CYBERSECURITY AS MUCH A SOCIAL PROBLEM AS A TECHNICAL ONE
PHOTO: SARAH ATKINSON, MICHIGAN TECH
BY KELLEY CHRISTENSEN
W
hat cybersecurity means to many people i s m a k i n g s u re yo u r passwords are strong and perhaps paying your bank a monthly fee for additional security measures. But in a world where an increasing number of the devices we use every day are becoming members of the Internet of Things—from the obvious ones like Alexa to the less obvious refrigerators and thermostats—cybersecurity attacks and countermeasures are expanding. Cybersecurity is a cross-disciplinary field. Research opportunities abound because of the important role security plays in autopilot vehicles, cyber-physical systems, industrial control systems and smart city infrastructure. Four researchers in Michigan Technological University’s College of Computing focus their research on strengthening cybersecurity, but each approaches the field from different angles, including malware detection, security of cellphones and vehicles (both human-piloted and autonomous), and training the future workforce.
PATTERN PLAY
“Behavior” may seem like an odd word to apply to machines. But the algorithms that power our devices are creatures of habit and repetition—that learn. Which means Bo Chen, assistant professor of computer science, is interested in detecting
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TechCentury
SPRING 2022
Teaching the next generation of cybersecurity professionals is paramount, as an increasing number of devices important to modern life become susceptible to cyberattacks.
unexpected behavior patterns that could be evidence of malware. “A top concern is that when a device is hacked, the malware can steal your information,” Chen said. “If you can detect the malware, you can take action. Data compromised by malware can be recovered after the intrusion is detected.” Chen’s research focuses on how cybersecurity professionals detect malware: by signature or behavior. Signature detection is akin to finding fingerprints left at the scene of a crime—but instead, finding bytes out of place in code. Behavior is a little more complicated. Researchers like Chen are establishing models that can detect excess patterns in code. If the patterns in a data set no longer line up—like crooked corners in a quilt or misplaced colors of thread in a carpet—the model can detect malware at work.
DEEP LEARNING
Teaching a machine to detect complicated patterns requires more than static programming. Neural network learning systems teach artificial intelligence (AI) to recognize situations similar to those they have already encountered. In learning by doing, neural networks create a framework for AIs to make decisions by weighing variables and bias. The more layers of decisions in a given network—a multitude of “if this, then that” questions—the deeper the learning. Xiaoyong Yuan, assistant professor of applied computing, focuses on cybersecurity and deep machine learning, studying how to use AI to solve cybersecurity challenges. Machine learning can help detect network attacks such as distributed denial-of-service (DDOS) strikes.
