期刊
APPLIED SOFT COMPUTING
卷 113, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.asoc.2021.107859
关键词
Botnet; Soft computing; LSTM; Internet of Things; Smart City
资金
- National Research Foun-dation of Korea (NRF) - Korea government [NRF-2019R1A2B5B0107041613]
Smart cities rely on millions of heterogeneous sensors without standard security architecture. An AI-based framework for IoT fleet security management is proposed to enhance device security and prevent the formation of large botnets.
Smart Cities contains millions of IoT sensors supporting critical applications such as Smart Transport, Buildings, Intelligent Vehicles, and Logistics. A central administrator appointed by the government manages and maintains the security of each node. Smart City relies upon millions of sensors that are heterogeneous and do not support standard security architecture. Different manufacturers have weak protection protocols for their products and do not update their firmware upon newly identified operating systems' vulnerabilities. Adversaries using brute force methods exploit the lack of inbuilt security systems on IoT devices to grow their bot network. Smart cities require a standard framework combining soft computing and Deep Learning (DL) for device fleet management and complete control of sensor operating systems for absolute security. This paper presents a real-world application for IoT fleet management security using a lightweight container-based botnet detection (C-BotDet) framework. Using a three-phase approach, the framework using Artificial Intelligence detects compromised IoT devices sending malicious traffic on the network. Balena Cloud revokes API keys and prevents a compromised device from infecting other devices to form a more giant botnet. VPN (Virtual Private Network) prevents inter-device communication and routes all malicious traffic through an external server. The framework quickly updates the standard Linux-based operating system IoT device fleet without relying on different manufacturers to update their system security individually. The simulation and analysis of the C-BotDet framework are presented in a practical working environment to demonstrate its implementation feasibility. (C) 2021 Published by Elsevier B.V.
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