Messaging systems are essential for efficiently transferring large volumes of data, ensuring rapid response times and high-throughput communication. The state-of-the-art on messaging systems mainly focuses on the performance evaluation of brokered messaging systems, which use an intermediate broker to guarantee reliability and quality of service. However, over the past decade, brokerless messaging systems have emerged, eliminating the single point of failure and trading off reliability guarantees for higher performance. Still, the state-of-the-art on evaluating the performance of brokerless systems is scarce. In this work, we solely focus on brokerless messaging systems. First, we perform a qualitative analysis of several possible candidates, to find the most promising ones. We then design and implement an extensive open-source benchmarking suite to systematically and fairly evaluate the performance of the chosen libraries, namely, ZeroMQ, NanoMsg, and NanoMsg-Next-Generation (NNG). We evaluate these libraries considering different metrics and workload conditions, and provide useful insights into their limitations. Our analysis enables practitioners to select the most suitable library for their requirements.

Remote firmware updates are critical for maintaining the performance and security of Internet-of-Things (IoT) device fleets. Remote updates are especially critical from a fleet management perspective, requiring thousands of devices to be kept up-to-date. When considering a fleet of devices, transferring large update files is inefficient. Therefore many modern systems consider differential updates. In this work, we describe and evaluate a secure differential update implementation for fleets of embedded IoT devices. We demonstrate how differential updates can be performed on a real hardware platform using a popular open-source embedded update framework, SWUpdate. We also show how differential updates can integrate with redundancy concepts such as A/B partitioning and on-device security mechanisms such as secure boot. Our experiments, performed using both real hardware and a device fleet simulator, identify crucial differences between differential and full-image updates: the differential updates can decrease the file size by 49%, the install time by 78%, and require up to 77% more temporary disk memory compared to full-image updates. These key insights are essential for developers and practitioners when selecting the IoT firmware update strategy.

Abstract We present a scalable and precise verifier for recurrent neural networks, calledProverbased on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. UsingProver, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows thatProversuccessfully verifies several challenging recurrent models in computer vision, speech, and motion sensor data classification beyond the reach of prior work.

Permissionless blockchains allow the execution of arbitrary programs (called smart contracts), enabling mutually untrusted entities to interact without relying on trusted third parties. Despite their potential, repeated security concerns have shaken the trust in handling billions of USD by smart contracts. To address this problem, we present Securify, a security analyzer for Ethereum smart contracts that is scalable, fully automated, and able to prove contract behaviors as safe/unsafe with respect to a given property. Securify's analysis consists of two steps. First, it symbolically analyzes the contract's dependency graph to extract precise semantic information from the code. Then, it checks compliance and violation patterns that capture sufficient conditions for proving if a property holds or not. To enable extensibility, all patterns are specified in a designated domain-specific language. Securify is publicly released, it has analyzed >18K contracts submitted by its users, and is regularly used to conduct security audits by experts. We present an extensive evaluation of Securify over real-world Ethereum smart contracts and demonstrate that it can effectively prove the correctness of smart contracts and discover critical violations.

Recent advances in networking hardware have led to a new generation of Remote Memory Access (RMA) networks in which processors from different machines can communicate directly, bypassing the operating system and allowing higher performance. Researchers and practitioners have proposed libraries and programming models for RMA to enable the development of applications running on these networks,