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Explore Shopify's approach to securing Kubernetes deployments through container signatures and admission controllers, ensuring a robust software supply chain.
Challenges traditional on-call practices in tech, analyzing their impact on workers and proposing alternatives for a future without on-call shifts. Explores industry history and calls for change.
Análisis de vulnerabilidades en la infraestructura de clave pública y demostración de ataques BGP para obtener certificados falsos, con propuestas de contramedidas para mejorar la seguridad.
Unveiling Foreshadow: A microarchitectural attack exploiting speculative execution to extract secrets from Intel SGX enclaves, compromising trusted execution environments and remote attestation.
Explore automatic vulnerability detection and exploit generation for Ethereum smart contracts using teEther, a tool that analyzes binary bytecode to identify and exploit security weaknesses.
Automated framework for analyzing privacy policies using deep learning, enabling scalable queries and applications like privacy icon assignment and question-answering systems.
Explores potential attacks on power grids using IoT botnets of high-wattage devices, demonstrating vulnerabilities and impacts through simulations on real-world models. Highlights interdependency between IoT and power grid security.
Explores DNS interception by on-path devices, revealing widespread occurrence and potential security risks. Presents novel detection methods and analyzes data from global IP addresses to uncover concerning practices.
Presentation on extending applied pi-calculus to model distance bounding protocols, analyzing security properties, and introducing a new property for protecting uncompromised devices like contactless payment cards.
Explores the evolution of Site Reliability Engineering practices, offering insights into various stages of implementation across key areas like incident response, postmortems, and monitoring to help teams assess and advance their SRE journey.
Innovative distributed range lock management system using one-sided RDMA, enhancing concurrent access to shared storage with improved throughput and reduced latency compared to CPU-based approaches.
Innovative caching system for distributed deep learning training, improving performance by prioritizing important data samples and enhancing cache hit ratios in I/O-intensive workloads.
Innovative framework for detecting and analyzing fail-slow failures in cloud storage systems, reducing tail latency and providing insights into root causes through large-scale dataset analysis.
Innovative file system for exascale supercomputers, bridging local and shared burst buffers. Enhances scalability, performance, and data sharing while addressing metadata management and I/O challenges.
Scalable RDMA-oriented learned key-value store for disaggregated memory systems, improving performance on dynamic workloads and addressing challenges in data storage and retrieval.
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