SNS programming environment user's guide

The computing environment is briefly described for the Supercomputing Network Subsystem (SNS) of the Central Scientific Computing Complex of NASA Langley. The major SNS computers are a CRAY-2, a CRAY Y-MP, a CONVEX C-210, and a CONVEX C-220. The software is described that is common to all of these computers, including: the UNIX operating system, computer graphics, networking utilities, mass storage, and mathematical libraries. Also described is file management, validation, SNS configuration, documentation, and customer services

Development of a virtual personal video recorder based on a set top box

Estágio realizado na Fraunhofer Portugal ResearchTese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Telecomunicações). Faculdade de Engenharia. Universidade do Porto. 200

CIRT News - September 2017

This issue contains the following articles: Faculty Spotlight: Streamlining Course and Program Development with Course Templates Director\u27s Message: CIRT Annual Report 2017 Upcoming Events Digital Thinking: Announcing Faculty Domains Best Practices Online: Communication in Canvas Canvas News CIRT\u27s Teaching Online Faculty Development Model Refined New in CIRT: Welcome Nick and Courtne

{SoK}: {An} Analysis of Protocol Design: Avoiding Traps for Implementation and Deployment

Today's Internet utilizes a multitude of different protocols. While some of these protocols were first implemented and used and later documented, other were first specified and then implemented. Regardless of how protocols came to be, their definitions can contain traps that lead to insecure implementations or deployments. A classical example is insufficiently strict authentication requirements in a protocol specification. The resulting Misconfigurations, i.e., not enabling strong authentication, are common root causes for Internet security incidents. Indeed, Internet protocols have been commonly designed without security in mind which leads to a multitude of misconfiguration traps. While this is slowly changing, to strict security considerations can have a similarly bad effect. Due to complex implementations and insufficient documentation, security features may remain unused, leaving deployments vulnerable. In this paper we provide a systematization of the security traps found in common Internet protocols. By separating protocols in four classes we identify major factors that lead to common security traps. These insights together with observations about end-user centric usability and security by default are then used to derive recommendations for improving existing and designing new protocols---without such security sensitive traps for operators, implementors and users

{SoK}: {An} Analysis of Protocol Design: Avoiding Traps for Implementation and Deployment

Today's Internet utilizes a multitude of different protocols. While some of these protocols were first implemented and used and later documented, other were first specified and then implemented. Regardless of how protocols came to be, their definitions can contain traps that lead to insecure implementations or deployments. A classical example is insufficiently strict authentication requirements in a protocol specification. The resulting Misconfigurations, i.e., not enabling strong authentication, are common root causes for Internet security incidents. Indeed, Internet protocols have been commonly designed without security in mind which leads to a multitude of misconfiguration traps. While this is slowly changing, to strict security considerations can have a similarly bad effect. Due to complex implementations and insufficient documentation, security features may remain unused, leaving deployments vulnerable. In this paper we provide a systematization of the security traps found in common Internet protocols. By separating protocols in four classes we identify major factors that lead to common security traps. These insights together with observations about end-user centric usability and security by default are then used to derive recommendations for improving existing and designing new protocols---without such security sensitive traps for operators, implementors and users

A principled approach to measuring the IoT ecosystem

Internet of Things (IoT) devices combine network connectivity, cheap hardware, and actuation to provide new ways to interface with the world. In spite of this growth, little work has been done to measure the network properties of IoT devices. Such measurements can help to inform systems designers and security researchers of IoT networking behavior in practice to guide future research. Unfortunately, properly measuring the IoT ecosystem is not trivial. Devices may have different capabilities and behaviors, which require both active measurements and passive observation to quantify. Furthermore, the IoT devices that are connected to the public Internet may vary from those connected inside home networks, requiring both an external and internal vantage point to draw measurements from. In this thesis, we demonstrate how IoT measurements drawn from a single vantage point or mesaurement technique lead to a biased view of the network services in the IoT ecosystem. To do this, we conduct several real-world IoT measurements, drawn from both inside and outside home networks using active and passive monitoring. First, we leverage active scanning and passive observation in understanding the Mirai botnet---chiefly, we report on the devices it infected, the command and control infrastructure behind the botnet, and how the malware evolved over time. We then conduct active measurements from inside 16M home networks spanning 83M devices from 11~geographic regions to survey the IoT devices installed around the world. We demonstrate how these measurements can uncover the device types that are most at risk and the vendors who manufacture the weakest devices. We compare our measurements with passive external observation by detecting compromised scanning behavior from smart homes. We find that while passive external observation can drive insight about compromised networks, it offers little by way of concrete device attribution. We next compare our results from active external scanning with active internal scanning and show how relying solely on external scanning for IoT measurements under-reports security important IoT protocols, potentially skewing the services investigated by the security community. Finally, we conduct passive measurements of 275~smart home networks to investigate IoT behavior. We find that IoT device behavior varies by type and devices regularly communicate over a myriad of bespoke ports, in many cases to speak standard protocols (e.g., HTTP). Finally, we observe that devices regularly offer active services (e.g., Telnet, rpcbind) that are rarely, if ever, used in actual communication, demonstrating the need for both active and passive measurements to properly compare device capabilities and behaviors. Our results highlight the need for a confluence of measurement perspectives to comprehensively understand IoT ecosystem. We conclude with recommendations for future measurements of IoT devices as well as directions for the systems and security community informed by our work

CPA\u27s guide to the Internet

https://egrove.olemiss.edu/aicpa_guides/1967/thumbnail.jp