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Title: Novel detection and alert scheme for IoT network
Authors: Shen, Xiaoyi
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Abstract: With the rapid advancement of science and technology, IoT networks have gradually grown to larger scales. In the recent years, overall structure of IoT network has become more complex, and the abnormal behavior of networks has also become increasingly complicated. In order to better improve the effectiveness of the IoT network to prevent anomalies, it is necessary to accurately study and analyse the abnormal behavior of network when anomalous behavior occurs, as well to solve and prevent the anomalies that exist in the network, thereby reducing their impact. This dissertation focused on the detection of abnormality with Wireshark and packet analysis system. An alert system also be developed to make an alert when abnormality happens. In order to be more suitable for IoT network, some IoT data is collected and analysed. From the data obtained, the network features can be used to detect the abnormality. Wireshark which is a freeware is then used to evaluate some conclusions about network packets. To improve efficiency and to reduce time delay, the packet analysis system is introduced to receive the network flow and to analyse data in real time. This will do away with human intervention to allow system automation. After detecting the abnormality of the IoT network, an alert system needs to inform the person in charge to resolve the problem. The system can play the alert music, pop up an alert window and print the alert information on the screen. With the conclusion of data analysis, the trust list can be set up to ease further development. With the two detection methods (the Wireshark detection and the packet analysis system) and the alert system, the IoT network abnormality will be detected easily and resolved timely.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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