A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT

• URL: http://arxiv.org/abs/2404.03276v1
• Date: Thu, 4 Apr 2024 08:00:12 GMT
• Title: A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
• Authors: Marco Arazzi, Serena Nicolazzo, Antonino Nocera,
• Abstract summary: We propose a complete framework that defines suitable levels of privacy and security requirements in the acquisition of services in the Internet of Things. Through the use of a Reinforcement Learning based solution, a user agent, inside the environment, is trained to choose the best smart objects granting access to the target services.
• Score: 2.765106384328772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The novel Internet of Things (IoT) paradigm is composed of a growing number of heterogeneous smart objects and services that are transforming architectures and applications, increasing systems' complexity, and the need for reliability and autonomy. In this context, both smart objects and services are often provided by third parties which do not give full transparency regarding the security and privacy of the features offered. Although machine-based Service Level Agreements (SLA) have been recently leveraged to establish and share policies in Cloud-based scenarios, and also in the IoT context, the issue of making end users aware of the overall system security levels and the fulfillment of their privacy requirements through the provision of the requested service remains a challenging task. To tackle this problem, we propose a complete framework that defines suitable levels of privacy and security requirements in the acquisition of services in IoT, according to the user needs. Through the use of a Reinforcement Learning based solution, a user agent, inside the environment, is trained to choose the best smart objects granting access to the target services. Moreover, the solution is designed to guarantee deadline requirements and user security and privacy needs. Finally, to evaluate the correctness and the performance of the proposed approach we illustrate an extensive experimental analysis.

Related papers

• Towards Secure Management of Edge-Cloud IoT Microservices using Policy as Code [6.200058263544999]
  IoT application providers increasingly use MicroService Architecture (MSA) to develop applications that convert IoT data into valuable information. The proposed framework contains a "control plane" to intelligently and dynamically utilise and configure cloud-native (i.e., container orchestrators and service mesh) technologies to enforce security policies. We implement a prototype of the proposed framework using open-source cloud-native technologies such as Docker, Istio, and Open Policy Agent to validate the framework.
  arXiv  Detail & Related papers  (2024-06-27T01:03:23Z)
• Adaptive Lightweight Security for Performance Efficiency in Critical Healthcare Monitoring [1.1874952582465603]
  The Internet of Things (IoT) with its diverse technologies has become an integral component of future healthcare systems. The evolving healthcare paradigm requires adaptive security procedures and technologies that can adapt to the varying resource constraints of IoT devices. This article brings forth the unique healthcare monitoring requirements and studies the existing encryption-based security approaches to provide the necessary security.
  arXiv  Detail & Related papers  (2024-06-06T06:55:16Z)
• Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems [62.252355444948904]
  This paper presents the findings of a literature review on the integration of edge intelligence (EI) and socialized learning (SL) SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents. We elaborate on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses.
  arXiv  Detail & Related papers  (2024-04-20T11:07:29Z)
• IoT in the Cloud: Exploring Security Challenges and Mitigations for a Connected World [18.36339203254509]
  The Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks. IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers.
  arXiv  Detail & Related papers  (2024-02-01T05:55:43Z)
• Service Level Agreements and Security SLA: A Comprehensive Survey [51.000851088730684]
  This survey paper identifies state of the art covering concepts, approaches, and open problems of SLA management. It contributes by carrying out a comprehensive review and covering the gap between the analyses proposed in existing surveys and the most recent literature on this topic. It proposes a novel classification criterium to organize the analysis based on SLA life cycle phases.
  arXiv  Detail & Related papers  (2024-01-31T12:33:41Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• A Survey of the Security Challenges and Requirements for IoT Operating Systems [0.0]
  The Internet of Things (IoT) is becoming an integral part of our modern lives as we converge towards a world surrounded by ubiquitous connectivity. The inherent complexity presented by the vast IoT ecosystem ends up in an insufficient understanding of individual system components and their interactions. There is a need for a unifying operating system (OS) that can act as a cornerstone regulating the development of stable and secure solutions.
  arXiv  Detail & Related papers  (2023-10-27T19:19:07Z)
• The Internet of Senses: Building on Semantic Communications and Edge Intelligence [67.75406096878321]
  The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human receptors' We elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms may satisfy the requirements of IoS use cases.
  arXiv  Detail & Related papers  (2022-12-21T03:37:38Z)
• HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection [0.0]
  We propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.
  arXiv  Detail & Related papers  (2022-04-08T19:06:16Z)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• A Privacy-Preserving Distributed Architecture for Deep-Learning-as-a-Service [68.84245063902908]
  This paper introduces a novel distributed architecture for deep-learning-as-a-service. It is able to preserve the user sensitive data while providing Cloud-based machine and deep learning services.
  arXiv  Detail & Related papers  (2020-03-30T15:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.