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