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Open AccessArticle

IMLADS: Intelligent Maintenance and Lightweight Anomaly Detection System for Internet of Things

by Tao Qin 1,2,*, Bo Wang 1, Ruoya Chen 1, Zunying Qin 1,3 and Lei Wang 1
1
MOE KLINNS Lab, Xi’an Jiaotong University, Xi’an 710049, China
2
Shenzhen Research School, Xi’an Jiaotong University, Shenzhen 518057, China
3
Network Information Center, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 958; https://doi.org/10.3390/s19040958
Received: 19 December 2018 / Revised: 9 February 2019 / Accepted: 20 February 2019 / Published: 24 February 2019
System security monitoring has become more and more difficult with the ever-growing complexity and dynamicity of the Internet of Things (IoT). In this paper, we develop an Intelligent Maintenance and Lightweight Anomaly Detection System (IMLADS) for efficient security management of the IoT. Firstly, unlike the traditional system use static agents, we employ the mobile agent to perform data collection and analysis, which can automatically transfer to other nodes according to the pre-set monitoring task. The mobility is handled by the mobile agent running platform, which is irrelevant with the node or its operation system. Combined with this technology, we can greatly reduce the number of agents running in the system while increasing the system stability and scalability. Secondly, we design different methods for node level and system level security monitoring. For the node level security monitoring, we develop a lightweight data collection and analysis method which only occupy little local computing resources. For the system level security monitoring, we proposed a parameter calculation method based on sketch, whose computational complexity is constant and irrelevant with the system scale. Finally, we design agents to perform suitable response policies for system maintenance and abnormal behavior control based on the anomaly mining results. The experimental results based on the platform constructed show that the proposed method has lower computational complexity and higher detection accuracy. For the node level monitoring, the time complexity is reduced by 50% with high detection accuracy. For the system level monitoring, the time complexity is about 1 s for parameter calculation in a middle scale IoT network. View Full-Text
Keywords: Internet of Things; system security monitoring; intelligent maintenance; lightweight anomaly detection; mobile agent Internet of Things; system security monitoring; intelligent maintenance; lightweight anomaly detection; mobile agent
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MDPI and ACS Style

Qin, T.; Wang, B.; Chen, R.; Qin, Z.; Wang, L. IMLADS: Intelligent Maintenance and Lightweight Anomaly Detection System for Internet of Things. Sensors 2019, 19, 958.

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