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

Sensors
Tao QinLei Wang

Abstract

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 sui...Continue Reading

References


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Citations

Apr 11, 2019·Sensors·Chia-Hung YehMei-Juan Chen

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Methods Mentioned

BETA
PCA

Software Mentioned

Windows
Avast
DBSCAN
IMLADS
Redhat Linux
Windows server
LAB

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