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META expands Facebook Protect Program

META expands Facebook Protect Program

Meta, the new name for Facebook, has expanded it’s Facebook Protect security program to journalists, government officials, human rights defenders, and activist who are often targets online. The program offers enhances security like two factor authentication and alerts for potential hacking threats. Almost 1 million accounts have turned on this protection since it came online in September 2021. It also gives members tips for improving security. #ScienceofSecurity https://thehackernews.com/2021/12/meta-expands-facebook-protect-program.html
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CPS-VO

The CPS-VO is an active resource that provides access to tools and methods emerging from the CPS research community as well as a collaboration platform and repository of information. The integrated suite of models, integration platforms, and intellectual frameworks are developed and contributed by the research community lead to a new era of low-cost, distributed and open design infrastructure.

CAREER: Autonomous Underwater Power Distribution System for Continuous Operation
Lead PI:
Nina Mahmoudian
Abstract
This CAREER project responds to an urgent need to develop mobile power distribution systems that lower deployment and operating costs while simultaneously increasing network efficiency and response in dynamic and often dangerous physical conditions. The significant need for an efficient and effective mobile power distribution system became evident during search and rescue/recovery missions following the Japan tsunami and the disappearance of the Malaysia MH370 airplane. The technology outcomes from this project will apply to a broad range of environments (in space, air, water or on ground) where the success of long-term robotic network missions is measured by the ability of the robots to operate, for an extended period of time, in highly dynamic and potentially hazardous environments. These advanced features will provide the following advantages: efficiency, efficacy, guaranteed persistence, enhanced performance, and increased success in search/rescue/recovery/discovery missions. Specifically, this project addresses the following technology problems as it translates from research discovery toward commercial application: inflated energy use currently required when the autonomous vehicles break from mission to return to recharging station; lack of multi-robot coordination needed to take into account both fundamental hardware and network science challenges necessary to respond to energy needs and dynamic environment conditions. By addressing these gaps in technology, this work establishes the theoretical, computational, and experimental foundation for mobile power delivery and onsite recharging capability. Moreover, the new technology developed in this project is universally adaptable for disparate autonomous vehicles especially autonomous underwater vehicles (AUVs). In more technical terms, this project creates network optimization and formation strategies that will enable a power distribution system to reconfigure itself depending on the number of operational autonomous vehicles and recharging specifications to meet overall mission specifications, the energy consumption needs of the network, situational conditions, and environmental variables. Such a system will play a vital role in real-time controlled applications across multiple disciplines such as sensor networks, robotics, and transportation systems where limited power resources and unknown environmental dynamics pose major constraints. In addition to addressing technology gaps, undergraduate and graduate students will be involved in this research and will receive interdisciplinary education/ innovation/ technology translation/ outreach experiences through: developing efficient network energy routing, path planning and coordination strategies; designing and creating experimental test-beds and educational platforms; and engaging K-12th grade students in Science, Technology, Engineering and Math including those from underrepresented groups. This project engages Michigan Tech's Great Lake Research Center (GLRC) and Center for Agile Interconnected Microgrids (AIM) to develop experimental test-beds and conduct tests that validate the resulting methods and algorithms, and ultimately, facilitate the technology translation effort from research discovery toward commercial reality.
Performance Period: 01/01/2019 - 04/30/2020
Institution: Purdue University
Sponsor: National Science Foundation
Award Number: 1921060
CPS: Synergy: Collaborative Research: Foundations of Secure Cyber-Physical Systems of Systems
Lead PI:
Stephen Checkoway
Abstract
Factories, chemical plants, automobiles, and aircraft have come to be described today as cyber-physical systems of systems--distinct systems connected to form a larger and more complex system. For many such systems, correct operation is critical to safety, making their security of paramount importance. Unfortunately, because of their heterogeneous nature and special purpose, it is very difficult to determine whether a malicious attacker can make them behave in a manner that causes harm. This type of security analysis is an essential step in building and certifying secure systems. Unfortunately, today's state of the art security analysis tools are tailored to the analysis of server, desktop, and mobile software. We currently lack the tools for analyzing the security of cyber physical systems of systems. The proposed work will develop new techniques for testing and analyzing security properties of such systems. These techniques will be used to build a new generation of tools that can handle the complexity of modern cyber-physical systems and thus make these critical systems more secure.The technical approach taken by the investigators is to applying proven dynamic analysis techniques, including dynamic information flow tracking and symbolic execution, to this problem. Existing tools, while powerful, are monolithic, designed to apply a single technique to a single system. Scaling them to multiple heterogeneous systems is the main contribution of the proposed work. To do so, the investigators will develop a common platform for cross-system dynamic analysis supporting arbitrary combinations of component execution modes (physical, simulated, and emulated), requiring new coordination mechanisms. Second, building on the platform above, they will implement cross-system dynamic information flow tracking, allowing dynamic information flow tracking across simulated, emulated, and potentially physical components. Third, they will extend existing symbolic/concrete execution techniques to execution across multiple heterogeneous systems. Fourth, they will introduce new ways of handling special-purpose hardware, a problem faced by dynamic analysis tools in general.
Performance Period: 08/16/2018 - 09/30/2019
Institution: Oberlin College
Sponsor: National Science Foundation
Award Number: 1901728
CPS: Synergy: Real-Time Cyber-Human-Vehicle Systems for Driving Safety Enhancement
Lead PI:
Junmin Wang
Abstract
Modern ground vehicles are complex cyber-physical systems (CPS) in which many functions are achieved by collaborative interactions between mechanical systems and electronic control units. In addition, human drivers also play important roles on the vehicle driving. For such cyber-human-vehicle systems (CHVS), the synergistic collaborations and integrations among human drivers, vehicle active motion control, and onboard real-time computation and communication are critical for enhancing vehicle driving safety. With the recent advances on vehicle onboard computation and communication technologies, this project aims to develop onboard-adaptable and personalizable human driver models, create driver-specific vehicle active motion control systems, design dynamic onboard real-time computation task scheduling methods that can effectively synthesize with the personalized vehicle motion control methods, and integrate the vehicle-to-vehicle communications for driver-specific, inter-vehicle motion control. The research, upon successful completion, can create methodologies for optimally synthesizing onboard computation and communications, individual human driving characteristics, and vehicle dynamics and active motion control to form a novel CHVS that can enhance the driving safety and increase the likelihood of collision-avoidance. The research objectives will be pursued through analytical, computational, and experimental studies. Driving simulator and real vehicle experiments together with high-fidelity simulations will assist the investigations. The research results from this project will be disseminated through usual academic publications, CPS meetings, and visits to relevant companies for industrial collaborations. Some of the research findings will be used to enrich several undergraduate and graduate courses in different disciplines. High-school summer camp and undergraduate student research opportunities will be generated through this project to attract students to engage in the research and to pursue higher education in science and engineering.
Performance Period: 09/08/2018 - 09/30/2020
Institution: University of Texas at Austin
Sponsor: National Science Foundation
Award Number: 1901632
CPS: Breakthrough: Collaborative Research: WARP: Wide Area assisted Resilient Protection
Lead PI:
Array Array
Abstract
The electric power grid experiences disturbances all the time that are routinely controlled, managed, or eliminated by system protection measures- designed by careful engineering studies and fine-tuned by condensing years of operational experience. Despite this, the grid sometimes experiences disruptive events that can quickly, and somewhat unstoppably catapult the system towards a blackout. Arresting such blackouts has remained elusive - mainly because relays (protection devices) operate on local data, and are prone to hidden faults that are impossible to detect until they manifest, resulting in misoperations that have sometime been precipitators or contributors to blackouts. Inspired by the Presidential policy directive on resilience -- meaning the ability to anticipate, prepare, withstand, and recover from disruptive events, this project proposes "WARP: Wide Area assisted Resilient Protection", a paradigm that adds a layer of finer (supervisory) intelligence to supplement conventional protection wisdom - which we call resilient protection. Exploiting high fidelity measurements and computation to calculate and analyze energy function components of power systems to identify disturbances, WARP would allow relays to be supervised - correct operations would be corroborated, and misoperations will be remedied by judiciously reversing the relay operation in a rational time-frame. The project also envisions predicting instability using advanced estimation techniques, thus being proactive. This will provide power grid the ability to auto-correct and bounce back from misoperations, curtailing the size, scale and progression of blackouts and improving the robustness and resilience of the electric grid -- our nation's most critical infrastructure. In WARP, disruptive events are deciphered by using synchrophasor data, energy functions, and dynamic state information via particle filtering. The information is fused to provide a global data set and intelligence signal that supervises relays, and also to predict system stability. Resilience is achieved when the supervisory signal rectifies the misoperation of relays, or endorses their action when valid. This endows relays with post-event-auto-correct abilities 
- a feature that never been explored/understood in the protection-stability nexus. Architectures to study the effect of latency and bad data are proposed. WARP introduces new notions: global detectability and distinguishability for power system events, stability prediction based on
the sensitivity of the energy function components and uses a novel factorization method: (CUR) preserving data interpretability to reduce data dimensionality. All the proposed
tools will be wrapped into a simulation framework to assess scalability and accuracy-runtime tradeoffs, and quantify the degree of resilience achieved. The effectiveness of the proposed scheme during extreme events will be measured by reenacting two well-documented blackout sequences. In addition, simulations on benchmarked systems will be performed to assess scalability and accuracy-runtime tradeoffs, and quantify the degree of resilience achieved.
Performance Period: 09/01/2018 - 08/31/2019
Institution: Clemson University
Sponsor: National Science Foundation
Award Number: 1855854
CPS:Synergy:Collaborative Research: Real-time Data Analytics for Energy Cyber-Physical Systems
Lead PI:
Maggie Cheng
Abstract
Inadequate system understanding and inadequate situational awareness have caused large-scale power outages in the past. With the increased reliance on variable energy supply sources, system understanding and situational awareness of a complex energy system become more challenging. This project leverages the power of big data analytics to directly improve system understanding and situational awareness. The research provides the methodology for detecting anomalous events in real-time, and therefore allow control centers to take appropriate control actions before minor events develop into major blackouts. The significance for the society and for the power industry is profound. Energy providers will be able to prevent large-scale power outages and reduce revenue losses, and customers will benefit from reliable energy delivery with service guarantees. Students, including women and underrepresented groups, will be trained for the future workforce in this area. The project includes four major thrusts: 1) real-time anomaly detection from measurement data; 2) real-time event diagnosis and interpretation of changes in the state of the network; 3) real-time optimal control of the power grid; 4) scientific foundations underpinning cyber-physical systems. The major outcome of this project is practical solutions to event or fault detection and diagnosis in the power grid, as well as prediction and prevention of large-scale power outages.
Performance Period: 10/01/2018 - 08/31/2019
Institution: Illinois Institute of Technology
Sponsor: National Science Foundation
Award Number: 1854077
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