Machine learning algorithm may be the key to timely, inexpensive cyber-defense

Attacks on vulnerable computer networks and cyber-infrastructure--often called zero-day attacks--can quickly overwhelm traditional defenses, resulting in billions of dollars of damage and requiring weeks of manual patching work to shore up the systems after the intrusion. Now, a Penn State-led team of researchers used a machine learning approach, based on a technique known as reinforcement learning, to create an adaptive cyber defense against these attacks. According to Minghui Zhu, associate professor of electrical engineering and computer science and Institute for Computational and Data Sciences co-hire, the team developed this adaptive machine learning-driven method to address current limitations in a method to detect and respond to cyber-attacks, called moving target defense, or MTD. "These adaptive manual target-defense techniques can dynamically and proactively reconfigure deployed defenses that can increase uncertainty and complexity for attackers during vulnerability windows," said Zhu. "However, existing MTD techniques suffer from two limitations. First, manual selection can be very time consuming. Secondly, manually selected configurations might not be the most cost-effective method to handle this."