Reinforcement Learning (RL) offers a promising path toward developing defenses for the next generation of computer networks. The hope is that RL not only helps to automate network defenses, but in addition, RL finds novel solutions to defend networks that adapt to deal with the increasing complexity of networks and threats. Despite the promise, existing work applying RL to cybersecurity trains cyber defenders on rigid and narrow problem definitions with small computer networks.

No abstract available.

Common authentication methods based on passwords, tokens, or fingerprints perform one-time authentication and rely on users to log out from the computer terminal when they leave. Users often do not log out, however, which is a security risk. The most common solution, inactivity timeouts, inevitably fail security (too long a timeout) or usability (too short a timeout) goals. One solution is to authenticate users continuously while they are using the terminal and automatically log them out when they leave. Several solutions are based on user proximity, but these are not sufficient: they only confirm whether the user is nearby but not whether the user is actually using the terminal. Proposed solutions based on behavioral biometric authentication (e.g., keystroke dynamics) may not be reliable, as a recent study suggests. To address this problem we propose Zero-Effort Bilateral Recurring Authentication (ZEBRA). In ZEBRA, a user wears a bracelet (with a built-in accelerometer, gyroscope, and radio) on her dominant wrist. When the user interacts with a computer terminal, the bracelet records the wrist movement, processes it, and sends it to the terminal. The terminal compares the wrist movement with the inputs it receives from the user (via keyboard and mouse), and confirms the continued presence of the user only if they correlate. Because the bracelet is on the same hand that provides inputs to the terminal, the accelerometer and gyroscope data and input events received by the terminal should correlate because their source is the same - the user's hand movement. In our experiments ZEBRA performed continuous authentication with 85% accuracy in verifying the correct user and identified all adversaries within 11s. For a different threshold that trades security for usability, ZEBRA correctly verified 90% of users and identified all adversaries within 50s.