Employing RNN and Petri Nets to Secure Edge Computing Threats in Smart Cities

IF 4.3 3区材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

ACS Applied Electronic Materials Pub Date : 2024-02-22 DOI:10.1007/s10723-023-09733-3

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引用次数: 0

Abstract

The Industrial Internet of Things (IIoT) revolution has led to the development a potential system that enhances communication among a city's assets. This system relies on wireless connections to numerous limited gadgets deployed throughout the urban landscape. However, technology has exposed these networks to various harmful assaults, cyberattacks, and potential hacker threats, jeopardizing the security of wireless information transmission. Specifically, unprotected IIoT networks act as vulnerable backdoor entry points for potential attacks. To address these challenges, this project proposes a comprehensive security structure that combines Extreme Learning Machines based Replicator Neural Networks (ELM-RNN) with Deep Reinforcement Learning based Deep Q-Networks (DRL-DQN) to safeguard against edge computing risks in intelligent cities. The proposed system starts by introducing a distributed authorization mechanism that employs an established trust paradigm to effectively regulate data flows within the network. Furthermore, a novel framework called Secure Trust-Aware Philosopher Privacy and Authentication (STAPPA), modeled using Petri Net, mitigates network privacy breaches and enhances data protection. The system employs the Garson algorithm alongside the ELM-based RNN to optimize network performance and strengthen anomaly detection capabilities. This enables efficient determination of the shortest routes, accurate anomaly detection, and effective search optimization within the network environment. Through extensive simulation, the proposed security framework demonstrates remarkable detection and accuracy rates by leveraging the power of reinforcement learning.

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利用 RNN 和 Petri 网防范智能城市中的边缘计算威胁

摘要工业物联网（IIoT）革命促使开发了一种潜在的系统，以加强城市资产之间的通信。该系统依赖于与部署在城市各处的众多有限小工具的无线连接。然而，技术使这些网络面临各种有害攻击、网络攻击和潜在的黑客威胁，从而危及无线信息传输的安全性。具体来说，未受保护的物联网网络是潜在攻击的脆弱后门入口。为应对这些挑战，本项目提出了一种综合安全结构，将基于极限学习机的复制器神经网络（ELM-RNN）与基于深度强化学习的深度 Q 网络（DRL-DQN）相结合，以防范智慧城市中的边缘计算风险。拟议的系统首先引入了分布式授权机制，该机制采用既定的信任范式来有效规范网络内的数据流。此外，一个名为 "安全信任感知哲学家隐私和认证（STAPPA）"的新型框架采用 Petri 网建模，可减轻网络隐私泄露并加强数据保护。该系统采用了 Garson 算法和基于 ELM 的 RNN，以优化网络性能并加强异常检测能力。这样就能在网络环境中高效确定最短路径、准确检测异常并有效优化搜索。通过大量仿真，所提出的安全框架利用强化学习的强大功能，展示了出色的检测率和准确率。

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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来源期刊

ACS Applied Electronic Materials Multiple-

CiteScore

7.20

自引率

4.30%

发文量

567