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 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.
Highlights
With the increasing scale and complexity of the Internet of Things (IoT), the security monitoring task of the IoT has become more and more difficult
Sensors 2019, 19, 958 we developed an intelligent maintenance and lightweight anomaly detection system for IoT
This paper mainly focuses on developing an intelligent maintenance and lightweight anomaly detection system for IoT
Summary
With the increasing scale and complexity of the Internet of Things (IoT), the security monitoring task of the IoT has become more and more difficult. We employ mobile agents to design the monitoring system and reduce the computational complexity. The mobile agents perform data collection and analysis using the local computing resource, which is different from the traditional static agents and can greatly reduce the amount of data transferred from the terminal nodes to the control server. For the node level security monitoring, we designed a new method to reduce the computational complexity from two aspects: the process of data collection and the process of anomaly detection. For the system level security monitoring, we employ the number of packets used for communication between different nodes as a parameter to perform anomaly detection. The third one is the quarantine management mobile agent, it transfers to the special node and performs different quarantine policies to control the abnormal behavior while does not affect the normal behavior heavily.
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