Abstract
The main challenge facing the Internet of Things (IoT) in general, and IoT security in particular, is that humans have never handled such a huge amount of nodes and quantity of data. Fortunately, it turns out that Machine Learning (ML) systems are very effective in the presence of these two elements. However, can IoT devices support ML techniques? In this paper, we investigated this issue and proposed a twofold contribution: a thorough study of the IoT paradigm and its intersections with ML from a security perspective; then, we actually proposed a holistic ML-based framework for access control, which is the defense head of recent IT systems. In addition to learning techniques, this second pillar was based on the organization and attribute concepts to avoid role explosion problems and applied to a smart city case study to prove its effectiveness.
Highlights
Access Control (AC) plays a pivotal role in the security world given its mission of protecting digital and physical accesses by delimiting and enforcing who has access to what and in which conditions [1]
We first need to delimit the perimeter covered by the Internet of Things (IoT) by giving a much more representative definition of the term, which will allow us later to tackle the question of AC with a much more appropriate vision, and above all, will lead us to know where and how we can use the power of Machine Learning (ML) to take advantage of the large amount of objects and data we are handling
Attribute Based Access Control [34] (ABAC) is another model that is taking up more and more space recently, its basic concept is to identify subjects and objects through attributes, the permissions are granted according to these attributes, which could be any relevant security-characteristics, this makes Attribute Based Access Control [34] (ABAC), unlike Role Based Access Control [33] (RBAC), more adapted to afford fine-grained AC highly valued in IoT circumstances
Summary
Access Control (AC) plays a pivotal role in the security world given its mission of protecting digital and physical accesses by delimiting and enforcing who has access to what and in which conditions [1]. Relying on a single technique to address an issue that is as complex as IoT is a weakness that confines the performance of many IoT security-oriented models. To fulfil the AC requirements, this paper will progressively build a global framework that focuses on policy management and AC models, and digs deeper into the mechanisms that accurately fit them; which leads to a smooth and coherent Machine Learning (ML) integration going down to highlight what and where ML algorithm(s) should be implemented. The remainder of this paper is presented as follows: Section II exposes an overview of ML applications in IoT scenarios; Sections III and IV reveals the building blocks of the ML-based framework aiming to handle IoT AC, as well as all the required concepts to understand it.
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