KLEIN: A New Family of Lightweight Block Ciphers

Resource-efficient cryptographic primitives become fundamental for realizing both security and efficiency in embedded systems like RFID tags and sensor nodes. Among those primitives, lightweight block cipher plays a major role as a building block for security protocols. In this paper, we describe a new family of lightweight block ciphers named KLEIN, which is designed for resource-constrained devices such as wireless sensors and RFID tags. Compared to the related proposals, KLEIN has advantage in the software performance on legacy sensor platforms, while in the same time its hardware implementation can also be compact

Specification: The Biggest Bottleneck in Formal Methods and Autonomy

Advancement of AI-enhanced control in autonomous systems stands on the shoulders of formal methods, which make possible the rigorous safety analysis autonomous systems require. An aircraft cannot operate autonomously unless it has design-time reasoning to ensure correct operation of the autopilot and runtime reasoning to ensure system health management, or the ability to detect and respond to off-nominal situations. Formal methods are highly dependent on the specifications over which they reason; there is no escaping the “garbage in, garbage out” reality. Specification is difficult, unglamorous, and arguably the biggest bottleneck facing verification and validation of aerospace, and other, autonomous systems. This VSTTE invited talk and paper examines the outlook for the practice of formal specification, and highlights the on-going challenges of specification, from design-time to runtime system health management. We exemplify these challenges for specifications in Linear Temporal Logic (LTL) though the focus is not limited to that specification language. We pose challenge questions for specification that will shape both the future of formal methods, and our ability to more automatically verify and validate autonomous systems of greater variety and scale. We call for further research into LTL Genesis

LNCS

A controller is a device that interacts with a plant. At each time point,it reads the plant’s state and issues commands with the goal that the plant oper-ates optimally. Constructing optimal controllers is a fundamental and challengingproblem. Machine learning techniques have recently been successfully applied totrain controllers, yet they have limitations. Learned controllers are monolithic andhard to reason about. In particular, it is difficult to add features without retraining,to guarantee any level of performance, and to achieve acceptable performancewhen encountering untrained scenarios. These limitations can be addressed bydeploying quantitative run-timeshieldsthat serve as a proxy for the controller.At each time point, the shield reads the command issued by the controller andmay choose to alter it before passing it on to the plant. We show how optimalshields that interfere as little as possible while guaranteeing a desired level ofcontroller performance, can be generated systematically and automatically usingreactive synthesis. First, we abstract the plant by building a stochastic model.Second, we consider the learned controller to be a black box. Third, we mea-surecontroller performanceandshield interferenceby two quantitative run-timemeasures that are formally defined using weighted automata. Then, the problemof constructing a shield that guarantees maximal performance with minimal inter-ference is the problem of finding an optimal strategy in a stochastic2-player game“controller versus shield” played on the abstract state space of the plant with aquantitative objective obtained from combining the performance and interferencemeasures. We illustrate the effectiveness of our approach by automatically con-structing lightweight shields for learned traffic-light controllers in various roadnetworks. The shields we generate avoid liveness bugs, improve controller per-formance in untrained and changing traffic situations, and add features to learnedcontrollers, such as giving priority to emergency vehicles

Safe Reinforcement Learning Using Probabilistic Shields (Invited Paper)

This paper concerns the efficient construction of a safety shield for reinforcement learning. We specifically target scenarios that incorporate uncertainty and use Markov decision processes (MDPs) as the underlying model to capture such problems. Reinforcement learning (RL) is a machine learning technique that can determine near-optimal policies in MDPs that may be unknown before exploring the model. However, during exploration, RL is prone to induce behavior that is undesirable or not allowed in safety- or mission-critical contexts. We introduce the concept of a probabilistic shield that enables RL decision-making to adhere to safety constraints with high probability. We employ formal verification to efficiently compute the probabilities of critical decisions within a safety-relevant fragment of the MDP. These results help to realize a shield that, when applied to an RL algorithm, restricts the agent from taking unsafe actions, while optimizing the performance objective. We discuss tradeoffs between sufficient progress in the exploration of the environment and ensuring safety. In our experiments, we demonstrate on the arcade game PAC-MAN and on a case study involving service robots that the learning efficiency increases as the learning needs orders of magnitude fewer episodes

New AES software speed records

Abstract. This paper presents new speed records for AES software, taking advantage of (1) architecture-dependent reduction of instructions used to compute AES and (2) microarchitecture-dependent reduction of cycles used for those instructions. A wide variety of common CPU architectures—amd64, ppc32, sparcv9, and x86—are discussed in detail, along with several specific microarchitectures

Acacia+, a Tool for LTL Synthesis

info:eu-repo/semantics/publishe

Assembly or Optimized C for Lightweight Cryptography on RISC-V?

Item does not contain fulltextCANS 202

All the AES You Need on Cortex-M3 and M4

Contains fulltext : 178459.pdf (preprint version ) (Open Access) Contains fulltext : 178459pub.pdf (publisher's version ) (Closed access)nul

Shield Synthesis for Reinforcement Learning

Contains fulltext : 225791.pdf (publisher's version ) (Closed access)ISoL