Search Results (7)

The convergence of operational technology (OT) and information technology (IT) in industrial environments, such as water treatment plants, has significantly increased the attack surface of Supervisory Control and Data Acquisition (SCADA) systems. Traditional intrusion detection systems (IDS), which focus solely on network traffic, often fail to detect stealthy, process-level attacks. This paper proposes a Digital Twin-driven Intrusion Detection (DT-ID) framework that integrates high-fidelity process simulation, real-time sensor modeling, adversarial attack injection, and hybrid anomaly detection using both physical residuals and machine learning. We evaluate the DT-ID framework using a simulated water treatment plant environment, testing against false data injection (FDI), denial-of-service (DoS), and command injection attacks. The system achieves a detection F1-score of 96.3%, a false positive rate below 2.5%, and an average detection latency under 500 ms, demonstrating substantial improvement over conventional rule-based and physics-only IDS in identifying stealthy anomalies. Our findings highlight the potential of cyber-physical digital twins to enhance SCADA security in critical infrastructure. In the following sections, we present the motivation and approach underlying this framework. Full article

This paper investigates the integration of intrusion detection systems (IDSs) within Digital Twin (DT) architectures to enhance cybersecurity in industrial environments. Using the CICIDS2017, CIC Modbus, and 4SICS 2015 datasets, we evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) in detecting network intrusions. Results indicate that RF achieves an accuracy of 99.9% for CICIDS2017, with high precision, recall, and low false positives. In contrast, SVM exhibits an accuracy of 94.2% for the same dataset, struggling with high rates of false positives and moderate recall. Similarly, for 4SICS 2015, RF demonstrates an accuracy of 93%, being balanced and reliable for industrial applications, while SVM shows only 88% accuracy, with a low precision of 65% and a high false alarm rate. For the CIC Modbus dataset, RF displays an accuracy of 95% in validation and 93% in testing, highlighting strong detection in ICS networks. However, SVM maintains an accuracy of 88%, with weak separation between benign and malicious traffic, and a higher misclassification rate. Our findings highlight the importance of DT-IDS integration in real-time threat detection and system resilience, paving the way for future research in deep learning-based IDS solutions. Full article

Degenerative diseases are one of the leading causes of chronic disability on a global scale, significantly affecting the quality of life of sufferers. These diseases also burden the health care system and individuals financially. The implementation of preventive strategies can be postponed until an accurate prediction of the disease status can be achieved. Degenerative diseases that are the leading cause of death in many countries are coronary heart disease (CHD), while diabetes mellitus disease (DMD) increases the risk of CHD. Most of the predictor variables from the dataset to predict the status of both diseases are continuous. However, not all prediction methods, including the Decision Tree Iterative Dichotomiser3 (DTID3) method, can process continuous data. This work aims to predict the status of both degenerative diseases, CHD and DM, using the DTID3 method with continuous type predictor variables transformed using discretization with the concept of set membership. Seven prediction models using the DTID3 method are proposed to predict the status of each degenerative disease. One DTID3 model uses the concept of crisp set membership, and six DTID3 models use the concept of fuzzy set membership (FDTID3). Each prediction model of FDTID3 represents one combination of fuzzy membership functions in discretizing continuous predictor variables, and one combination consists of three membership functions. The performance of the proposed FDTID3 model depends on the fuzzy membership functions used. The hypothesis that the performance of the seven proposed models differs at least in one metric and that the performance of the FDTID3 models is higher than the DTID3 model discretized using the concept of crisp sets has been proven. Full article

Oceanic trajectories frequently exhibit multiple periodic patterns across various time intervals, e.g., tidal variations, mesoscale eddies, and El Niño events correspond to diurnal, seasonal, and interannual fluctuations in environmental factors. To explore hidden spatiotemporal multiple periodic behaviors in noisy ocean data, we propose a novel trajectory clustering method, namely DTID-STFC. It first identifies dense time intervals (DTIs) in which trajectories occur frequently. Subsequently, within each DTI, it utilizes spectral embedding to project trajectories onto a latent subspace and proposes three-way fuzzy clustering to obtain results. We evaluate the proposed method on simulated datasets and compare it with traditional and state-of-the-art trajectory clustering approaches. Experimental results indicate that it outperforms other methods across all five metrics. Moreover, when applying the DTID-STFC method to the analysis of mesoscale cyclonic eddies in the South China Sea and vessel data, it demonstrates more discernible results than traditional methods, and it aligns well with physical oceanographic processes. This proposed method offers valuable insights into identifying periodic behaviors from complex and noisy spatiotemporal oceanic trajectory data. Full article

The present work aims to design a robust method to detect and certify the deterministic chaos or ergodic process for the forced torsional vibrations (FTV) of a double tripod industrial driveshaft (DTID) in transition through the principal parametric resonance region (PPRR) which is considered by the researchers in the field as one of the most important resonance regions for the systems having parametric excitations. The DTID’s model for FTV considers the following effects: nonuniformities of inertial characteristics of the DTID’s elements, the harmonic torque excitation induced by the asynchronous electrical motor used for a heavy-duty grain mill, and the harmonic reaction torque generated by different granulation of the substance needed to be milled. Based on these aspects, a model of the FTV for the DTID was designed which was a modified, physically consistent model already used by the authors to investigate the FTV of automotive driveshafts (homokinetic transmission). For the DTID elements, the dynamic instability for nonstationary FTV in the PPRR using time–history analysis (THA) was analyzed—THA represents the phase portraits. Time–history analysis is a detection method for possible chaotic dynamic behavior for the nonstationary FTV (NFTV) in transition through PPRR. If this dynamic behavior was seen, a new robust method LEA–PM was created to certify and confirm the deterministic chaos for the NFTV of DTID. The new method, LEA–PM, is composed of the Lyapunov exponent’s approach (LEA) coupled with the Poincaré Map (PM) applied to the global system of differential equations that describe the FTV of DTID in the PPRR. This new robust method, which embeds LEA and PM, LEA–PM, establishes if the mechanical system has a deterministic chaotic dynamic behavior (strange attractor) or an ergodic dynamic process in this resonant region. LEA represents a new method that includes not only the maximal Lyapunov exponent method (MLEM) but also new mathematical criteria that is “the sum of all Lyapunov exponents has to be negative” which, coupled with MLEM, indicates the presence of deterministic chaos (strange attractors). THA–LEA–PM had been used for the NFTV of DTID computing the phase portraits, the Lyapunov exponents, and representing the Poincaré Maps of the NFTV for the DTID’s elements in transition through PPRR, founding deterministic chaos or ergodic dynamic behavior. Based on the obtained results, numerical simulations revealed the pitting manifestations of the DTID’s elements, typical for the geared systems transmission, mentioned recently in experimental data research for the homokinetic transmissions. Using the new robust method, THA–LEA–PM (time–history analysis coupled with LEA–PM) can be used in future research for chaotic dynamic analysis of DTID’s NFTV transition through superharmonic resonances, subharmonic resonances, combination resonances, and internal resonances. Time–history analysis as a detection method for chaos and LEA–PM as a certifying method for deterministic chaos can be integrated as a design tool for DTID’s FTV control of the homokinetic transmission. Full article

Objectives: In idiopathic pulmonary fibrosis (IPF), alterations in the pulmonary surfactant system result in an increased alveolar surface tension and favor repetitive alveolar collapse. This study aimed to assess the usefulness of electrical impedance tomography (EIT) in characterization of regional ventilation in IPF. Materials and methods: We investigated 17 patients with IPF and 15 healthy controls from the University of Giessen and Marburg Lung Center (UGMLC), Germany, for differences in the following EIT parameters: distribution of ventilation (TID), global inhomogeneity index (GI), regional impedance differences through the delta of end-expiratory lung impedance (dEELI), differences in surface of ventilated area (SURF), as well as center of ventilation (CG) and intratidal gas distribution (ITV). These parameters were assessed under spontaneous breathing and following a predefined escalation protocol of the positive end-expiratory pressure (PEEP), applied through a face mask by an intensive care respirator (EVITA, Draeger, Germany). Results: Individual slopes of dEELI over the PEEP increment protocol were found to be highly significantly increased in both groups (p < 0.001) but were not found to be significantly different between groups. Similarly, dTID slopes were increasing in response to PEEP, but this did not reach statistical significance within or between groups. Individual breathing patterns were very heterogeneous. There were no relevant differences of SURF, GI or CGVD over the PEEP escalation range. A correlation of dEELI to FVC, BMI, age, or weight did not forward significant results. Conclusions: In this study, we did see a significant increase in dEELI and a non-significant increase in dTID in IPF patients as well as in healthy controls in response to an increase of PEEP under spontaneous breathing. We propose the combined measurements of EIT and lung function to assess regional lung ventilation in spontaneously breathing subjects. Full article

Interferon-mediated host factors myxovirus (Mx) proteins are key features in regulating influenza A virus (IAV) infections. Viral polymerases are essential for viral replication. The Mx1 protein has been known to interact with viral nucleoprotein (NP) and PB2, resulting in the influence of polymerase activity and providing interspecies restriction. The equine influenza virus has evolved as an independent lineage to influenza viruses from other species. We estimated the differences in antiviral activities between human MxA (huMxA) and equine Mx1 (eqMx1) against a broad range of IAV strains. We found that huMxA has antiviral potential against IAV strains from non-human species, whereas eqMx1 could only inhibit the polymerase activity of non-equine species. Here, we demonstrated that NP is the main target of eqMx1. Subsequently, we found adaptive mutations in the NP of strains A/equine/Jilin/1/1989 (H3N8_JL89) and A/chicken/Zhejiang/DTID-ZJU01/2013 (H7N9_ZJ13) that confer eqMx1 resistance and sensitivity respectively. A substantial reduction in Mx1 resistance was observed for the two mutations G34S and H52N in H3N8_JL89 NP. Thus, eqMx1 is an important dynamic force in IAV nucleoprotein evolution. We, therefore, suggest that the amino acids responsible for Mx1 resistance should be regarded as a robust indicator for the pandemic potential of lately evolving IAVs. Full article