Biblio

Analyzing the DNS traffic of Internet hosts has been a successful technique to counter cyberattacks and identify connections to malicious domains. However, recent stealthy attacks hide malicious activities within seemingly legitimate connections to popular web services made by benign programs. Traditional DNS monitoring and signature-based detection techniques are ineffective against such attacks. To tackle this challenge, we present a new program-level approach that can effectively detect such stealthy attacks. Our method builds a fine-grained Program-DNS profile for each benign program that characterizes what should be the “expected” DNS behavior. We find that malware-injected processes have DNS activities which significantly deviate from the Program-DNS profile of the benign program. We then develop six novel features based on the Program-DNS profile, and evaluate the features on a dataset of over 130 million DNS requests collected from a real-world enterprise and 8 million requests from malware-samples executed in a sandbox environment. We compare our detection results with that of previously-proposed features and demonstrate that our new features successfully detect 190 malware-injected processes which fail to be detected by previously-proposed features. Overall, our study demonstrates that fine-grained Program-DNS profiles can provide meaningful and effective features in building detectors for attack campaigns that bypass existing detection systems.

Augmented reality (AR) technologies are rapidly being adopted across multiple sectors, but little work has been done to ensure the security of such systems against potentially harmful or distracting visual output produced by malicious or bug-ridden applications. Past research has proposed to incorporate manually specified policies into AR devices to constrain their visual output. However, these policies can be cumbersome to specify and implement, and may not generalize well to complex and unpredictable environmental conditions. We propose a method for generating adaptive policies to secure visual output in AR systems using deep reinforcement learning. This approach utilizes a local fog computing node, which runs training simulations to automatically learn an appropriate policy for filtering potentially malicious or distracting content produced by an application. Through empirical evaluations, we show that these policies are able to intelligently displace AR content to reduce obstruction of real-world objects, while maintaining a favorable user experience.