Alarm Reduction Research Articles

Hybrid Classifier for Detecting Zero-Day Attacks on IoT Networks

Recently, Internet of Things (IoT) networks have been exposed to many electronic attacks, giving rise to concerns about the security of these networks, where their weaknesses and gaps can be exploited to access or steal data. These networks are threatened by several cyberattacks, one of which is the zero-day distributed denial-of-service (DDoS) attack, which is considered one of the dangerous attacks targeting network security. As such, it is necessary to find smart solutions to address such attacks swiftly. To address these attacks, this research proposed a hybrid IDS to detect cyber-attacks on IoT networks via machine learning (ML) algorithms, namely, XGBoost, K-nearest neighbors, and stochastic gradient descent (SGD), while classifiers are combined via an ML ensemble. Grid search CV was used to find the best hyperparameters for each classifier at each classification stage. Random projection was used to select the relevant features for training the model. In the evaluation and performance testing phase of the model, two cybersecurity datasets (CIC-IDS2017 and CIC-DDoS2019) were used to test the efficiency of the model in detecting zero-day threats. The best results were obtained for the CIC-DDoS2019 dataset, where 20 features out of the total selection were used. The model was able to achieve an accuracy of 99.91% and an intrusion detection time of 0.22 seconds. The confusion matrix results also revealed a reduction in false alarms. The results and their comparison with those of recent relevant studies demonstrated the effectiveness of the hybrid model in securing IoT networks from zero-day attacks as well as its superiority in terms of accuracy and intrusion detection time. This study is an important step in enhancing security in the IoT environment by presenting a new hybrid model that is capable of dealing with zero-day attacks that are difficult to detect with traditional models.

Evading Cyber-Attacks on Hadoop Ecosystem: A Novel Machine Learning-Based Security-Centric Approach towards Big Data Cloud

The growing industry and its complex and large information sets require Big Data (BD) technology and its open-source frameworks (Apache Hadoop) to (1) collect, (2) analyze, and (3) process the information. This information usually ranges in size from gigabytes to petabytes of data. However, processing this data involves web consoles and communication channels which are prone to intrusion from hackers. To resolve this issue, a novel machine learning (ML)-based security-centric approach has been proposed to evade cyber-attacks on the Hadoop ecosystem while considering the complexity of Big Data in Cloud (BDC). An Apache Hadoop-based management interface “Ambari” was implemented to address the variation and distinguish between attacks and activities. The analyzed experimental results show that the proposed scheme effectively (1) blocked the interface communication and retrieved the performance measured data from (2) the Ambari-based virtual machine (VM) and (3) BDC hypervisor. Moreover, the proposed architecture was able to provide a reduction in false alarms as well as cyber-attack detection.

Variations in Alarm Burden, Source, and Cause Across Inpatient Units at a Children's Hospital.

Alarms at hospitals are frequent and can lead to alarm fatigue posing patient safety risks. We aimed to describe alarm burden over a 1-year period and explored variations in alarm rates stratified by unit type, alarm source, and cause. A retrospective study of inpatient alarm and patient census data at 1 children's hospital from January 1, 2019, to December 31, 2019, including 8 inpatient units: 6 medical/surgical unit (MSU), 1 PICU, and 1 NICU. Rates of alarms per patient day (appd) were calculated overall and by unit type, alarm source, and cause. Poisson regression was used for comparisons. There were 7 934 997 alarms over 84 077 patient days (94.4 appd). Significant differences in alarm rates existed across inpatient unit types (MSU 81.3 appd, PICU 90.7, NICU 117.5). Pulse oximetry (POx) probes were the alarm source with highest rate, followed by cardiorespiratory leads (54.4 appd versus 31). PICU had lowest rate of POx alarms (33.3 appd, MSU 37.6, NICU 92.6), whereas NICU had lowest rate of cardiorespiratory lead alarms (16.2 appd, MSU 40.1, PICU 31.4). Alarms stratified by cause displayed variation across unit types where "low oxygen saturation" had the highest overall rate, followed by "technical" alarms (43.4 appp versus 16.3). ICUs had higher rates of low oxygenation saturation alarms, but lower rates of technical alarms than MSUs. Clinical alarms are frequent and vary across unit types, sources, and causes. Unit-level alarm rates and frequent alarm sources (eg, POx) should be considered when implementing alarm reduction strategies.

Machine learning model with output correction: Towards reliable bradycardia detection in neonates

Bradycardia is a commonly occurring condition in premature infants, often causing serious consequences and cardiovascular complications. Reliable and accurate detection of bradycardia events is pivotal for timely intervention and effective treatment. Excessive false alarms pose a critical problem in bradycardia event detection, eroding trust in machine learning (ML)-based clinical decision support tools designed for such detection. This could result in disregarding the algorithm’s accurate recommendations and disrupting workflows, potentially compromising the quality of patient care. This article introduces an ML-based approach incorporating an output correction element, designed to minimise false alarms. The approach has been applied to bradycardia detection in preterm infants. We applied five ML-based autoencoder techniques, using recurrent neural network (RNN), long-short-term memory (LSTM), gated recurrent unit (GRU), 1D convolutional neural network (1D CNN), and a combination of 1D CNN and LSTM. The analysis is performed on ∼440 hours of real-time preterm infant data. The proposed approach achieved 0.978, 0.73, 0.992, 0.671 and 0.007 in AUC-ROC, AUC-PRC, recall, F1 score, and false positive rate (FPR) respectively and a false alarms reduction of 36% when compared with methods without the correction approach. This study underscores the imperative of cultivating solutions that alleviate alarm fatigue and encourage active engagement among healthcare professionals.

DIAmante TESS AutoRegressive Planet Search (DTARPS). II. Hundreds of New TESS Candidate Exoplanets

The DIAmante TESS AutoRegressive Planet Search for the southern ecliptic hemisphere (DTARPS-S) project seeks to identify photometric transiting planets from 976,814 southern hemisphere stars observed in Year 1 of the TESS mission. This paper follows the methodology developed by Melton et al. (Paper I) using light curves extracted and preprocessed by the DIAmante project. Paper I emerged with a list of 7377 light curves with statistical properties characteristic of transiting planets but dominated by false alarms and false positives. Here a multistage vetting procedure is applied including: centroid motion and crowding metrics, false alarm and false positive reduction, photometric binary elimination, and ephemeris match removal. The vetting produces a catalog of 462 DTARPS-S candidates across the southern ecliptic hemisphere and 310 objects in a spatially incomplete Galactic plane list. 58% were not previously identified as transiting systems. Candidates are flagged for possible blending from nearby stars based on Zwicky Transient Facility data and for possible radial velocity variations based on Gaia satellite data. Orbital periods and planetary radii are refined using astrophysical modeling; the resulting parameters closely match published values for confirmed planets. The DTARPS-S population and astrophysical properties are discussed in Paper III.

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replacing manual detection methods. However, practical implementation has exposed a limitation in current methods - their constrained ability to accurately discern the spatial dimensions of external signals, which complicates the authentication of threat events. Our research endeavors to overcome the above issues by harnessing deep learning techniques to achieve a more fine-grained recognition and localization process. This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation. This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. Specifically, we introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features and construct a threat estimation and recognition network. The utilization of collected acoustic signal data is optimized, and the underlying principle is elucidated. Moreover, we incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency. Empirical evidence gathered from real-world scenarios underscores the efficacy of our method, notably in its substantial reduction of false alarms and remarkable gains in recognition accuracy. More generally, our method exhibits versatility and can be extrapolated to a broader spectrum of recognition tasks and scenarios.

Fatigue and vigilance in medical experts detecting breast cancer

An abundance of laboratory-based experiments has described a vigilance decrement of reducing accuracy to detect targets with time on task, but there are few real-world studies, none of which have previously controlled the environment to control for bias. We describe accuracy in clinical practice for 360 experts who examined >1 million women's mammograms for signs of cancer, whilst controlling for potential biases. The vigilance decrement pattern was not observed. Instead, test accuracy improved over time, through a reduction in false alarms and an increase in speed, with no significant change in sensitivity. The multiple-decision model explains why experts miss targets in low prevalence settings through a change in decision threshold and search quit threshold and propose it should be adapted to explain these observed patterns of accuracy with time on task. What is typically thought of as standard and robust research findings in controlled laboratory settings may not directly apply to real-world environments and instead large, controlled studies in relevant environments are needed.

Improving Safety Culture by Establishing Alarm Fatigue Performance in The Adult Intensive Care Unit, Dammam Hospital

Alarm fatigue is becoming a more significant global public health issue that compromises patient safety. Alarm fatigue is especially common among critical care nurses, who are the frontline healthcare providers that provide patients with the most direct care and round-the-clock patient monitoring. An excessive number of alerts, together with erroneous and ineffective alerts, can cause alarm fatigue and negatively impact the psychological well-being of nurses and patients during their recovery process. To find a way to lower the number of false alarms and assess the effectiveness of the measures put in place, a quantitative, descriptive cross-sectional approach was employed. In the adult ICU at NGHA Dammam, this pilot project was carried out from June 2019 to November 2020. In order to identify true alarms and false alarms, the Central Monitor Nurse Daily Checklist was utilized as a data collection tool in conjunction with education training and departmental policies and procedures. There are about 820 alerts per patient each day, including 104 telemetry and 716 bedside alarms. Three months into the project, the project's top alarm measurement was 1503 alarms per patient per day. The project has shown a constant trend in alarm declinations after this peak, largely as a result of project improvement activities. The lowest recorded alarm count in November 2020 was 226 alerts, a 72% decrease from the initial 820 alarms. Nonetheless, the declining trend over the previous three months showed a 22%, 42%, and 48% drop in the overall number of alarms. This study found that alarm fatigue, which is influenced by a number of variables, including safety culture practices, was more common among critical care nurses working in the Intensive Care Unit at NGHA Dammam. Overall, during the first two months of its deployment, this study effectively met its aim and had a considerable impact on the reduction of "false alarms". The results of this study, taken together with improved psychological health for nurses and the provision of a therapeutic environment for patients, offer crucial direction for future intervention programs aimed at reducing alarm fatigue among critical care nurses. Future studies should concentrate on the traits of adherence and management, as well as the obstacles that prevent ICU staff from continuing to implement interventions and false alarm reduction strategies.

Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals.

Improving Telemetry use in the Acute Assessment Unit.

Despite published guidelines, telemetry use is inappropriate in 25-43% of cases. This impacts patient safety and telemetry effectiveness. QI methodology was used to review telemetry in a hospital acute medical unit with the aim of reducing inappropriate use and addressing alarm fatigue. A 'Telemetry Indication Form' was created. Eight weeks of baseline data was collated before introducing the 'Indication Form'. Four plan-do-study-act cycles were conducted. At each cycle, data was analysed using statistical process control charts. Inappropriate telemetry use significantly reduced from 32% to 4%. Total telemetry use also fell. Unfortunately, interventions to address alarm rates did not result in significant reduction in false alarms. A 'Telemetry Indication Form' has significant potential to improve patient safety through reducing inappropriate use.

Machine learning in concept drift detection using statistical measures

ABSTRACT In the data stream, the data has non-stationary quality because of continual and inconsistent change. This change is represented as the concept drift in the classifying process of the streaming data. Representing this data drift concept in data stream mining requires pre-labeled samples. However, labeling samples in real-time streaming (online) is not feasible due to resource utilization and time constraints. Therefore, this paper proposes the concept of Probabilistic Concept Drift Detection (PCDD) in the group classifier. PCDD relies on the data stream classification process and provides concept drift without labeled samples. The PCDD model is evaluated through an empirical study on a dataset called Poker-Hand. The study results show a high concept drift detection rate and a significant reduction in false alarms and missed detections compared to contemporary models. Hence, the results of the experimental study prove the accuracy and scalability of the PCDD model.

Multiple views in single‐path synthetic aperture sonar for mine counter‐measures classification and pre‐identification

AbstractThe authors present exploitation of Multiple Views in Single‐Path (MVSP) Synthetic Aperture Sonar (SAS). The authors demonstrate the interest of MVSP SAS for underwater Mine Counter‐Measures (MCM) with unmanned vehicles, to comply with requirements for performances of detection and classification and time constraints. The authors here lay the emphasis on exploitation in reduction of false alarms of classification by alleviating the ambiguity between mines and natural bottom objects. Finally, the authors show that MVSP SAS is offering a pre‐identification tool with high impact on MCM process duration.

Optimization of rainfall thresholds for landslide early warning through false alarm reduction and a multi-source validation

AbstractThis study proposes an innovative approach to develop a regional-scale landslide forecasting model based on rainfall thresholds optimized for operational early warning. In particular, it addresses two main issues that usually hinder the operational implementation of this kind of models: (i) the excessive number of false alarms, resulting in civil protection system activation without any real need, and (ii) the validation procedure, usually performed over periods too short to guarantee model reliability. To overcome these limitations, several techniques for reducing the number of false alarms were applied in this study, and a multiple validation phase was conducted using data from different sources. An intensity-duration threshold system for each of the five alert zones composing the Liguria region (Italy) was identified using a semiautomatic procedure called MaCumBA, considering three levels of criticality: low, moderate, and high. The thresholds were developed using a landslide inventory collected from online newspapers by a data mining technique called SECaGN. This method was chosen to account for only those events that echo on the Internet and therefore impact society, ignoring landslides occurred in remote areas, not of interest for civil protection intervention, which would adversely affect the model performance because they would result in false alarms. A calibration phase was performed to minimize the impact of false alarms, allowing at least one false alarm per year over the moderate criticality level. In addition, an innovative approach to include antecedent rainfall as the third dimension of the intensity-duration thresholds was applied, generating a consistent reduction in false alarms. The results were validated through an independent landslide inventory and were compared with (i) the alert issued by the regional civil protection agency to observe the improvements achieved with the proposed model and to evaluate to what extent the proposed model is consistent with the assessments of the civil protection and (ii) a dataset of the national states of emergency to verify the suitability of the developed thresholds for alerting citizens. The thresholds obtained showed high predictive capabilities, confirming their suitability for implementation in an operational landslide early warning system.

Robust small infrared target detection using weighted adaptive ring top-hat transformation

Complex background in infrared images often challenges the accurate detection of small targets. To address this problem, we propose a weighted adaptive ring top-hat transformation (WARTH) for extracting infrared small targets in complex backgrounds. The method utilizes an adaptive ring-shaped structural element (SE) and a target awareness indicator to effectively measure local and global feature information to detect small targets while minimizing false alarms accurately. Firstly, the difference-of-structure tensors (DoST) is designed, and the positive smallest eigenvalue of DoST (PSEDoST) is computed to construct the adaptive ring-shaped SE that captures local feature information for background estimation. Secondly, the image is converted into the Fourier phase spectrum, and a two-stage sliding window filtering technique is designed to generate the target awareness indicator that perceives global feature information of small targets. Finally, the WARTH is defined by fusing the above two measurements, which can further eliminate false alarms and improve the robustness of target detection. The experimental results demonstrate that the WARTH is superior to several advanced methods in terms of false alarm reduction and small target detection in complex backgrounds.

Detection of application-layer DDoS attacks using machine learning and genetic algorithms

Application-layer Distributed Denial of Service (App-DDoS) attacks continue to be a pervasive problem in cybersecurity, despite the availability of various defensive frameworks. This research addresses the challenges associated with App-DDoS detection and presents a highly effective and adaptable solution for detecting various types of App-DDoS attacks. Motivated by the critical need for improved DDoS detection, our approach achieves dual objectives by accurately detecting both known and unknown DDoS attacks while minimizing false alarms. To achieve this, we combine Random Forest (RF), Gaussian Mixture Models (GMM) and a human with expertise in DDoS to enhance the system's resilience against evolving attack patterns. Furthermore, we prioritize high-quality data curation by utilizing multiple datasets, CICIDS2017 and CICDDoS2019, and incorporating GMM to adapt effectively to varying data distributions over time. In addition, we propose a comprehensive feature selection strategy that addresses the false alarm rate and improves classifier performance by utilizing decision tree (DT) feature importance and the minimum redundancy maximum relevance (MRMR) approach. Moreover, we adopt genetic algorithms (GA) for automated hyper-parameter optimization to ensure efficient and effective DDoS detection. Quantitative analysis shows a significant reduction in false alarms to 0.12% (52 out of 45,149 samples), with the RF classifier achieving outstanding accuracy (99.9%), precision (100%), recall (99.8%), and F1 score (99.9%). Handling unknown App-DDoS attacks, our approach demonstrates remarkable performance across all datasets.

Response Characteristics of Smoke Detection for Reduction of Unwanted Fire Alarms in Studio-Type Apartments

Photoelectric smoke detectors (SDs) often emit false alarms in studio-type apartments, where fire prevention is crucial. This study investigates the response characteristics of conventional and analog smoke detection factors to reduce false positives in studio-type apartments. A mock-up was tested based on relevant domestic laws, standards, statistical data, and experimental cases. A simulation of a cooking scenario involving burned food items was conducted, and optical density, particulate matter (PM), and carbon monoxide levels were measured and compared with actual smoke detection at six different locations. The measured values of conventional smoke detectors (CSDs) and analog smoke detectors (ASDs) at these locations were used to derive the activation time of CSDs and ASDs for the entire mock-up space. The results showed that the CSD activated at 7.42 min, while the ASD activated at 11.57 min. PM10.0, CO, and CO2 showed similar activation time trends. The PM10.0, CO, and CO2 concentrations at the time of SD activation were estimated. The findings suggest that a sensor with a consistent coefficient of variation, such as PM10.0 and CO, should be recommended.

Opportunities for the Transformation of Border Towns into Sustainable Systems in the Republic of North Macedonia by Applying the Integral Theory

The contemporary urban functioning of cities requires adaptability and progressive development guided by the Sustainable Development Goals (SDG) that can be adjusted to local circumstances and needs. Currently, the small border towns in the Republic of North Macedonia (RNM) are facing numerous problems on social, economic and ecological levels (e.g., economic inequality, the unemployment and social isolation of citizens, the insufficient implementation of planning documents, the inadequate physical and functional structure of public spaces, the lack of environmental plans, improper waste management, etc.), which have influenced the efficiency and sustainability of their systems. Therefore, urban transformations are necessary due to the alarming reduction in the working-age population and the general stagnation which these towns experience. The aim of this article is to define possible solutions and recommendations for the ongoing urban challenges and transformations based on the performed analysis of the primary data sources, especially targeting the psychological and behavioral levels of identified problems, the culture of living and the management policies of local governments. The main research method used in the study was the AQAL method (all quadrants, all levels), which provided a comprehensive perspective of the current urban problems and conditions combined with a comparative analysis within an integrated framework. The application of these methods enabled the identification of the specific problems in the border towns, as well as the means and capacities for solving them, while simultaneously addressing the shortcomings and opportunities for urban renewal. By understanding the different viewpoints provided by the AQAL method, local governments, planners and policymakers can create adaptable urban models that can accommodate and overcome future changes and obstacles generated by contextual limitations. The obtained research results represent a starting point for the development and improvement of local sustainability through the advancement of economic innovations, environmental practices and social relations. Furthermore, specific recommendations are provided, directly and indirectly targeting the specificities of the selected towns, their urban development and management, as well as their future environmental and social sustainability.

Lean approach in the application of new technologies: integration of risk, situational awareness, and resilience by a prehospital emergency medical service

After an earthquake or an industrial chemical release, a timely and effective response is crucial and can prevent or significantly reduce the risk of casualties. To this end, first responders and rescue teams have been equipped with state-of-the-art tools and specialised instruments to improve their capabilities in terms of accuracy, rapid location, and reduction of false alarms. The European Union-funded Search and Rescue project (Emerging technologies for the Early location of Entrapped victims under Collapsed Structures and Advanced Wearables for risk assessment and First Responders Safety in SAR operations) has designed, implemented and tested a highly compatible open architecture platform for first responders in a pilot case study of a chemical incident. An analysis of major chemical accidents classified by the eMars database (Major Accident Reporting System, established by the European Seveso Directive) was carried out; it has determined the types of companies that have suffered chemical accidents with the highest number of injuries and fatalities. Based on this previous analysis, a chemical spill pilot study was devised to test advanced user equipment systems and backup applications, improving first responders’ decision-making and providing a common, dynamic operational perspective of the disaster. The Lean Method was used to evaluate processes, identify waste, test new solutions and, finally, increase the value of the product and service produced.

Quantifying the role of photoacclimation and self-facilitation for seagrass resilience to light deprivation.

Light gradients are ubiquitous in marine systems as light reduces exponentially with depth. Seagrasses have a set of mechanisms that help them to cope with light stress gradients. Physiological photoacclimation and clonal integration help to maximize light capture and minimize carbon losses. These mechanisms can shape plants minimum light requirements (MLR), which establish critical thresholds for seagrass survival and help us predict ecosystem responses to the alarming reduction in light availability. Using the seagrass Cymodocea nodosa as a case study, we compare the MLR under different carbon model scenarios, which include photoacclimation and/or self-facilitation (based on clonal integration) and that where parameterized with values from field experiments. Physiological photoacclimation conferred plants with increased tolerance to reducing light, approximately halving their MLR from 5-6% surface irradiance (SI) to ≈ 3% SI. In oligotrophic waters, this change in MLR could translate to an increase of several meters in their depth colonization limit. In addition, we show that reduced mortality rates derived from self-facilitation mechanisms (promoted by high biomass) induce bistability of seagrass meadows along the light stress gradient, leading to abrupt shifts and hysteretic behaviors at their deep limit. The results from our models point to (i) the critical role of physiological photoacclimation in conferring greater resistance and ability to recover (i.e., resilience), to seagrasses facing light deprivation and (ii) the importance of self-facilitating reinforcing mechanisms in driving the resilience and recovery of seagrass systems exposed to severe light reduction events.

Dynamic CUSUM Chart With an Integrated Indicator for Bearing Condition Monitoring

The operation status of bearings plays a critical role in the mechanical systems. Current models for condition monitoring of bearings tend to overlook the complex properties of the process, including its non-Gaussian, nonlinear, and dynamic characteristics. To address these issues, this paper proposes a novel approach for condition monitoring based on a novel dynamic cumulative sum (CUSUM) chart with an integrated indicator. The integrated indicator accounts for the non-Gaussian, nonlinearity, and dynamic properties of the process by utilizing optimal components that are decomposed via independent component analysis (ICA) and its extended versions. The developed dynamic CUSUM chart can adapt to the variation of the process. The proposed condition monitoring strategy is validated using the simulation and experiment data of bearings. Results demonstrate that the proposed method yields a significant improvement in hit rates and a reduction in false alarms compared to conventional methods.