Attacking Logic Locked Circuits Using Reinforcement Learning

Logic Locking is an emerging form of hardware obfuscation that is intended to be a solution to many of the trust issues associated with the modern globalized IC supply chain. By inserting extra key-gates into a circuit, the functionality of the circuit can be locked until the correct order of bits or “key” is applied to the key gates. To assess the strength of new logic locking techniques, we propose a new attack that uses deep reinforcement learning. This attack aims to test logic locking as well as evaluate reinforcement learning as a possible attack against logic locking. By using a deep Q-learning neural network, Q-values can be approximated and the model can be trained much faster than using traditional Q-learning. By allowing the model to change a single bit in the key each timestep, simulations of the circuit with the key produced can be run and the outputs can be compared to that of the unlocked version of the circuit, called an oracle. A reward is calculated based on how many bits of the locked circuit are correct and is used to reinforce the learning of the model. During the training phases of the model, the relationship between a highly correct key and how correct the outputs are for some random inputs is not strong, causing the model to struggle to learn quickly

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications