ABSTRACT
Distributed Denial of Service (DDoS) attack is one of the biggest cyber threats. DDoS attacks have evolved in quantity and volume to evade detection and increase damage. Changes during the COVID-19 pandemic have left traditional perimeter-based security measures vulnerable to attackers that have diversified their activities by targeting health services, e-commerce, and educational services. DDoS attack prediction searches for signals of attack preparation to warn about the imminence of the attack. Prediction is necessary to handle high-volumetric DDoS attacks and to increase the time to defend against them. This survey article presents the classification of studies from the literature comprising the current state-of-the-art on DDoS attack prediction. It highlights the results of this extensive literature review categorizing the works by prediction time, architecture, employed methodology, and the type of data utilized to predict attacks. Further, this survey details each identified study and, finally, it emphasizes the research opportunities to evolve the DDoS attack prediction state-of-the-art.
ABSTRACT
The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9-30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.
ABSTRACT
The COVID-19 pandemic has stretched public health resources to the limits, and the only realistic way to keep the infection rates low is effective testing to prevent community transmission. In this research study, we propose an innovative method to empower autonomous vehicle-driven mobile assessment facilities to support early detection of the cases contracted with the virus, and enable early detection of sources for hot spots. We describe a self organizing feature map (SOFM) approach to the allocation of the mobile assessment centers, and also use the same method to determine the travel route of the autonomous vehicles, and provide critical decision support to the supply chain manager. Our results reveal that the optimal number of neurons under varying test times can be obtained by 5 different zero-day coordinates of initially contracted cases and worst-case scenario to find out the contracted cases in 17 days and 27 days under two different test time scenarios.
ABSTRACT
The emergence of COVID-19 and its variants has dramatically shifted the way that societies respond to a pandemic crisis and postpandemic plans. One of the response needs is the ability to track potential transmissions within population movements effectively, which can exploit the means of pervasive computing by collecting and processing ubiquitously acquired anonymously aggregated data, as well as long-term data that constitute prior but limited contextual knowledge. This article presents a practical method for the assessment of the risk profiles of communities by acquiring, fusing, and analyzing data from public transportation, district population distribution, passenger interactions, and cross-locality travel data while still preserving individual user privacy. The proposed framework fuses these data sources into a realistic simulation of a transit network for a given time span to report on the risk of the public transit system as whole, as well as problematic wards and routes. By shedding credible insights into the impact of public transit on pandemic spread, the article findings will help to set the groundwork for aggregate transmission simulation tools that could provide pandemic response teams with a robust framework for the evaluations of city districts most at risk, and how to adjust municipal services accordingly. © 1973-2011 IEEE.
ABSTRACT
Internet of Things(IoT) facilitates key technologies that rely on sensing, communication and processing in daily routines. As an IoT-enabled paradigm, mobile crowdsensing (MCS) can offer more possibilities for data collection to support various IoT applications and services. As an extension, MCS can be used for data gathering amid COVID-19 pandemic crisis. Bridging Artificial Intelligence and IoT can achieve not only maintaining low infection rates of COVID-19 but can also facilitate an effective rapid testing strategy to reduce community spread. In this research, an intelligent strategy to deploy autonomous vehicle-based mobile testing facilities is proposed to enable early detection of infected cases based upon MCS data acquired through smart devices via wireless communications such as Wifi, LTE and 5G. To this end, a Self Organizing Feature Map is designed to manage MCS-based data for planning of the autonomous mobile assessment centers. Pre-identified zero-day locations and worst-case scenario are considered to determine the best combination for MCS participation rate and budget limitations. Numerical results demonstrate that once 30% of MCS participants are recruited, it becomes possible to cover the pre-identified zero-day locations and enable detection of infected cases under the worst case scenario to determine the AV routes more efficiently than other options for a certain number of neurons in SOFM. The worst-case scenario demonstrates that 30% participant rate ensures detection of infected cases in 27 days for 81 stops even infected cases are outside of the autonomous vehicle testing coverage. © 2021 Elsevier B.V.
ABSTRACT
Crowd monitoring and management is an important application of Mobile Crowdsensing (MCS). The emergence of COVID-19 pandemic has made the modeling and simulation of community infection spread a vital activity in the battle against the disease. This paper provides insights for the utility of MCS to inform the decision support systems combating the pandemic. We present an MCS-driven community risk modeling solution against COVID-19 pandemic with the support of smart mobile device users (i.e., MCS participants), who opt-in to crowdsensing campaigns and grant access to their mobile device's built-in sensors (including GPS). Each community is defined by the spatio-temporal instances of MCS participants that are clustered based on the projected future movements of these participants. The MCS platform keeps track of the mobility patterns of the participants and utilizes unsupervised machine learning (ML) algorithms, more specifically k-means, Hidden Markov Model (HMM), and Expectation Maximization (EM) to predict a risk score of COVID-19 community spread for each community ahead of time. Through numerical results from simulating a metropolitan area (e.g., Paris), it is shown that communities? COVID-19 risk scores at the end of a set of MCS campaign can be predicted 20% ahead of time (i.e., upon completion of 80% of the MCS time commitments) with a dependability score up to 0.96 and an average of 0.93. Further tests with a larger population of participants show that community risk scores can be predicted 20% ahead of time with a dependability score up to 0.99 and an average of 0.98. © 2020 ACM.