In recent years, advanced threats against Cyber–Physical Systems (CPSs), such as Distributed Denial of Service (DDoS) attacks, are increasing. Furthermore, traditional machine learning-based intrusion detection systems (IDSs) often fail to efficiently detect such attacks when corrupted datasets are used for IDS training. To face these challenges, this paper proposes a novel error-robust multidimensional technique for DDoS attack detection. By applying the well-known Higher Order Singular Value Decomposition (HOSVD), initially, the average value of the common features among instances is filtered out from the dataset. Next, the filtered data are forwarded to machine learning classification algorithms in which traffic information is classified as a legitimate or a DDoS attack. In terms of results, the proposed scheme outperforms traditional low-rank approximation techniques, presenting an accuracy of 98.94%, detection rate of 97.70% and false alarm rate of 4.35% for a dataset corruption level of 30% with a random forest algorithm applied for classification. In addition, for error-free conditions, it is found that the proposed approach outperforms other related works, showing accuracy, detection rate and false alarm rate of 99.87%, 99.86% and 0.16%, respectively, for the gradient boosting classifier.
The rapid and accurate determination of the level of taurine biomarker in various tissues and body fluids can be of great interest in the early diagnosis of several important pathologies and diseases. In the present study, an innovative electrochemical interface for quantitation of taurine based on ternary signal amplification strategy was fabricated. In this work, silver nanoparticles were electrodeposited onto green and biocompatible nanocomposite containing α-cyclodextrin as conductive matrix. Therefore, a double layer film based on α-cyclodextrin and silver nanoparticles was exploited to develop a highly sensitive electrochemical sensor for detection of taurine. Fully electrochemical methodology was used to prepare a transducer on a glassy carbon electrode which provided a high surface area towards sensitive detection of taurine biomarker. The surface morphology of electrode surface was characterized by high-resolution field emission scanning electron microscope (FE-SEM). The proposed sensing platform provides a simple tool for taurine detection. The calibration curve for taurine concentration was linear in 0.7 nM to 0.1 mM with low limit of quantification of 0.7 nM. The practical analytical utility of the modified electrode was illustrated by determination of taurine in unprocessed human plasma samples with recovery of 90.8% to 104%.
Automatized scalable healthcare support solutions allow real-time 24/7 health monitoring of patients, prioritizing medical treatment according to health conditions, reducing medical appointments in clinics and hospitals, and enabling easy exchange of information among healthcare professionals. With recent health safety guidelines due to the COVID-19 pandemic, protecting the elderly has become imperative. However, state-of-the-art health wearable device platforms present limitations in hardware, parameter estimation algorithms, and software architecture. This paper proposes a complete framework for health systems composed of multi-sensor wearable health devices (MWHD), high-resolution parameter estimation, and real-time monitoring applications. The framework is appropriate for real-time monitoring of elderly patients' health without physical contact with healthcare professionals, maintaining safety standards. The hardware includes sensors for monitoring steps, pulse oximetry, heart rate (HR), and temperature using low-power wireless communication. In terms of parameter estimation, the embedded circuit uses high-resolution signal processing algorithms that result in an improved measure of the HR. The proposed high-resolution signal processing-based approach outperforms state-of-the-art HR estimation measurements using the photoplethysmography (PPG) sensor.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.