KISS methodologies for network management and anomaly detection

Trabajo presentado al 26th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2018. Croacia, 13-15 de septiembre de 2018Current networks are increasingly growing in size, complexity and the amount of monitoring data that they produce, which requires complex data analysis pipelines to handle data collection, centralization and analysis tasks. Literature approaches, include the use of custom agents to harvest information and large data centralization systems based on clusters to achieve horizontal scalability, which are expensive and difficult to deploy in real scenarios. In this paper we propose and evaluate a series of methodologies, deployed in real industrial production environments, for network management, from the architecture design to the visualization system as well as for the anomaly detection methodologies, that intend to squeeze the vertical resources and overcome the difficulties of data collection and centralization.The authors would like to thank MINECO, received through grant TEC2015-69417 (TRAFICA)

Pan Asia Networking (PAN) prospectus review 2006-2011

A sample of research findings is provided from Pan Asia Networking (PAN) research projects. These examples give a sense of the type of evidence that has been gathered by partners under PAN’s three main themes (Policies, Technologies, and Effects). The themes are summarized in a table in terms of objectives, research activities, and expected outcomes. PAN-supported research also aims to be relevant and rigorous enough to influence policy debates and attract media attention. PAN encouraged new researchers in ICT4D programming such as the PANdora distance learning research network, and LIRNEasia (communications policy). This review encompasses a wide array of outputs and outcomes

InternalBlue - Bluetooth Binary Patching and Experimentation Framework

Bluetooth is one of the most established technologies for short range digital wireless data transmission. With the advent of wearables and the Internet of Things (IoT), Bluetooth has again gained importance, which makes security research and protocol optimizations imperative. Surprisingly, there is a lack of openly available tools and experimental platforms to scrutinize Bluetooth. In particular, system aspects and close to hardware protocol layers are mostly uncovered. We reverse engineer multiple Broadcom Bluetooth chipsets that are widespread in off-the-shelf devices. Thus, we offer deep insights into the internal architecture of a popular commercial family of Bluetooth controllers used in smartphones, wearables, and IoT platforms. Reverse engineered functions can then be altered with our InternalBlue Python framework---outperforming evaluation kits, which are limited to documented and vendor-defined functions. The modified Bluetooth stack remains fully functional and high-performance. Hence, it provides a portable low-cost research platform. InternalBlue is a versatile framework and we demonstrate its abilities by implementing tests and demos for known Bluetooth vulnerabilities. Moreover, we discover a novel critical security issue affecting a large selection of Broadcom chipsets that allows executing code within the attacked Bluetooth firmware. We further show how to use our framework to fix bugs in chipsets out of vendor support and how to add new security features to Bluetooth firmware

The Federal Big Data Research and Development Strategic Plan

This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development

The Federal Big Data Research and Development Strategic Plan

This document was developed through the contributions of the NITRD Big Data SSG members and staff. A special thanks and appreciation to the core team of editors, writers, and reviewers: Lida Beninson (NSF), Quincy Brown (NSF), Elizabeth Burrows (NSF), Dana Hunter (NSF), Craig Jolley (USAID), Meredith Lee (DHS), Nishal Mohan (NSF), Chloe Poston (NSF), Renata Rawlings-Goss (NSF), Carly Robinson (DOE Science), Alejandro Suarez (NSF), Martin Wiener (NSF), and Fen Zhao (NSF). A national Big Data1 innovation ecosystem is essential to enabling knowledge discovery from and confident action informed by the vast resource of new and diverse datasets that are rapidly becoming available in nearly every aspect of life. Big Data has the potential to radically improve the lives of all Americans. It is now possible to combine disparate, dynamic, and distributed datasets and enable everything from predicting the future behavior of complex systems to precise medical treatments, smart energy usage, and focused educational curricula. Government agency research and public-private partnerships, together with the education and training of future data scientists, will enable applications that directly benefit society and the economy of the Nation. To derive the greatest benefits from the many, rich sources of Big Data, the Administration announced a “Big Data Research and Development Initiative” on March 29, 2012.2 Dr. John P. Holdren, Assistant to the President for Science and Technology and Director of the Office of Science and Technology Policy, stated that the initiative “promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security.” The Federal Big Data Research and Development Strategic Plan (Plan) builds upon the promise and excitement of the myriad applications enabled by Big Data with the objective of guiding Federal agencies as they develop and expand their individual mission-driven programs and investments related to Big Data. The Plan is based on inputs from a series of Federal agency and public activities, and a shared vision: We envision a Big Data innovation ecosystem in which the ability to analyze, extract information from, and make decisions and discoveries based upon large, diverse, and real-time datasets enables new capabilities for Federal agencies and the Nation at large; accelerates the process of scientific discovery and innovation; leads to new fields of research and new areas of inquiry that would otherwise be impossible; educates the next generation of 21st century scientists and engineers; and promotes new economic growth. The Plan is built around seven strategies that represent key areas of importance for Big Data research and development (R&D). Priorities listed within each strategy highlight the intended outcomes that can be addressed by the missions and research funding of NITRD agencies. These include advancing human understanding in all branches of science, medicine, and security; ensuring the Nation’s continued leadership in research and development; and enhancing the Nation’s ability to address pressing societal and environmental issues facing the Nation and the world through research and development

Consolidating Research and Education Networking in Africa (CORENA) : monitoring and evaluation strategy

This report outlines the Monitoring and Evaluation (M&E) strategy for Consolidating Research and Education Networking in Africa (CORENA), a program of the UbuntuNet Alliance for Research and Education Networking. The program objective is to secure high bandwidth connections at affordable rates to help connect African National Research and Education Networks (NRENs) to each other, to other NRENs worldwide, and to the Internet in general. Effective M&E ensures that project strategies are properly aligned to changing contexts, and that progress towards the program goal is tracked, in order to identify areas requiring attention

PREparedness, REsponse and SySTemic transformation (PRE-RE-SyST): a model for disability-inclusive pandemic responses and systemic disparities reduction derived from a scoping review and thematic analysis

Background People with disabilities (PwD) have been facing multiple health, social, and economic disparities during the COVID-19 pandemic, stemming from structural disparities experienced for long time. This paper aims to present the PREparedness, RESponse and SySTemic transformation (PRE-RE-SyST): a model for a disability-inclusive pandemic responses and systematic disparities reduction. Methods Scoping review with a thematic analysis was conducted on the literature published up to mid-September 2020, equating to the initial stages of the COVID-19 pandemic. Seven scientific databases and three preprint databases were searched to identify empirical or perspective papers addressing health and socio-economic disparities experienced by PwD as well as reporting actions to address them. Snowballing searches and experts' consultation were also conducted. Two independent reviewers made eligibility decisions and performed data extractions on any action or recommended action to address disparities. A thematic analysis was then used for the model construction, informed by a systems-thinking approach (i.e., the Iceberg Model). Results From 1027 unique references, 84 were included in the final analysis. The PRE-RE-SyST model articulates a four-level strategic action to: 1) Respond to prevent or reduce disability disparities during a pandemic crisis; 2) Prepare ahead for pandemic and other crises responses; 3) Design systems and policies for a structural disability-inclusiveness; and 4) Transform society's cultural assumptions about disability. 'Simple rules' and literature-based examples on how these strategies can be deployed are provided. Conclusion The PRE-RE-SyST model articulates main strategies, 'simple rules' and possible means whereby public health authorities, policy-makers, and other stakeholders can address disability disparities in pandemic crises, and beyond. Beyond immediate pandemic responses, disability-inclusiveness is needed to develop everyday equity-oriented policies and practices that can transform societies towards greater resiliency, as a whole, to pandemic and other health and social emergencies.This work was supported by the DBT/Wellcome Trust India Alliance Fellowship [grant IA/CPHE/16/1/502650], awarded to Dr. Sureshkumar Kamalakanna