Software-based Security Framework for Edge and Mobile IoT

• URL: http://arxiv.org/abs/2404.06435v1
• Date: Tue, 9 Apr 2024 16:25:13 GMT
• Title: Software-based Security Framework for Edge and Mobile IoT
• Authors: José Cecílio, Alan Oliveira de Sá, André Souto,
• Abstract summary: This work focuses on designing secure communication among remote servers and embedded IoT devices. The proposed approach uses lightweight cryptography, optimizing device performance and security without overburdening their limited resources.
• Score: 0.5735035463793009
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: With the proliferation of Internet of Things (IoT) devices, ensuring secure communications has become imperative. Due to their low cost and embedded nature, many of these devices operate with computational and energy constraints, neglecting the potential security vulnerabilities that they may bring. This work-in-progress is focused on designing secure communication among remote servers and embedded IoT devices to balance security robustness and energy efficiency. The proposed approach uses lightweight cryptography, optimizing device performance and security without overburdening their limited resources. Our architecture stands out for integrating Edge servers and a central Name Server, allowing secure and decentralized authentication and efficient connection transitions between different Edge servers. This architecture enhances the scalability of the IoT network and reduces the load on each server, distributing the responsibility for authentication and key management.

Related papers

• A Comprehensive Framework for Building Highly Secure, Network-Connected Devices: Chip to App [1.4732811715354452]
  This paper proposes a holistic approach to securing network-connected devices. At the hardware level, we focus on secure key management, reliable random number generation, and protecting critical assets. For secure communication, we emphasize TLS 1.3 and optimized cipher suites tailored for both standard and resource-constrained devices.
  arXiv  Detail & Related papers  (2025-01-23T14:44:34Z)
• Secure Resource Allocation via Constrained Deep Reinforcement Learning [49.15061461220109]
  We present SARMTO, a framework that balances resource allocation, task offloading, security, and performance. SARMTO consistently outperforms five baseline approaches, achieving up to a 40% reduction in system costs. These enhancements highlight SARMTO's potential to revolutionize resource management in intricate distributed computing environments.
  arXiv  Detail & Related papers  (2025-01-20T15:52:43Z)
• An Efficiency Firmware Verification Framework for Public Key Infrastructure with Smart Grid and Energy Storage System [0.6757476692230008]
  Rapid evolution of smart grids has attracted numerous nation-state actors seeking to disrupt the power infrastructure of adversarial nations. We propose a digital signing and verification framework grounded in Public Key Infrastructure (PKI), specifically tailored for resource-constrained devices such as smart meters.
  arXiv  Detail & Related papers  (2025-01-10T05:43:31Z)
• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• zk-IoT: Securing the Internet of Things with Zero-Knowledge Proofs on Blockchain Platforms [0.0]
  This paper introduces the zk-IoT framework, a novel approach to enhancing the security of Internet of Things (IoT) ecosystems. Our framework ensures the integrity of firmware execution and data processing in potentially compromised IoT devices.
  arXiv  Detail & Related papers  (2024-02-13T09:34:23Z)
• Wireguard: An Efficient Solution for Securing IoT Device Connectivity [0.0]
  The proliferation of vulnerable Internet-of-Things (IoT) devices has enabled large-scale cyberattacks. This research evaluates if Wireguard, an emerging VPN protocol, can provide efficient security tailored for resource-constrained IoT systems.
  arXiv  Detail & Related papers  (2024-02-03T09:11:11Z)
• Securing Cloud-Based Internet of Things: Challenges and Mitigations [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)
• 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)
• Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs [62.91362897985057]
  An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption.
  arXiv  Detail & Related papers  (2021-10-04T19:41:42Z)
• Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks [96.24723959137218]
  We study an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) We propose a novel framework, called federated edge intelligence (FEI), that allows edge servers to evaluate the required number of data samples according to the energy cost of the IoT network. We prove that our proposed algorithm does not cause any data leakage nor disclose any topological information of the IoT network.
  arXiv  Detail & Related papers  (2020-11-25T12:51:59Z)
• Smart Home, security concerns of IoT [91.3755431537592]
  The IoT (Internet of Things) has become widely popular in the domestic environments. People are renewing their homes into smart homes; however, the privacy concerns of owning many Internet connected devices with always-on environmental sensors remain insufficiently addressed. Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices.
  arXiv  Detail & Related papers  (2020-07-06T10:36:11Z)

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.