Service Outage Research Articles

Canaries in coal mines and normal accidents: The CrowdStrike outage and its lessons for critical infrastructure

AbstractThe service outage caused by a faulty CrowdStrike software update in July 2024 triggered a widespread disruption in services and activities worldwide, ranging from airline operations, to credit card transaction processing, emergency and cellular telephone operations, and technical issues for more than 8 million Microsoft Windows users. The outage was not the result of a deliberate attempt to disrupt systems, but rather of what Charles Perrow has termed a “normal accident.” This article explores the implications of technological complexity for critical infrastructure and offers ideas for a research agenda to better understand resilience in tightly coupled, critical infrastructure systems.

Towards Resource-Efficient DDoS Detection in IoT: Leveraging Feature Engineering of System and Network Usage Metrics

The Internet of Things (IoT) is omnipresent, exposing a large number of devices that often lack security controls to the public Internet. In the modern world, many everyday processes depend on these devices, and their service outage could lead to catastrophic consequences. There are many Deep Packet Inspection (DPI) based intrusion detection systems (IDS). However, their linear computational complexity induced by the event-driven nature poses a power-demanding obstacle in resource-constrained IoT environments. In this paper, we shift away from the traditional IDS as we introduce a novel and lightweight framework, relying on a time-driven algorithm to detect Distributed Denial of Service (DDoS) attacks by employing Machine Learning (ML) algorithms leveraging the newly engineered features containing system and network utilization information. These features are periodically generated, and there are only ten of them, resulting in a low and constant algorithmic complexity. Moreover, we leverage IoT-specific patterns to detect malicious traffic as we argue that each Denial of Service (DoS) attack leaves a unique fingerprint in the proposed set of features. We construct a dataset by launching some of the most prevalent DoS attacks against an IoT device, and we demonstrate the effectiveness of our approach with high accuracy. The results show that standalone IoT devices can detect and classify DoS and, therefore, arguably, DDoS attacks against them at a low computational cost with a deterministic delay.

Overhead and Performance of Dynamic Network Slice Allocation for Mobile Users

Based on virtualisation technologies like SDN and NFV, modern networks like 5G create customised logical networks known as Network Slicing to serve distinct user groups. The mobility of a significant part of these users brings great challenges for that scenario, e.g. demand variations and continuous connectivity. In such a scenario, the dynamic resource allocation of these slices is shown as a possible solution. However, that approach may be sensitive to computing overhead and trade-off scenarios, which are not fully evaluated in the literature. This work focuses on that gap by researching the impact of network slice reconfiguration overhead on the overall network performance. An extensive evaluation of Dynamic Network Slicing in providing user mobility support in Fog computing-based architectures is presented. This paper presents a resource allocation model that calculates the required time to allocate a slice instance. Furthermore, based on those values, this work presents an evaluation of the impact of resource allocation delays in terms of bandwidth allocation, latency, and service outage in different dynamic slicing scenarios that include service migration. Simulations based on realistic values point out that reducing the allocation delay from 60 to 30 s can lead the network to 25% more stable latency to its users in 60 min slice lifetime.

Countering DDoS threats: leveraging ensemble methods for detection and mitigation

Cloud computing is the modern concept of distributing numerous services through the Internet, such as web applications, databases, and programmers that operate on several servers. As Cloud computing technologies evolve, increasing susceptibility to attack may result from service outages during data storage and transmission. The most common sort of assault against Cloud settings is distributed denial-of-service (DDoS). Several approaches for detecting and mitigating these attacks have been offered, however they are ineffective. In this research, we propose a method for detecting and mitigating DDoS attacks in their early phases, considering top-layer advances at the application layer and the TCP handshake mechanism. This study employs a variety of ensemble-based machine learning approaches to classify incoming data as legitimate or malicious to respond to DDoS attacks at the application layer. Furthermore, the double TCP connection concept is used to prevent DDoS. Experiments show that the stacked voting system detects DDOS attacks with the best F-score of 99.9%.

Analysis of Ukraine power grid cyber-attack 2015

Analysis of Ukraine power grid cyber-attack 2015

Multiple objectives dynamic VM placement for application service availability in cloud networks

Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement problem in order to optimize application availability in cloud computing paradigm. The problem is modeled as an integer nonlinear programming (INLP) optimization with multiple objectives and constraints. The framework comprises three major modules that use optimization methods and algorithms to determine the most effective VM placement strategy in cases of application deployment, failure, and scaling. Our primary goals are to minimize power consumption, resource waste, and server failures while also ensuring that application availability requirements are met. We compare our proposed heuristic VM placement solution with three related algorithms from the literature and find that it outperforms them in several key areas. Our solution is able to admit more applications, reduce power consumption, and increase CPU and RAM utilization of the servers. Moreover, we use a deep learning method that has high accuracy and low error loss to predict application task failures, allowing for proactive protection actions to reduce service outage. Overall, our framework provides a comprehensive solution by optimizing dynamic VM placement. Therefore, the framework can improve the quality of cloud computing services and enhance the experience for users.

The Republic of Kakao goes on hiatus: The public cost of platform monopolies in South Korea

In the wake of the recent nationwide service outage of Kakao, this article questions the cost that the Korean public may have to bear when private platform monopolies imbue all facets of public lives. Using cultural political economy as both a theoretical framework and a mode of inquiry, it seeks to unravel the ways in which platform monopolies are legitimized and defended by a mix of institutional, regulatory, and discursive forces. Findings reveal that Kakao’s proprietary digital platforms and their services are not only justified by dominant political–economic forces, but also discursively appropriated by themselves and the government for the purposes of driving innovation, elevating national competitiveness, and promoting economic growth, yet at the expense of public scrutiny and accountability. The article concludes by briefly evaluating the implications that these unwelcoming trends have for the Korean public.

How forcing community resilience in rural communities harms sustainable development

Community resilience is critical to managing the effects of climate change and in achieving the United Nations (UN) Sustainable Development Goals (SDG). Resilient communities are able to manage stressors and recover from them, such as in instances of energy service outages. Instances like these can lead to communities that feel forced to exhibit individual characteristics of resilience, such as neighbors relying on each other in times of need because history has shown them that they cannot rely on outside institutions for help. Communities may adopt factors of individual psychological resilience in the face of energy service outages because they lack structural support to exhibit community resilience or to pursue resilient energy systems. This lack of access to support and resources is in conflict with principles of procedural justice and energy sovereignty while reinforcing institutional mistrust within affected communities and contributing to social vulnerability. This article contemplates and expounds on the idea of coerced resilience in the face of energy service outages and severe weather within a rural, remote community in Michigan’s Upper Peninsula (UP). The UP is located at the tail end of electricity infrastructure, putting its residents at increased risk of experiencing energy service outages that are further complicated by its isolation and severe winter weather. We examine the idea of coerced resilience, its relation to social vulnerability, and how it conflicts with concepts of energy justice and the UN’s SDG. We further go on to highlight how certain populations and youth can minimize instances of coerced resilience and contribute to sustainable development making it an important consideration to achieve sustainable development goals.

Turn-Down Capability of Ansaldo Energia's AE94.3A

Abstract At an ever-faster pace, market's requirements for operational flexibility and turn-down capabilities are fundamental for heavy-duty gas turbines. Gas turbine energy plants require the lowest minimum environmental load (MEL) for times when the power request is mainly covered by renewable sources and, however, need to promptly respond to green energy shortages. Ansaldo Energia has addressed such requirements overall its gas turbine portfolio, recently including GT26, as discussed in GT2021-59457. This paper addresses the MEL reduction for AE94.3A via a modular upgrade package intended for the 110+ units Service fleet. To accomplish that, several features were designed and tested including variable inlet guide vanes (VIGV) extra-closure, air bypass through compressor blow-off lines, and extended use of anti-ice air recirculation. None of these require major hardware changes but only upgrades within a normal service outage, together with minor control system adaptations. The wide-ranging technical challenges attached to the MEL reduction features are discussed. Validation on field via special instrumentation focused on compressor, secondary air system (SAS), and turbine for several engines is also presented. The achieved minimum environmental load reduction is shown and its potential discussed together with some features already in place, such as the use of CO catalyzer or the regulation of control valves put on external cooling lines. The readiness and performance level of this MEL upgrade package, already in operation on several power plants, is here demonstrated. This makes the AE94.3A gas turbine more flexible toward current and future market requirements.

Public opinion effects of digital state repression: How internet outages shape government evaluation in Africa

ABSTRACT 1 Internet shutdowns have become a popular instrument for repressive regimes to silence dissent in a digitized world. While authorities seek to suppress opponents by imposing Internet outages, we know little about how the public reacts to such incisive measures. The regime might face anger and resentment from the public as a response to Internet deprivation. Why do regimes still use Internet shutdowns when they do not only face economic but also societal losses? In this paper, I argue that Internet shutdowns lower the public’s evaluation of the political leadership as citizens blame the government for the service outages. For the analysis, I combine fine-grained data on Internet outages with survey data from the Afrobarometer and apply an “unexpected event during survey design.” Results show that citizens do not hold the government accountable for Internet disruptions, thus making Internet shutdowns a powerful tool for autocrats to silent dissent digitally.

Willingness to Pay for Ancillary Water Service Improvements in Puerto Rico

Water scarcity impedes economic development and can be expensive or infeasible to resolve in many developing countries with recurring water service outages. In such circumstances, water supply organizations may be able to improve ancillary water service attributes to alleviate the burden of service interruptions. We use a discrete choice experiment to estimate households’ willingness to pay (WTP) for improved water quality, coordinated scheduling of planned service interruptions, water use restrictions, and improved dissemination of information about upcoming service interruptions in Puerto Rico. The results indicate that households’ WTP varies considerably across attributes and is greatest ($ 28/month per household) for ensuring adequate water quality. Preferences for water service improvements vary across geographic locations and latent user groups.

Whatsapp Panic Attitudes and Practices Facing the Application's Downtime

This study investigates the attitudes and practices of WhatsApp users in response to Facebook's global service outage in October 2020. It is framed within cultural software studies and examines how users experience and relate to social networking platforms. The research also explores the generational dimension in the use of technology and its impact on social relationships.

Whatsapp Panic Attitudes and Practices Facing the Application's Downtime

This study investigates the attitudes and practices of WhatsApp users in response to Facebook's global service outage in October 2020. It is framed within cultural software studies and examines how users experience and relate to social networking platforms. The research also explores the generational dimension in the use of technology and its impact on social relationships.

Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis

The main goal of this research is to analyze the perception of service in public sector companies in the city of Bogota via Twitter and text mining to identify areas, problems, and topics aiming for quality service improvement. To achieve this objective, a structured method for data modeling is implemented based on the KDD methodology. Tweets from January to June 2022 related to the companies in the sector are processed, and a temporal analysis of the evolution of sentiment is performed based on the dictionaries Bing, AFINN, and NRC. Subsequently, the LDA algorithm (Latent Dirichlet Allocation algorithm) is used to visually identify the topics with the greatest negative impact reported by the users in each of the 6 months by adding the temporal dimension. The results revealed that, for Aqueduct (water supply service), the topic with the highest dissatisfaction is related to the “Water Tank Request” processes; for Enel (energy services) “Service Outages”; and for Vanti (gas services), “Case solution and request information”. Temporal patterns of tweets, sentiments, and topics are also highlighted for the three companies.

Publisher Correction: IT service outage cost: case study and implications for cyber insurance

Publisher Correction: IT service outage cost: case study and implications for cyber insurance

A White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-Based Cybersecurity Solution for a Smart City Environment.

Smart grids (SGs) play a vital role in the smart city environment, which exploits digital technology, communication systems, and automation for effectively managing electricity generation, distribution, and consumption. SGs are a fundamental module of smart cities that purpose to leverage technology and data for enhancing the life quality for citizens and optimize resource consumption. The biggest challenge in dealing with SGs and smart cities is the potential for cyberattacks comprising Distributed Denial of Service (DDoS) attacks. DDoS attacks involve overwhelming a system with a huge volume of traffic, causing disruptions and potentially leading to service outages. Mitigating and detecting DDoS attacks in SGs is of great significance to ensuring their stability and reliability. Therefore, this study develops a new White Shark Equilibrium Optimizer with a Hybrid Deep-Learning-based Cybersecurity Solution (WSEO-HDLCS) technique for a Smart City Environment. The goal of the WSEO-HDLCS technique is to recognize the presence of DDoS attacks, in order to ensure cybersecurity. In the presented WSEO-HDLCS technique, the high-dimensionality data problem can be resolved by the use of WSEO-based feature selection (WSEO-FS) approach. In addition, the WSEO-HDLCS technique employs a stacked deep autoencoder (SDAE) model for DDoS attack detection. Moreover, the gravitational search algorithm (GSA) is utilized for the optimal selection of the hyperparameters related to the SDAE model. The simulation outcome of the WSEO-HDLCS system is validated on the CICIDS-2017 dataset. The widespread simulation values highlighted the promising outcome of the WSEO-HDLCS methodology over existing methods.

Snake Optimization Technique for Spectrum Handoff in Cluster based Cognitive Radio Network

In Cognitive Radio (CR) networks, the use of Secondary Users (SU) in the spectrum has an undesirable effect on spectrum handoff, which causes a handoff delay. The handoff procedure can result in service outages and considerable transmission quality delays, making it a regular source of concern for the SU. An effective spectrum handoff strategy that utilizes the Spectrum Binary Snake Optimization (SBSO) algorithm and the M/G/1 queuing model has been proposed in this study. The use of Cluster Based Cooperative Spectrum Sensing improves SU performance and reduces channel congestion. In order to report the active and inactive channels in the spectrum, the cluster head is connected to the SU base station, and a decision report is subsequently generated by the fusion center. With the use of a bitwise and mutation operator format, SBSO reduces the overall service time required for handoff in the approach that is being proposed. The proposed methodology also provides a framework for observing how primary user activity and spectrum handoff delays behave in the presence of potential interruptions in a CR network. The simulation model of the proposed work optimizes the packet delivery ratio with the three benchmark functions, and provides optimal handoff, and is compared to SBSO and other models that offer a better trade off over delay achievement.

A conceptual framework for estimation of initial emergency food and water resource requirements in disasters

Many households lack the necessary food and water supplies to sustain themselves for more than three days during a disaster. Community vulnerability assessments can be used to identify households with more pressing needs for emergency food and water resources. It is critical that these assessments include the interaction between physical impacts to lifeline infrastructure and the social vulnerabilities of food and water insecurity to prioritize, allocate, and distribute emergency resources. In this paper, we review and synthesize relevant literature to propose a new multidisciplinary conceptual framework of community vulnerability assessment for estimating initial emergency food and water resource requirements in a developed country. Using the framework as a guide, we illustrate its practical application through a simplified, deterministic model of initial resource requirements in disaster response, and offer a quantitative, comprehensive description of its application within the geophysical hazard context of the “ShakeOut” scenario—a major Mw 7.8 earthquake on California's San Andreas fault, occurring within the Los Angeles Basin, CA (USA) region. Model results estimate that 999,027 households (2,947,130 residents) will require initial emergency food and water resource requirements. Estimates include about 6 million meals and 9 million liters of water, concentrated in Lancaster-Palmdale, El Monte-Baldwin Park, East Los Angeles-Downey in Los Angeles County, the Coachella Valley (Riverside County), and in populated areas of San Bernardino County. A sensitivity analysis of social vulnerability interactions with utility service outages investigates the influence of food insecurity on the amplification of resource needs. This study establishes fundamental knowledge at the nexus of natural hazards, critical infrastructure disruptions, and social vulnerability by providing initial estimates of emergency resource demand while advancing the understanding of social inequity in emergency resource access.

NON-COMMUNICABLE DISEASE SYNDEMICS AMONG LOW-INCOME POPULATIONS: PAKISTAN AND WAY FORWARD

Non-communicable diseases (NCDs) have substantially contributed to morbidity and mortality of populations. The emergence of the novel coronavirus SARS-CoV2 pandemic took centre stage toppling over the global health and economies leading to high morbidity and mortality rates. Presently as many as one in five with underlying NCDs are at risk of severe Covid-19[3].In May 2020, the World Health Organization (WHO) undertook a fast evaluation survey, which revealed that 75% of nations have experienced service outages related to NCDs. Public health campaigns and NCD surveillance programmes were among the hardest damaged. Gains against COVID-19 are hollow wins when the risky interactions between NCDs and virus infection are not perceived as syndemics along with social and economic disparities.

I-HARF: Intelligent and Hierarchical Framework for Adaptive Resource Facilitation in Edge-IoT Systems

Edge computing is being used to facilitate closer computing, storage, and networking resources to support various IoT applications including delay-sensitive ones. It is envisioned that the future Edge-IoT systems will incorporate heterogeneous IoT devices distributed over multiple geographical zones of certain institutions with edge resource demands that vary according to time and location. Edge servers (resource facilitators) are with limited resources and are susceptible to “outlandish” situations, such as service overloading, outage, and external attacks; they may also have to handle the roaming of IoT devices among different zones. These situations induce the need for alternative edge servers using an adaptive resource facilitation scheme to fulfill the demands of the IoT applications. In this article, we develop a novel intelligent and hierarchical resource facilitation framework named I-HARF that adapts to dynamic Edge-IoT situations, including outlandish situations, mobility, application’s sensitivity, and varying resource demand of IoT applications based on time and location. I-HARF achieves an adaptive facilitation and holistically addresses the facilitation technical barriers by: 1) adopting the hierarchical structure which efficiently migrates the resource facilitation from intrazone to interzone levels; 2) extending novel intrazone and interzone optimization models to boost the utilities of the edge servers and the IoT applications; and 3) developing a novel and unique actor dual-critic and collective actor–critic deep reinforcement learning (DRL) designs that intelligently facilitate the edge resources in both intrazone and interzone, respectively. The evaluation results demonstrate I-HARF’s capability enabling adaptive resource facilitation that adjusts according to the dynamic Edge-IoT situations.