1,622 research outputs found
BaseSAFE: Baseband SAnitized Fuzzing through Emulation
Rogue base stations are an effective attack vector. Cellular basebands
represent a critical part of the smartphone's security: they parse large
amounts of data even before authentication. They can, therefore, grant an
attacker a very stealthy way to gather information about calls placed and even
to escalate to the main operating system, over-the-air. In this paper, we
discuss a novel cellular fuzzing framework that aims to help security
researchers find critical bugs in cellular basebands and similar embedded
systems. BaseSAFE allows partial rehosting of cellular basebands for fast
instrumented fuzzing off-device, even for closed-source firmware blobs.
BaseSAFE's sanitizing drop-in allocator, enables spotting heap-based
buffer-overflows quickly. Using our proof-of-concept harness, we fuzzed various
parsers of the Nucleus RTOS-based MediaTek cellular baseband that are
accessible from rogue base stations. The emulator instrumentation is highly
optimized, reaching hundreds of executions per second on each core for our
complex test case, around 15k test-cases per second in total. Furthermore, we
discuss attack vectors for baseband modems. To the best of our knowledge, this
is the first use of emulation-based fuzzing for security testing of commercial
cellular basebands. Most of the tooling and approaches of BaseSAFE are also
applicable for other low-level kernels and firmware. Using BaseSAFE, we were
able to find memory corruptions including heap out-of-bounds writes using our
proof-of-concept fuzzing harness in the MediaTek cellular baseband. BaseSAFE,
the harness, and a large collection of LTE signaling message test cases will be
released open-source upon publication of this paper
A Survey on Data Plane Programming with P4: Fundamentals, Advances, and Applied Research
With traditional networking, users can configure control plane protocols to
match the specific network configuration, but without the ability to
fundamentally change the underlying algorithms. With SDN, the users may provide
their own control plane, that can control network devices through their data
plane APIs. Programmable data planes allow users to define their own data plane
algorithms for network devices including appropriate data plane APIs which may
be leveraged by user-defined SDN control. Thus, programmable data planes and
SDN offer great flexibility for network customization, be it for specialized,
commercial appliances, e.g., in 5G or data center networks, or for rapid
prototyping in industrial and academic research. Programming
protocol-independent packet processors (P4) has emerged as the currently most
widespread abstraction, programming language, and concept for data plane
programming. It is developed and standardized by an open community and it is
supported by various software and hardware platforms. In this paper, we survey
the literature from 2015 to 2020 on data plane programming with P4. Our survey
covers 497 references of which 367 are scientific publications. We organize our
work into two parts. In the first part, we give an overview of data plane
programming models, the programming language, architectures, compilers,
targets, and data plane APIs. We also consider research efforts to advance P4
technology. In the second part, we analyze a large body of literature
considering P4-based applied research. We categorize 241 research papers into
different application domains, summarize their contributions, and extract
prototypes, target platforms, and source code availability.Comment: Submitted to IEEE Communications Surveys and Tutorials (COMS) on
2021-01-2
Configurable data center switch architectures
In this thesis, we explore alternative architectures for implementing con_gurable Data Center Switches along with the advantages that can be provided by such switches. Our first contribution centers around determining switch architectures that can be implemented on Field Programmable Gate Array (FPGA) to provide configurable switching protocols. In the process, we identify a gap in the availability of frameworks to realistically evaluate the performance of switch architectures in data centers and contribute a simulation framework that relies on realistic data center traffic patterns. Our framework is then used to evaluate the performance of currently existing as well as newly proposed FPGA-amenable switch designs. Through collaborative work with Meng and Papaphilippou, we establish that only small-medium range switches can be implemented on today's FPGAs. Our second contribution is a novel switch architecture that integrates a custom in-network hardware accelerator with a generic switch to accelerate Deep Neural Network training applications in data centers. Our proposed accelerator architecture is prototyped on an FPGA, and a scalability study is conducted to demonstrate the trade-offs of an FPGA implementation when compared to an ASIC implementation. In addition to the hardware prototype, we contribute a light weight load-balancing and congestion control protocol that leverages the unique communication patterns of ML data-parallel jobs to enable fair sharing of network resources across different jobs. Our large-scale simulations demonstrate the ability of our novel switch architecture and light weight congestion control protocol to both accelerate the training time of machine learning jobs by up to 1.34x and benefit other latency-sensitive applications by reducing their 99%-tile completion time by up to 4.5x. As for our final contribution, we identify the main requirements of in-network applications and propose a Network-on-Chip (NoC)-based architecture for supporting a heterogeneous set of applications. Observing the lack of tools to support such research, we provide a tool that can be used to evaluate NoC-based switch architectures.Open Acces
Doctor of Philosophy
dissertationOver 40 years ago, the first computer simulation of a protein was reported: the atomic motions of a 58 amino acid protein were simulated for few picoseconds. With today's supercomputers, simulations of large biomolecular systems with hundreds of thousands of atoms can reach biologically significant timescales. Through dynamics information biomolecular simulations can provide new insights into molecular structure and function to support the development of new drugs or therapies. While the recent advances in high-performance computing hardware and computational methods have enabled scientists to run longer simulations, they also created new challenges for data management. Investigators need to use local and national resources to run these simulations and store their output, which can reach terabytes of data on disk. Because of the wide variety of computational methods and software packages available to the community, no standard data representation has been established to describe the computational protocol and the output of these simulations, preventing data sharing and collaboration. Data exchange is also limited due to the lack of repositories and tools to summarize, index, and search biomolecular simulation datasets. In this dissertation a common data model for biomolecular simulations is proposed to guide the design of future databases and APIs. The data model was then extended to a controlled vocabulary that can be used in the context of the semantic web. Two different approaches to data management are also proposed. The iBIOMES repository offers a distributed environment where input and output files are indexed via common data elements. The repository includes a dynamic web interface to summarize, visualize, search, and download published data. A simpler tool, iBIOMES Lite, was developed to generate summaries of datasets hosted at remote sites where user privileges and/or IT resources might be limited. These two informatics-based approaches to data management offer new means for the community to keep track of distributed and heterogeneous biomolecular simulation data and create collaborative networks
Software Perfomance Assessment at Architectural Level: A Methodology and its Application
Las arquitecturas software son una valiosa herramienta para la evaluación de las propiedades cualitativas y cuantitativas de los sistemas en sus primeras fases de desarrollo. Conseguir el diseño adecuado es crÃtico para asegurar la bondad de dichas propiedades. Tomar decisiones tempranas equivocadas puede implicar considerables y costosos cambios en un futuro. Dichas decisiones afectarÃan a muchas propiedades del sistema, tales como su rendimiento, seguridad, fiabilidad o facilidad de mantenimiento. Desde el punto de vista del rendimiento software, la ingenierÃa del rendimiento del software (SPE) es una disciplina de investigación madura y comúnmente aceptada que propone una evaluación basada en modelos en las primeras fases del ciclo de vida de desarrollo software. Un problema en este campo de investigación es que las metodologÃas hasta ahora propuestas no ofrecen una interpretación de los resultados obtenidos durante el análisis del rendimiento, ni utilizan dichos resultados para proponer alternativas para la mejora de la propia arquitectura software. Hasta la fecha, esta interpretación y mejora requiere de la experiencia y pericia de los ingenieros software, en especial de expertos en ingenierÃa de prestaciones. Además, a pesar del gran número de propuestas para evaluar el rendimiento de sistemas software, muy pocos de estos estudios teóricos son posteriormente aplicados a sistemas software reales. El objetivo de esta tesis es presentar una metodologÃa para el asesoramiento de decisiones arquitecturales para la mejora, desde el punto de vista de las prestaciones, de las sistemas software. La metodologÃa hace uso del Lenguaje Unificado de Modelado (UML) para representar las arquitecturas software y de métodos formales, concretamente redes de Petri, como modelo de prestaciones. El asesoramiento, basado en patrones y antipatrones, intenta detectar los principales problemas que afectan a las prestaciones del sistema y propone posibles mejoras para mejoras dichas prestaciones. Como primer paso, estudiamos y analizamos los resultados del rendimiento de diferentes estilos arquitectónicos. A continuación, sistematizamos los conocimientos previamente obtenidos para proponer una metodologÃa y comprobamos su aplicabilidad asesorando un caso de estudio real, una arquitectura de interoperabilidad para adaptar interfaces a personas con discapacidad conforme a sus capacidades y preferencias. Finalmente, se presenta una herramienta para la evaluación del rendimiento como un producto derivado del propio ciclo de vida software
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