Endpoint Detection Research Articles

We need to aim at the top: Factors associated with cybersecurity awareness of cyber and information security decision-makers.

Cyberattacks pose a significant business risk to organizations. Although there is ample literature focusing on why people pose a major risk to organizational cybersecurity and how to deal with it, there is surprisingly little we know about cyber and information security decision-makers who are essentially the people in charge of setting up and maintaining organizational cybersecurity. In this paper, we study cybersecurity awareness of cyber and information security decision-makers, and investigate factors associated with it. We conducted an online survey among Slovenian cyber and information security decision-makers (N = 283) to (1) determine whether their cybersecurity awareness is associated with adoption of antimalware solutions in their organizations, and (2) explore which organizational factors and personal characteristics are associated with their cybersecurity awareness. Our findings indicate that awareness of well-known threats and solutions seems to be quite low for individuals in decision-making roles. They also provide insights into which threats (e.g., distributed denial-of-service (DDoS) attacks, botnets, industrial espionage, and phishing) and solutions (e.g., security operation center (SOC), advanced antimalware solutions with endpoint detection and response (EDR)/extended detection and response (XDR) capabilities, organizational critical infrastructure access control, centralized device management, multi-factor authentication, centralized management of software updates, and remote data deletion on lost or stolen devices) are cyber and information security decision-makers the least aware of. We uncovered that awareness of certain threats and solutions is positively associated with either adoption of advanced antimalware solutions with EDR/XDR capabilities or adoption of SOC. Additionally, we identified significant organizational factors (organizational role type) and personal characteristics (gender, age, experience with information security and experience with information technology (IT)) related to cybersecurity awareness of cyber and information security decision-makers. Organization size and formal education were not significant. These results offer insights that can be leveraged in targeted cybersecurity training tailored to the needs of groups of cyber and information security decision-makers based on these key factors.

Deep Learning-Based Gender Recognition in Cherry Valley Ducks Through Sound Analysis.

Gender recognition is an important part of the duck industry. Currently, the gender identification of ducks mainly relies on manual labor, which is highly labor-intensive. This study aims to propose a novel method for distinguishing between males and females based on the characteristic sound parameters for day-old ducks. The effective data from the sounds of day-old ducks were recorded and extracted using the endpoint detection method. The 12-dimensional Mel-frequency cepstral coefficients (MFCCs) with first-order and second-order difference coefficients in the effective sound signals of the ducks were calculated, and a total of 36-dimensional feature vectors were obtained. These data were used as input information to train three classification models, include a backpropagation neural network (BPNN), a deep neural network (DNN), and a convolutional neural network (CNN). The training results show that the accuracies of the BPNN, DNN, and CNN were 83.87%, 83.94%, and 84.15%, respectively, and that the three classification models could identify the sounds of male and female ducks. The prediction results showed that the prediction accuracies of the BPNN, DNN, and CNN were 93.33%, 91.67%, and 95.0%, respectively, which shows that the scheme for distinguishing between male and female ducks via sound had high accuracy. Moreover, the CNN demonstrated the best recognition effect. The method proposed in this study can provide some support for developing an efficient technique for gender identification in duck production.

Development of a new method for analyzing the behavior of rapidly dissolving films in contact with liquids

Oral Thin Film (OTF) is a promising dosage form, easy to use and patient-friendly drug delivery systems suitable for every consumer profile. This dosage form ensures safety, stability, and acceptability of the drug, that's why it can be a good alternative for tablets and capsules for children and elderly therapies. As no detailed monography of OTFs including characterization methods and uniform requirements has been presented in any of the pharmacopoeias to date, this study focuses on the development of an innovative analyzing method to characterize the OTF behavior and particularly to determine its disintegration time by goniometry. These parameters are key for the development of OTFs and for studying, at an early stage, the impact of formulation and process variables on product quality and impacting Critical Quality Attributes (CQA) by a Quality by Design approach.Different model OTF preparations were investigated as models to determine the suitability of the disintegration test systems. These placebos OTF were used to compare the disintegration times obtained by the tablets pharmacopeial method (conducted in 800 mL of distilled water) and the novel proposed method. As expected, the disintegration times determined by the proposed method were similar than those obtained by the conventional disintegration test. Moreover, they seem more reliable to the in vivo behavior because of the small amount of solvent used. The experiments showed acceptable reproducibility (low variation within sample replicates due to a well-defined determination of the measurement endpoint). This method provides clear endpoint detection and is applicable for different types of OTFs based on different polymers. Moreover, a better understanding of the physical-chemical properties of oral thin films towards water could be obtained. This new method of analysis could provide a valuable approach to the development of new OTF formulas, thanks to the different information that can be obtained, such as critical energy, free energy and polar and dispersive components.

A rapid, specific and ultrasensitive detection of the Chikungunya virus based on RT-RPA:CRISPR/Cas12a one-pot dual mode end-point detection system

BackgroundChikungunya (CHIK) is an underdiagnosed acute febrile illness (AFI) and an important cause of acute encephalitis syndrome (AES). Unavailaibility of rapid and sensitive molecular point-of-care tests (PoCTs) for CHIK at grass-root level, results in increased hospital burden, due to delayed diagnosis or misdiagnosis with other clinically relevant diseases. Since, no therapeutic intervention is readily available, accurate and differential diagnosis of CHIK is the only available option to initiate early supportive treatment. Thus, we aimed to develop a one-pot reverse transcription recombinase polymerase amplification (RT-RPA) mediated CRISPR/Cas12a based detection platform for rapid, specific, and ultrasensitive detection of chikungunya virus (CHIKV) in clinical samples. ResultsWe have successfully integrated CRISPR/Cas12a technology with reverse transcription recombinase polymerase amplification (RT-RPA) for the detection of Chikungunya virus (CHIKV). The developed assay enabled rapid detection of CHIKV within 35 min, requiring minimal handling process and instrumentation. Next, this assay demonstrated dual mode end-point detection capabilities, employing both fluorescence and lateral flow detection within a reaction. Our one-pot system allows the entire process to be completed without the need to open the reaction tube, thereby eliminating the risk of cross-contamination. Remarkably, the assay exhibits an analytical sensitivity of 412 zg μL−1 (≈1 copy), and 100 % clinical sensitivity and specificity for CHIKV. Furthermore, the developed assay demonstrated limit of detection of 8 gene copies of CHIKV. The assay demonstrates precise detection of CHIKV without any cross-reactivity with other pathogens commonly associated AFI or AES. SignificanceThe overall findings of this study indicate that the RT-RPA:CRISPR/Cas12a detection assay, with one-pot dual-mode detection approach enables rapid, specific and ultrasensitive molecular detection of CHIKV. This advancement holds significant potential for CHIKV detection in resource-limited settings, providing a robust tool for diagnosis and management of the disease. This developed assay may empower clinicians to initiate prompt supportive therapy for Chikungunya fever, thereby improving patient outcomes and public health responses.

In Situ and Rapid Toxicity Assessment of Air Pollution by Self-Assembly Passive Colonization Hydrogel.

Air pollution is a leading environmental health risk factor, and in situ toxicity assessment is urgently needed. Bacteria-based bioassays offer cost-effective and rapid toxicity assessments. However, the application of these bioassays for air toxicity assessment has been challenging, due to the instability of bacterial survival and functionality when directly exposed to air pollutants. Here, we developed an approach employing self-assembly passive colonization hydrogel (SAPCH) for in situ air toxicity assessment. The SAPCH features a core-shell structure, enabling the quantitatively immobilization of bacteria on its shell while continuously provides nutrients from its core. An antimicrobial polyelectrolyte layer between the core and shell confines bacteria to the air-liquid interface, synchronizing bacterial survival with exposure to air pollutants. The SAPCH immobilized a battery of natural and recombinant luminescent bacteria, enabling simultaneous detection of various toxicological endpoints (cytotoxicity, genotoxicity and oxidative stress) of air pollutants within 2 h. Its sensitivity was 3-5 orders of magnitude greater than that of traditional liquid-phase toxicity testing, and successfully evaluating the toxicity of volatile organic compounds and combustion smoke. This study presents a method for in situ, rapid, and economical toxicity assessment of air pollution, making a significant contribution to future air quality monitoring and control.

Improved Plasma Etch Endpoint Detection Using Attention-Based Long Short-Term Memory Machine Learning

Existing etch endpoint detection (EPD) methods, primarily based on single wavelengths, have limitations, such as low signal-to-noise ratios and the inability to consider the long-term dependencies of time series data. To address these issues, this study proposes a context of time series data using long short-term memory (LSTM), a kind of recurrent neural network (RNN). The proposed method is based on the time series data collected through optical emission spectroscopy (OES) data during the SiO2 etching process. After training the LSTM model, the proposed method demonstrated the ability to detect the etch endpoint more accurately than existing methods by considering the entire time series. The LSTM model achieved an accuracy of 97.1% in a given condition, which shows that considering the flow and context of time series data can significantly reduce the false detection rate. To improve the performance of the proposed LSTM model, we created an attention-based LSTM model and confirmed that the model accuracy is 98.2%, and the performance is improved compared to that of the existing LSTM model.

Simplified Electrochemical Approach for End-Point Yet Quantitative Detection of Nucleic Acids in Resource-Limited Settings.

Nucleic acid detection plays a crucial role in various aspects of health care, necessitating accessible and reliable quantification methods, especially in resource-limited settings. This work presents a simplified electrochemical approach for end-point yet quantitative nucleic acid detection. By elevating the concentration of redox species and choosing potential as the signals, we achieved enhanced signal robustness, even in the presence of interfering substances. Leveraging this robustness, we accurately measured pH-induced redox potential changes in methylene blue solution for end-point nucleic acid detection after loop-mediated isothermal amplification (LAMP). Our method demonstrated quantitative detection of the SARS-CoV-2 N gene and human ATCB gene and successful discrimination of the human BRAF V600E mutation, comparable in sensitivity to commercial kits. The developed user-friendly electrochemical method offers a simplified and reliable approach for end-point yet quantitative detection of nucleic acids, potentially expanding the benefits of nucleic acid testing in resource-limited settings.

Acoustic modulation signal recognition based on endpoint detection

The modulation recognition technology for acoustic signals holds significant research importance in signal demodulation and communication signal reconnaissance, serving as a crucial component and key aspect. This paper investigates the modulation recognition technology for acoustic signals (< 20 kHz) from the perspectives of signal preprocessing and feature extraction. Firstly, it selects seven modulation signals 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM as recognition targets and systematically compares the effectiveness of four different endpoint detection algorithms in modulation signal recognition. To further enhance the performance of the short-time energy entropy ratio algorithm, this study introduces three different noise reduction algorithms for optimization. Finally, to accurately identify and distinguish between 2 and 4FSK signals, this study optimizes the related algorithms of the cyclic spectrum by using the kurtosis coefficient value Kur of the cyclic spectrum parameter matrix when the cyclic frequency α = 0 to differentiate between these two signals. The results show that at SNR of 4 dB, the proposed modulation recognition algorithm can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.

Quantitative Detection of the CP4-EPSPS Protein in Genetically Modified Soybeans Using a Novel Fluorescence Immunoassay Assay.

Protein-based detection methods, enzyme-linked immunosorbent assays (ELISAs) and lateral flow strips, have been widely used for rapid, specific, and sensitive detection of genetically modified organisms (GMOs). However, the traditional ELISA method for the quantitative detection of GMOs has certain limitations. Herein, a quantum dot (QD)-based fluorescence-linked immunosorbent assay was developed using QDs as fluorescent markers for the detection of glyphosate-resistant protein (CP4-EPSPS) in the MON89788 soybean. The end-point fluorescent detection system was carried out using QDs conjugated with a goat anti-rabbit secondary antibody. Compared with the conventional sandwich ELISA method, the newly developed fluorescence-linked immunosorbent assay was highly sensitive and accurate for detecting the CP4-EPSPS protein. The quantified linearity was achieved in the range of 0.05-5% (w/w) for the MON89788 soybean sample. The recovery of protein extracted from mixed MON89788 soybean samples ranged from 87.67% to 116.83%. The limits of detection and limits of quantification were 0.7101 and 2.152 pg/mL, respectively. All of the results indicated that the QD-based fluorescence-linked immunosorbent assay was a highly specific and sensitive method for monitoring the CP4-EPSPS protein in GMOs.

A machine learning-enhanced endpoint detection and response framework for fast and proactive defense against advanced cyber attacks

A machine learning-enhanced endpoint detection and response framework for fast and proactive defense against advanced cyber attacks

Intravenous lipid emulsion interference in coagulation testing: an ex vivo analysis

Introduction Intravenous lipid emulsion is used in the rescue treatment of certain poisonings. A complication is interference with laboratory analyses. The aim of this study was to determine the impact of intravenous lipid emulsion on routine laboratory analysis of coagulation parameters ex vivo and determine if any of the analytical techniques remain reliable. Methods Samples were obtained from 19 healthy volunteers and divided in triplicate. One sample served as a control, and the other two were diluted to simulate the treatment of an average adult with Intralipid® 20 per cent Fresenius Kabi 100 mL (dilution-1) or 500 mL (dilution-2). Coagulation tests performed were prothrombin time, activated prothrombin time, D-dimer concentration and fibrinogen. Coagulation testing was performed by three techniques. Test-1 was performed on a Sysmex CN6000 analyzer. Test-2 was performed with a manual mechanical endpoint method using the semi-automated Stago KC4 Delta. Test-3 involved high-speed centrifugation before repeat testing on the Sysmex CN6000 analyzer. Results For test-1, only nine (47 per cent) samples in dilution-1 could be analyzed for coagulation tests, and no coagulation tests could be analyzed for dilution-2 because of lipaemia. For test-2 and test-3, all samples could be analyzed, and all results of both testing methods fell within the limits of the laboratory reference range. Discussion Difficulties in laboratory analysis of patients having received intravenous lipid emulsion are due to multiple factors. Most automated coagulation analyzers use optical measurements, which can be unreliable in the presence of a high intravenous lipid concentration. By altering the lipaemia in the testing solution using high-speed centrifugation or by using manual mechanical endpoint detection, we were able to obtain reliable results. These findings are limited by the use of an ex vivo method and healthy volunteers. Conclusions This ex vivo model confirms that Intralipid® interferes with routine coagulation studies. It is important that clinicians are aware and inform their laboratories of its administration.

A method of using modern endpoint detection and response (EDR) systems to protect against complex attacks

The subject of the research in this article is the architecture of Endpoint Detection and Response and the EDR agent as their base parts in terms of mechanisms for detecting and countering complex attacks on information and communication systems (ICS). The aim of the work is to develop of method for improving the efficiency of using Endpoint Detection and Response (EDR) to reduce the risks of compromising ICS information, industrial, and infrastructure objects by effectively redistributing and utilizing the available EDR mechanisms, the cybersecurity team, and other resources available for implementing security measures in an enterprise, institution, or organization. The article addresses the following tasks: reviewing and analyzing existing EDR systems, analyzing the architecture of EDR solutions and EDR agents, the features of their use, the logic behind the construction of methods and mechanisms for detecting threats to the system from malicious actors and malicious code. The task of providing recommendations for the organization of ICS is also separately addressed in terms of the need to protect the entire ICS and its individual elements, as well as in terms of the available resources (the cybersecurity team, their qualifications and level of awareness of the architecture of EDR solutions) and means (available EDR system elements) for organizing protection. The following methods are used: modeling attack mechanisms, modeling attacker behavior. The following results were obtained: general and specific recommendations were formulated for optimizing the operation of EDR systems and ensuring the effective use of EDR system elements in the information and communication networks of enterprises, organizations, and institutions of various types and orientations depending on the available resources and the information requiring protection. Conclusions: The identified recommendations for the application of EDR mechanisms for protecting information systems and networks allow optimizing the costs of creating a protection infrastructure and implementing security measures, taking into account the characteristics of the available tools and the training and awareness of the cybersecurity team both in terms of response time to threats and the complexity and cost of performing protection tasks.

The Evolution of Ransomware: Tactics, Techniques, and Mitigation Strategies

Ransomware has developed into one of the most dangerous cyber threats. It is developed for the purpose of encrypting the data, and the owner is to pay the specified amount of money for the decoder. From simple rudimentary to developed sort with advanced encryption methods and modern distribution techniques, this research article is a complete historical journey of ransomware. Some of the specific aspects of ransomware development include the process of how the attackers act, starting from the traditional approach of spraying the malware to another strategic approach of singling out industries and organizations for attacks.The article also analyzes how the world came to know about ransomware-as-a-service (RaaS). The analysts have attributed the docket to the fact that it has also made it easy for inexperienced and unskilled hackers to embrace the commodity space by merely launching ransomware attacks. Current ransomware strategies that hackers employ include double extortion, where they steal information and threaten to publish the stolen data publicly, besides demanding a ransom for the encrypted information. The use of coins in the payment of ransom is examined with regard to their anonymity and the emergence of targeting of key infrastructure and other giant entities.Tactics that can allow for minimizing the attacks’ impact are equally crucial in the fight against ransomware. Some of the extensive strategies highlighted in this article are basic cyber hygiene measures, backup and restore methodologies, and endpoint detection and prevention measures. Implementing frequent training exercises to sensitize users to the risks of ransomware attacks is underlined, as is the need to have a clearly outlined incident response procedure in case of infections to enable the speedy handling of the attacks.This paper argues that an ability to comprehend the complexity of today’s ransomware, along with multiple layers of a defense system, will help organizations improve their readiness against this adaptive and continuous threat. This paper contains an analysis of the current state of ransomware and measures that need to be taken to curb the threat posed by this cyber threat; thus, it can serve as a valuable resource to cybersecurity experts.

Performance Evaluation Elastic Security as Open Source Endpoint Detection and Response for Advanced Persistent Threat Cyberattack

Detecting APT using conventional information protection systems poses significant challenges. For instance, signature-based detection tools like antivirus primarily rely on predefined signature rules to identify malware. However, in scenarios like zero-day attacks where malware signatures are unknown, detection becomes unreliable. While EDR traditionally hinges on signature-based rules, recent advancements integrate machine learning techniques for enhanced detection capabilities. In this study, we conducted an evaluation of open-source EDR, specifically Elastic Security, for APT detection. APT attack vectors were simulated utilizing the Caldera Platform. The evaluation involved validating each attack vector sent by Caldera against detection alerts generated by Elastic Security. The detection outcomes revealed three categories: detected alerts conforming to predefined rules, undetected alerts despite predefined rules, and undetected alerts due to undefined rules. Some attack vectors lacked rule definitions, potentially resulting in elevated false positives. Additionally, certain attack vectors failed to trigger alerts despite rule definitions.

An eddy current sensor for end-point detection in chemical mechanical planarization using composition signal filtration and a digital potentiometer

End-point detection (EPD) is important for controlling the chemical mechanical polishing process to prevent underpolishing and overpolishing. One EPD method using the relative current density on a Cu wafer involves an eddy current, which has advantages of noncontact and a fast response speed. However, the eddy current method has a large mean error at a low relative current density. To overcome the disadvantage, composition signal filtration uses a high mean error and long delay time using a moving average and 1-D Kalman filtration. After applying composition filtration, the mean error improved by approximately 94.7 %. Still, it is difficult to measure a thickness of 4000 Å or less with existing systems. A digital potentiometer was developed that changed the resistance in real time and improved the detection range. The step number for changing the resistance was 120 steps in the resistance range of 100–5000 Ω at approximately 40 Ω/step. When a digital potentiometer was applied, the EPD detection range improved by 85.8 % over the existing system. In other words, the developed system could measure a Cu wafer thickness of 586 Å.

Novel triplex nucleic acid lateral flow immunoassay for rapid detection of Nipah virus, Middle East respiratory syndrome coronavirus and Reston ebolavirus

We report the development of a triplex nucleic acid lateral flow immunoassay (NALFIA) for the detection of the genomes of Nipah virus (NiV), Middle East respiratory syndrome coronavirus (MERS-CoV) and Reston ebolavirus (REBOV), which are intended for screening bats as well as other hosts and reservoirs of these three viruses. Our triplex NALFIA is a two-step assay format: the target nucleic acid in the sample is first amplified using tagged primers, and the tagged dsDNA amplicons are captured by antibodies immobilized on the NALFIA device, resulting in signal development from the binding of a streptavidin-colloidal gold conjugate to a biotin tag on the captured amplicons. Triplex amplification of the N gene of NiV, the UpE gene of MERS-CoV, and the Vp40 gene of REBOV was optimized, and three compatible combinations of hapten labels and antibodies were identified for end point detection. The lowest RNA copy numbers detected by the triplex NALFIA were 8.21e4 for the NiV N target, 7.09e1 for the MERS-CoV UpE target, and 1.83e4 for the REBOV Vp40 target. Using simulated samples, the sensitivity and specificity for MERS-CoV and REBOV targets were estimated to be 100%, while the sensitivity and specificity for the NiV target were 91% and 93.3%, respectively. The compliance rate between triplex NALFIA and real-time RT‒PCR was 92% for the NiV N target and 100% for the MERS-CoV UpE and REBOV Vp40 targets.

Biodosimetry of UV radiation through the detection of cytogenetic endpoints in Allium cepa meristems

In this study, we examined the response of Allium cepa sprout stem cells, or meristems, to UVB and UVC radiation. The choice of Allium cepa, or onion, was made to avoid the controversial use of animal models. Allium cepa is a well-established in vivo standard model that is frequently used in cytogenetic research connected to various environmental contaminants. Indicators such as micronuclei and chromosomal abnormalities were used to evaluate the genotoxicity of UVB and UVC radiation, and the mitotic index was used to investigate the cytotoxicity of the radiation, providing information on cellular proliferation. The Shapiro-Wilk test (p < 0.05) confirmed the normality of the data. The analysis of the Pearson linear correlation coefficient (r = 0.97), conducted across all dose points considered, including the negative control, revealed an almost perfect positive linear relationship between the dose and the number of cells with micronuclei, for both UVB and UVC. The frequency of induction of micronuclei as a function of dose for both radiation types was found to be similar. However, a difference in the morphology of the cells exposed to UVB radiation compared to those exposed to UVC radiation was observed. In conclusion, the mitotic index analysis showed no significant differences in cell activity between UVC and UVB irradiation compared with control samples. The results from this study support the use of Allium cepa and cytogenetic endpoints as a biodosimetric method for ultraviolet radiation.

Analysis of methods for bypassing modern EDR endpoint protection systems

The purpose of the article is to review and analyze the methods of bypassing complex solutions for endpoint protection (EDR). The article highlights and describes the salient features of each of the EDR bypass methods and provides recommendations for countering them. EDR (Endpoint Detection and Response) is a type of cross-platform software currently most commonly used for event monitoring, security incident generation and formalization, and incident response. For each method and tool, example of it’s useand advantages and disadvantages are described. The article will be useful for cybersecurity analysts who want to deepen their knowledge of EDR and strengthen endpoint security. It will provide readers with an insight into EDR bypass techniques and help them apply recommendations to reduce the risks of EDR bypass during cyber ttacks.

Advanced Techniques of Malware Evasion and Bypass in the Age of Antivirus

The use of antivirus software as the main line of protection against growing cyber threats highlights the necessity of comprehending and resolving its limits. This study provides light on the ease of use and accessibility of tools used by hackers by carefully examining the complex terrain of malware evasion and bypass tactics. The persistent evolution of malware evasion and bypass techniques presents a significant cybersecurity challenge. The main objective is to educate users about the ever-changing hazards and provide them with the knowledge they need to properly strengthen their digital defenses. The literature analysis highlights the necessity for continued attention by establishing a strong correlation between the effectiveness of evasion strategies and their age and popularity. While modern antivirus software shows strong resistance against a range of tried-and-true techniques when updated on a regular basis, the study reveals a crucial component in its testing. This entails applying simple yet effective tweaks to well-known evasion techniques, demonstrating their capacity to fool even the most recent antivirus software. A thorough examination of malware evasion tactics, including both on-desk and in-memory approaches, is given in the methods section. Packing, obfuscators, protectors, reflective DLL injection, remote process memory injection, process hollowing, and inline hooking are all covered in detail in this paper. Subsequently, the study delves deeper into distinct evasion strategies, such defensive evasion through direct system calls and sophisticated evasion tactics, showcasing malware developers' versatility in evading antivirus and endpoint detection and response (EDR) systems.

Using QUASR-PCR as a field-based genotyping assay for a tick acaricide resistance marker

A novel, turnkey, field-based workflow was developed and validated using Rhipicephalus microplus DNA as a template to detect the presence of the voltage-gated sodium channel kdr mutation. The field-based compatible workflow comprises manual sample homogenization for DNA extraction, PCR amplification of the targets in a closed tube, and end-point detection of the PCR products. An R. microplus species-specific assay was also included to confirm species identity and ensure the validity of the kdr mutation assay. The assays were sensitive and specific to the targets, and the workflow resulted in a turnaround time of approximately 1 h at a low cost. The novel combination of PCR with closed-tube and end-point fluorescent detection allows for easy conversion of existing conventional lab-based PCR assays into field-based detection assays. The incorporation of custom-designed 3D-printed components in the workflow provides easy adaptability and modification of the components for diverse nucleic acid detection workflows.