muhammad Sharjeel Zareen, Shahzaib Tahir, M. Akhlaq, B. Aslam
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引用次数: 5

Abstract

Internet of Things (IoT) is progressing at a fast pace. Issues of security and privacy, emerged with introduction of IoT in late nineties, are still amongst the main challenges. In security issues, authentication and authorization of edge devices are main concerns due to resource constrained nature of edge devices. Various solutions have been proposed in the past to address said concerns but most of the solutions are based on increasing the computational capacity, storage and power in edge devices. However, said solutions are not practical since these solutions are either not possible due to small size of edge devices of IoT or not economical for their wide spread adoption. Some of the solutions also suggest the use of light weight cryptographic primitives. However, same are also not practical since all edge devices do not have requisite resources to implement these solutions. This paper proposes use of Artificial Intelligence (AI)/ machine learning in addressing the issues of authentication and authorization in edge devices. Proposed solution is based on fog computing model within a framework of a smart house but without reliance on computational capacity, storage or power of edge devices.
物联网中用于边缘设备认证和授权的人工智能/机器学习
物联网(IoT)正在快速发展。随着90年代末物联网的引入,安全和隐私问题仍然是主要挑战之一。在安全问题中,由于边缘设备资源的有限性,边缘设备的认证和授权是人们关注的主要问题。过去已经提出了各种解决方案来解决这些问题,但大多数解决方案都是基于增加边缘设备的计算能力、存储和功率。然而,上述解决方案并不实用,因为这些解决方案要么由于物联网边缘设备的小尺寸而不可能实现,要么由于其广泛采用而不经济。一些解决方案还建议使用轻量级加密原语。然而,由于所有边缘设备都没有必要的资源来实现这些解决方案,因此这些解决方案也不实用。本文建议使用人工智能(AI)/机器学习来解决边缘设备中的身份验证和授权问题。提出的解决方案是基于智能房屋框架内的雾计算模型,但不依赖于边缘设备的计算能力、存储或功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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