Two researchers in WSU’s School of Electrical Engineering and Computer Science have received faculty Early Career awards from the National Science Foundation.
Monowar Hasan and Yan Yan, assistant professors, each received the five-year, $600,000 awards. The prestigious awards support early-career faculty who are leading research advances and who have the potential to become role models in research and education.
Detecting information leakages in cyber-physical systems
Hasan’s award is for his work to detect and mitigate information leakage in time-critical cyber-physical systems.
Cyber-physical system like those in autonomous vehicles, drones, medical devices, and power plants rely on a mix of complex modules and software. Many of these systems still use older, legacy software that wasn’t built with today’s cybersecurity challenges in mind.
“Understanding how data flows in cyber-physical systems — what’s running, how apps communicate, and how information moves — is already challenging. Network and internet connectivity in newer technologies, like autonomous vehicles, makes it even harder,” Hasan said.
Covert timing channels are unintended communication channels that allow malicious actors to secretly exchange information between software tasks by manipulating the execution timing.
“Attackers don’t need to break everything at once — just knowing the timing of critical tasks can let them crash a system,” Hasan said. “These leaks pose real risks to both safety and reliability.”
Hasan’s project explores hidden “covert channels” in real-time schedulers and develops strategies to detect, measure, and block them. His team will study the system behaviors that create these timing vulnerabilities and design improved schedulers to prevent information leaks.
“Our goal is to track how data flows through critical systems — does it follow design specs, or is someone tampering with it?” Hasan said. “Answering that helps us build systems that are more resilient and trustworthy.
Our work will improve the security, safety, and resilience of real-time cyber-physical systems. This CAREER project is just the beginning — there are still many challenges and opportunities to tackle in the field,” he added.
With WSU since 2023, Hasan holds a PhD in computer science from the University of Illinois Urbana-Champaign and a master’s degree from the University of Manitoba.
Improving AI models
Yan, meanwhile, will conduct research in improving artificial intelligence (AI) models.
He is developing a new artificial intelligence framework that aims to narrow down searches in very large data sets to more efficiently provide a better picture of reality. The advance would allow AI technology to be more readily used in critical decision making in high-stake applications, such as in engineering design optimization, cybersecurity, health monitoring, or smart agriculture.
“This work handles a blank area in AI machine learning,” said Yan. “In AI machine learning, people usually set up something like the average performance, but the average cannot reflect all the information.”
Yan has been developing a prediction paradigm that builds a set instead of giving the average value for a prediction. His work marks a shift in how he and others have worked to improve AI models. For several years, he had worked to improve average performance, but he eventually realized that the average doesn’t do enough to get at what people really need in information. After that realization, he spent six months studying and moving in a new direction.
“It’s very reasonable to consider the probability because that is really something people can really use and connect to the decision-making process,” he said. “I realized the average or standard way is not enough, and I needed to shift – not completely ignore everything I have learned, but just to add something more.”
In addition to providing a more accurate picture, the work will lead to more efficiency in AI algorithms. The required energy use to power AI is a growing concern.
“We need to be more efficient in resource, data, and computational costs because the training of machine learning AI models requires a lot of time and power as well as human labor,” he said. “Reducing that part of the computational costs would increase efficiency.”
Yan joined WSU in 2020. He holds a PhD from the University of Technology Sydney and a BE in Computer Science from Tianjin University in China.