EDAS: An Evaluation Prototype for Autonomic Event-Driven Adaptive Security in the Internet of Things

Abstract

In Internet of Things (IoT), the main driving technologies are considered to be tiny sensory objects. These objects cannot host traditional preventive and detective technologies to provide protection against the increasing threat sophistication. Furthermore, these solutions are limited to analyzing particular contextual information, for instance network information or files, and do not provide holistic context for risk analysis and response. Analyzing a part of a situation may lead to false alarms and later to unnecessary and incorrect configurations. To overcome these concerns, we proposed an event-driven adaptive security (EDAS) model for IoT. EDAS aims to observe security events (changes) generated by various things in the monitored IoT environment, investigates any intentional or unintentional risks associated with the events and adapts to it autonomously. It correlates different events in time and space to reduce any false alarms and provides a mechanism to predict attacks before they are realized. Risks are responded to autonomically by utilizing a runtime adaptation ontology. The mitigation action is chosen after assessing essential information, such as the risk faced, user preferences, device capabilities and service requirements. Thus, it selects an optimal mitigation action in a particular adverse situation. The objective of this paper is to investigate EDAS feasibility and its aptitude as a real-world prototype in a remote patient monitoring context. It details how EDAS can be a practical choice for IoT-eHealth in terms of the security, design and implementation features it offers as compared to traditional security controls. We have explained the prototype's major components and have highlighted the key technical challenges.

Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics

“…In order to evaluate the advantages of the proposed architecture, based on the previously-mentioned methods, a simplified approach of the architecture tradeoff analysis method (ATAM) has been selected to perform a comparative analysis [ 40 , 48 , 49 ]. This method is widely used by the research community for architecture evaluation [ 50 , 51 , 52 ]. When the architecture is evaluated, depending on the requirements, different qualities must be analyzed.…”

Managing security trade-offs in the Internet of Things using adaptive security

“…The Risk Adapter utilizes a runtime adaptation ontology and responds to an alarm by selecting an optimal response as per the contextual requirements. The model is extended to a technical specification of a system architecture, and its feasibility is investigated as a real-world artifact in [5]. A description of its major components is given in Table I.…”

“…The Risk Adapter components, as shown in Fig. 7 (excluding the Object and Alarm), are the necessary vocabulary in the adaptation ontology proposed in [5]. It is accessed as per the scenario to formulate the adaptation (trade-off) decision which is sent to the Event Source as an adaptation request (AdaptRequest).…”

“…However, due to the IoT architectural complexity, it is challenging to recognize, assess and model potential trade-off situations using adaptive security. To address adaptive security in IoT, we have proposed and analyzed the feasibility of Event-driven Adaptive Security (EDAS) architecture in [4] and [5]. In this article, we explicate a scenario-based method to evaluate various security tradeoffs using EDAS.…”

Gerência de Autenticação de Dispositivos IoT Adaptativa Aos Ambientes Urbanos Apoiada em Políticas e Confiança Social

“…Os SAAs de IoT que consideram o contexto (ambiente) como fator de adaptação simplesmente alteram o mecanismo de autenticação à medida que o dispositivo se move entre ambientes [Aman and Snekkenes 2015]. E há aqueles que exploram a percepção sobre o risco como fator de adaptação, ao detectar mudanças do risco derivado da localização, do tráfego de rede e de mensagens trocadas pelo dispositivo [Sylla et al 2020].…”