2026-09-19T04:08:29Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1215972026-06-06T00:54:52Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Michael Siegel.
Chu, Weilian.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-07-12T17:40:38Z
2019-07-12T17:40:38Z
2018
2018
https://hdl.handle.net/1721.1/121597
1098171923
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 67-68).
The Internet of Things (IoT) is starting to take off in the modern day tech scene, with almost every user device being connected to a huge network with other devices; industrial energy delivery systems are no different. However, IoT in industry (known as IIoT) severely lags behind conventional IoT networks when it comes to cybersecurity, as IIoT endpoint devices generally lack the same level of memory and computation as conventional IoT endpoints. As a result, the same level of security measurements can't be implemented, and IIoT devices often are vulnerable to malicious users attempting to hack the device. The goal of this research is to create a lightweight software system that protects the endpoint devices from hackers, as well as prevent malicious accesses from impacting other parts of the system. This thesis will focus on the command and control unit, where the aim is to develop a neural net classifier to detect anomalies in network traffic. This thesis details the background surrounding IIoT endpoint devices, and the current attempts that have been made at providing a solution, as well as our approach to solving this problem, using both supervised and unsupervised machine learning approaches, and describe the testing environment with which the experiments were conducted. In the end we discuss how the work contributes to the future progress of IIoT security.
by Weilian Chu.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
68 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Evaluating effectiveness of an embedded system endpoint security technology on energy delivery systems
Thesis
application/pdf
2019-07-12T17:40:36Z
Publication
Master
EECS
unknown
unknown
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<Abstract>The Internet of Things (IoT) is starting to take off in the modern day tech scene, with almost every user device being connected to a huge network with other devices; industrial energy delivery systems are no different. However, IoT in industry (known as IIoT) severely lags behind conventional IoT networks when it comes to cybersecurity, as IIoT endpoint devices generally lack the same level of memory and computation as conventional IoT endpoints. As a result, the same level of security measurements can't be implemented, and IIoT devices often are vulnerable to malicious users attempting to hack the device. The goal of this research is to create a lightweight software system that protects the endpoint devices from hackers, as well as prevent malicious accesses from impacting other parts of the system. This thesis will focus on the command and control unit, where the aim is to develop a neural net classifier to detect anomalies in network traffic. This thesis details the background surrounding IIoT endpoint devices, and the current attempts that have been made at providing a solution, as well as our approach to solving this problem, using both supervised and unsupervised machine learning approaches, and describe the testing environment with which the experiments were conducted. In the end we discuss how the work contributes to the future progress of IIoT security.</Abstract>
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