T. T. Nguyen and V. J. Reddi, “
Deep Reinforcement Learning for Cyber Security,”
ArXiv. 2019.
Publisher's VersionAbstractThe scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and large-scale. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This paper presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multi-agent DRL-based game theory simulations for defense strategies against cyber attacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems.
Paper B. Boroujerdian, et al., “
The Role of Compute in Autonomous Aerial Vehicles,”
arXiv preprint arXiv:1906.10513, 2019.
AbstractAutonomous-mobile cyber-physical machines are part of our future. Specifically, unmanned-aerial-vehicles have seen a resurgence in activity with use-cases such as package delivery. These systems face many challenges such as their low-endurance caused by limited onboard-energy, hence, improving the mission-time and energy are of importance. Such improvements traditionally are delivered through better algorithms. But our premise is that more powerful and efficient onboard-compute should also address the problem. This paper investigates how the compute subsystem, in a cyber-physical mobile machine, such as a Micro Aerial Vehicle, impacts mission-time and energy. Specifically, we pose the question as what is the role of computing for cyber-physical mobile robots? We show that compute and motion are tightly intertwined, hence a close examination of cyber and physical processes and their impact on one another is necessary. We show different impact paths through which compute impacts mission-metrics and examine them using analytical models, simulation, and end-to-end benchmarking. To enable similar studies, we open sourced MAVBench, our tool-set consisting of a closed-loop simulator and a benchmark suite. Our investigations show cyber-physical co-design, a methodology where robot's cyber and physical processes/quantities are developed with one another consideration, similar to hardware-software co-design, is necessary for optimal robot design.
PDF B. Boroujerdian, et al., “
The Role of Compute in Autonomous Aerial Vehicles”. 2019.
AbstractAutonomous-mobile cyber-physical machines are part of our future. Specifically, unmanned-aerial-vehicles have seen a resurgence in activity with use-cases such as package delivery. These systems face many challenges such as their low-endurance caused by limited onboard-energy, hence, improving the mission-time and energy are of importance. Such improvements traditionally are delivered through better algorithms. But our premise is that more powerful and efficient onboard-compute should also address the problem. This paper investigates how the compute subsystem, in a cyber-physical mobile machine, such as a Micro Aerial Vehicle, impacts mission-time and energy. Specifically, we pose the question as what is the role of computing for cyber-physical mobile robots? We show that compute and motion are tightly intertwined, hence a close examination of cyber and physical processes and their impact on one another is necessary. We show different impact paths through which compute impacts mission-metrics and examine them using analytical models, simulation, and end-to-end benchmarking. To enable similar studies, we open sourced MAVBench, our tool-set consisting of a closed-loop simulator and a benchmark suite. Our investigations show cyber-physical co-design, a methodology where robot's cyber and physical processes/quantities are developed with one another consideration, similar to hardware-software co-design, is necessary for optimal robot design.
S. Krishnan, B. Boroujerdian, A. Faust, and V. J. Reddi, “
Toward Exploring End-to-End Learning Algorithms for Autonomous Aerial Machines,”
Workshop Algorithms And Architectures For Learning In-The-Loop Systems In Autonomous Flight with International Conference on Robotics and Automation (ICRA). 2019.
AbstractWe develop AirLearning, a tool suite for endto-end closed-loop UAV analysis, equipped with a customized yet randomized environment generator in order to expose the UAV with a diverse set of challenges. We take Deep Q networks (DQN) as an example deep reinforcement learning algorithm and use curriculum learning to train a point to point obstacle avoidance policy. While we determine the best policy based on the success rate, we evaluate it under strict resource constraints on an embedded platform such as RasPi 3. Using hardware in the loop methodology, we quantify the policy’s performance with quality of flight metrics such as energy consumed, endurance and the average length of the trajectory. We find that the trajectories produced on the embedded platform are very different from those predicted on the desktop, resulting in up to 26.43% longer trajectories.
Quality of flight metrics with hardware in the loop characterizes those differences in simulation, thereby exposing how the choice of onboard compute contributes to shortening or widening of ‘Sim2Real’ gap.
Paper