Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 4 de 4
Mais filtros

Base de dados
Intervalo de ano de publicação
Proc Natl Acad Sci U S A ; 118(34)2021 08 24.
Artigo em Inglês | MEDLINE | ID: mdl-34408019


Understanding how populations' daily behaviors change during the COVID-19 pandemic is critical to evaluating and adapting public health interventions. Here, we use residential electricity-consumption data to unravel behavioral changes within peoples' homes in this period. Based on smart energy-meter data from 10,246 households in Singapore, we find strong positive correlations between the progression of the pandemic in the city-state and the residential electricity consumption. In particular, we find that the daily new COVID-19 cases constitute the most dominant influencing factor on the electricity demand in the early stages of the pandemic, before a lockdown. However, this influence wanes once the lockdown is implemented, signifying that residents have settled into their new lifestyles under lockdown. These observations point to a proactive response from Singaporean residents-who increasingly stayed in or performed more activities at home during the evenings, despite there being no government mandates-a finding that surprisingly extends across all demographics. Overall, our study enables policymakers to close the loop by utilizing residential electricity usage as a measure of community response during unprecedented and disruptive events, such as a pandemic.

COVID-19/epidemiologia , COVID-19/prevenção & controle , Controle de Doenças Transmissíveis/métodos , Comportamento Cooperativo , Equipamentos e Provisões Elétricas/estatística & dados numéricos , Eletricidade , Quarentena , COVID-19/transmissão , Características da Família , Humanos , Saúde Pública , SARS-CoV-2/isolamento & purificação , Singapura/epidemiologia
Sci Rep ; 11(1): 5329, 2021 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-33674635


Disinformation continues to raise concerns due to its increasing threat to society. Nevertheless, a threat of a disinformation-based attack on critical infrastructure is often overlooked. Here, we consider urban traffic networks and focus on fake information that manipulates drivers' decisions to create congestion at a city scale. Specifically, we consider two complementary scenarios, one where drivers are persuaded to move towards a given location, and another where they are persuaded to move away from it. We study the optimization problem faced by the adversary when choosing which streets to target to maximize disruption. We prove that finding an optimal solution is computationally intractable, implying that the adversary has no choice but to settle for suboptimal heuristics. We analyze one such heuristic, and compare the cases when targets are spread across the city of Chicago vs. concentrated in its business district. Surprisingly, the latter results in more far-reaching disruption, with its impact felt as far as 2 km from the closest target. Our findings demonstrate that vulnerabilities in critical infrastructure may arise not only from hardware and software, but also from behavioral manipulation.

IEEE Trans Cybern ; 51(7): 3687-3698, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32452784


This article investigates the impact of data integrity attacks (DIAs) on cooperative economic dispatch of distributed generators (DGs) in an ac microgrid. To establish resiliency against such attacks and ensure optimal operation, a localized event-driven attack-resilient scheme is proposed. Most of the existing works examine neighboring information to infer the presence of DIAs, where the detection is limited to events such as multiple link failures. Two kinds of DIAs are considered in this article-namely, fault and random attacks, which are segregated based on the final values of consensus updates. First, to improve the robustness of the detection theory, a localized resilient control update is designed by modeling each DG with a reference incremental cost. Second, event-driven control signal is generated for the local incremental cost and held upon the detection of attacks, to prevent malicious data from propagating to the neighboring nodes. The proposed strategy acts immediately upon the detection of DIA to ensure maximization in the economic profit. Furthermore, the proposed detection approach is theoretically verified and validated using simulation conditions.

PLoS One ; 15(8): e0236517, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32785250


Social media has made it possible to manipulate the masses via disinformation and fake news at an unprecedented scale. This is particularly alarming from a security perspective, as humans have proven to be one of the weakest links when protecting critical infrastructure in general, and the power grid in particular. Here, we consider an attack in which an adversary attempts to manipulate the behavior of energy consumers by sending fake discount notifications encouraging them to shift their consumption into the peak-demand period. Using Greater London as a case study, we show that such disinformation can indeed lead to unwitting consumers synchronizing their energy-usage patterns, and result in blackouts on a city-scale if the grid is heavily loaded. We then conduct surveys to assess the propensity of people to follow-through on such notifications and forward them to their friends. This allows us to model how the disinformation may propagate through social networks, potentially amplifying the attack impact. These findings demonstrate that in an era when disinformation can be weaponized, system vulnerabilities arise not only from the hardware and software of critical infrastructure, but also from the behavior of the consumers.

Comunicação , Disseminação de Informação , Mídias Sociais , Rede Social , Cidades , Sistemas Computacionais , Decepção , Humanos , Londres , Software , Inquéritos e Questionários