DEEP LEARNING FOR SECURING CRITICAL INFRASTRUCTURE WITH THE EMPHASIS ON POWER SYSTEMS AND WIRELESS COMMUNICATION

Description

<p><em>Imbulgoda Liyangahawatte, Gihan Janith Mendis Ph.D., Purdue University, May</em></p>
<p><em>2023. Deep learning for securing critical infrastructure with the emphasis on power</em></p>
<p><em>systems and wireless communication. Major Professor: Dr. Jin Kocsis.</em></p>
<p><br></p>
<p><em>Critical infrastructures, such as power systems and communication</em></p>
<p><em>infrastructures, are of paramount importance to the welfare and prosperity of</em></p>
<p><em>modern societies. Therefore, critical infrastructures have a high vulnerability to</em></p>
<p><em>attacks from adverse parties. Subsequent to the advancement of cyber technologies,</em></p>
<p><em>such as information technology, embedded systems, high-speed connectivity, and</em></p>
<p><em>real-time data processing, the physical processes of critical infrastructures are often</em></p>
<p><em>monitored and controlled through cyber systems. Therefore, modern critical</em></p>
<p><em>infrastructures are often viewed as cyber-physical systems (CPSs). Incorporating</em></p>
<p><em>cyber elements into physical processes increases efficiency and control. However, it</em></p>
<p><em>also increases the vulnerability of the systems to potential cybersecurity threats. In</em></p>
<p><em>addition to cyber-level attacks, attacks on the cyber-physical interface, such as the</em></p>
<p><em>corruption of sensing data to manipulate physical operations, can exploit</em></p>
<p><em>vulnerabilities in CPSs. Research on data-driven security methods for such attacks,</em></p>
<p><em>focusing on applications related to electrical power and wireless communication</em></p>
<p><em>critical infrastructure CPSs, are presented in this dissertation. As security methods</em></p>
<p><em>for electrical power systems, deep learning approaches were proposed to detect</em></p>
<p><em>adversarial sensor signals targeting smart grids and more electric aircraft.</em></p>
<p><em>Considering the security of wireless communication systems, deep learning solutions</em></p>
<p><em>were proposed as an intelligent spectrum sensing approach and as a primary user</em></p>
<p><em>emulation (PUE) attacks detection method on the wideband spectrum. The recent</em></p>
<p><em>abundance of micro-UASs can enable the use of weaponized micro-UASs to conduct</em></p>
<p><em>physical attacks on critical infrastructures. As a solution for this, the radio</em></p>
<p><em>frequency (RF) signal-analyzing deep learning method developed for spectrum</em></p>
<p><em>sensing was adopted to realize an intelligent radar system for micro-UAS detection.</em></p>
<p><em>This intelligent radar can be used to provide protection against micro-UAS-based</em></p>
<p><em>physical attacks on critical infrastructures.</em></p>

Links & Downloads

1. 10.25394/pgs.22704568.v1

Tags

System and network security

Deep learning

Deep Learning

Cyber-physical system security

Wireless Communication System

Drone detection radar

Power system cybersecurity

Additional Fields

Identifer	oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22704568
Date	27 April 2023
Creators	Gihan janith mendis Imbulgoda liyangahawatte (10488467)
Source Sets	Purdue University
Detected Language	English
Type	Text, Thesis
Rights	CC BY 4.0
Relation	https://figshare.com/articles/thesis/DEEP_LEARNING_FOR_SECURING_CRITICAL_INFRASTRUCTURE_WITH_THE_EMPHASIS_ON_POWER_SYSTEMS_AND_WIRELESS_COMMUNICATION/22704568