Hybrid Method For Detection Research Articles

Use of high-resolution fluorescence in situ hybridization for fast and robust detection of SARS-CoV-2 RNAs

Early, rapid, and accurate diagnostic tests play critical roles not only in the identification/management of individuals infected by SARS-CoV-2, but also in fast and effective public health surveillance, containment, and response. Our aim has been to develop a fast and robust fluorescence in situ hybridization (FISH) detection method for detecting SARS-CoV-2 RNAs by using an HEK 293 T cell culture model. At various times after being transfected with SARS-CoV-2 E and N plasmids, HEK 293 T cells were fixed and then hybridized with ATTO-labeled short DNA probes (about 20 nt). At 4 h, 12 h, and 24 h after transfection, SARS-CoV-2 E and N mRNAs were clearly revealed as solid granular staining inside HEK 293 T cells at all time points. Hybridization time was also reduced to 1 h for faster detection, and the test was completed within 3 h with excellent results. In addition, we have successfully detected 3 mRNAs (E mRNA, N mRNA, and ORF1a (−) RNA) simultaneously inside the buccal cells of COVID-19 patients. Our high-resolution RNA FISH might significantly increase the accuracy and efficiency of SARS-CoV-2 detection, while significantly reducing test time. The method can be conducted on smears containing cells (e.g., from nasopharyngeal, oropharyngeal, or buccal swabs) or smears without cells (e.g., from sputum, saliva, or drinking water/wastewater) for detecting various types of RNA viruses and even DNA viruses at different timepoints of infection.

Hybrid Detection Method for Multi-Intent Recognition in Air–Ground Communication Text

Hybrid Detection Method for Multi-Intent Recognition in Air–Ground Communication Text

Application of GRIN-lens-based in-line digital holographic microscopy to automatic detection and localization of particles released from a MEMS device.

Recently, we developed a compact and easy-to-implement in-line digital holographic microscope (DHM) using a GRIN rod lens, which provides better resolution (1.3µm) compared with commonly used pinhole-based DHM setups. Here, we employ this microscope to acquire 3D holographically reconstructed images of silica microparticles, within the 10-300µm size range, launched/released from a microelectro-mechanical systems (MEMS) device. To the best of our knowledge, this is the first time that a MEMS device is implemented to store and launch microparticles. The custom-designed MEMS device consists of a 50µm thick flat circular silicon ultrasonic membrane mounted on an off-the-shelf piezoelectric transducer. Moreover, we propose and experimentally demonstrate a new automatic hybrid detection and localization method for particle field holography that benefits from a combination of well-known minimum intensity and variance of gray level distribution focus metrics. This robust method is fast and provides precise d e p t h/z position of particles. The proposed method is applied for particle testing of the MEMS device, reconstructing 3D visualization, and measuring the size and velocity of released particles. The obtained experimental results show that the velocity of released particles, previously dry-loaded onto the MEMS device, is of the order of a few tens of cm/s.

A Hybrid Malware Vulnerability using Deep Learning Technique

A Hybrid Malware Vulnerability using Deep Learning Technique

Signal Processing Application Based on a Hybrid Wavelet Transform to Fault Detection and Identification in Power System

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

In-situ detection scheme for EGFR gene with temperature and pH compensation using a triple-channel optical fiber biosensor

An advanced multi-parameter optical fiber sensing technology for EGFR gene detection based on DNA hybridization technology is demonstrated in this paper. For traditional DNA hybridization detection methods, temperature and pH compensation can not be realized or need multiple sensor probes. However, the multi-parameter detection technology we proposed can simultaneously detect complementary DNA, temperature and pH based on a single optical fiber probe. In this scheme, three optical signals including dual surface plasmon resonance signal (SPR) and Mach-Zehnder interference signal (MZI) are excited by binding the probe DNA sequence and pH-sensitive material with the optical fiber sensor. The paper proposes the first research to achieve simultaneous excitation of dual SPR signal and Mach-Zehnder interference signal in a single fiber and used for three-parameter detection. Three optical signals have different sensitivities to the three variables. From a mathematical point of view, the unique solutions of exon-20 concentration, temperature and pH can be obtained by analyzing the three optical signals. The experimental results show that the exon-20 sensitivity of the sensor can reach 0.07 nm nM−1, and the limit of detection is 3.27 nM. The designed sensor gives a fast response, high sensitivity, and low detection limit, which is important for the field of DNA hybridization research and for solving the problems of biosensor susceptibility to temperature and pH.

Intrusion, anomaly, and attack detection in smart vehicles

With the advancement of technology and Internet penetration in all aspects of life in today's modern world, smart vehicles, particularly connected vehicles, are rising. As a result, as smart vehicles have evolved and grown in popularity, so have the cyber threats that threaten them. Vehicles may communicate with other vehicles, road infrastructure, and other smart devices; as a result, the security of related equipment must be ensured. Researchers presented many articles investigating smart vehicle intrusion, anomaly, and attack detection methods. In most of these, researchers only examined the proposed detection methods on in-vehicle networks (IVNs) and external networks for smart ground vehicles. However, reviewing and analyzing the detection methods provided in all communications and connections related to smart ground/air vehicles is necessary. This paper examines the detection methods for in-vehicle networks, inter-vehicle networks, ground vehicle power stations, and the Internet of Drones (IoD). Besides, it analyzes recently published studies in the four categories: intrusion detection systems (IDSs), anomaly detection, attack detection, and hybrid detection methods in smart vehicles. The methods examined in this survey were published in 2018–2022 in reputable journals such as Elsevier, IEEE, Springer, ScienceDirect, etc. We examine datasets, simulations/implementations, and key evaluation criteria for each detection method studied. Also, we compare these methods using appropriate tables and diagrams and analyze the problems in detecting intrusions, anomalies, and attacks in smart vehicles. Finally, we address the research questions and discuss future research challenges.

Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach

Existing web-based security applications have failed in many situations due to the great intelligence of attackers. Among web applications, Cross-Site Scripting (XSS) is one of the dangerous assaults experienced while modifying an organization's or user's information. To avoid these security challenges, this article proposes a novel, all-encompassing combination of machine learning (NB, SVM, k-NN) and deep learning (RNN, CNN, LSTM) frameworks for detecting and defending against XSS attacks with high accuracy and efficiency. Based on the representation, a novel idea for merging stacking ensemble with web applications, termed “hybrid stacking”, is proposed. In order to implement the aforementioned methods, four distinct datasets, each of which contains both safe and unsafe content, are considered. The hybrid detection method can adaptively identify the attacks from the URL, and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy, accelerate the training process, and effectively remove the unsafe JScript/JavaScript keywords from the URL. The simulation results show that the proposed hybrid model is more efficient than the existing detection methods. It produces more than 99.5% accurate XSS attack classification results (accuracy, precision, recall, f1_score, and Receiver Operating Characteristic (ROC)) and is highly resistant to XSS attacks. In order to ensure the security of the server's information, the proposed hybrid approach is demonstrated in a real-time environment.

Advanced Intrusion Detection Combining Signature-Based and Behavior-Based Detection Methods

Recently, devices in real-time systems, such as residential facilities, vehicles, factories, and social infrastructure, have been increasingly connected to communication networks. Although these devices provide administrative convenience and enable the development of more sophisticated control systems, critical cybersecurity concerns and challenges remain. In this paper, we propose a hybrid anomaly detection method that combines statistical filtering and a composite autoencoder to effectively detect anomalous behaviors possibly caused by malicious activity in order to mitigate the risk of cyberattacks. We used the SWaT dataset, which was collected from a real water treatment system, to conduct a case study of cyberattacks on industrial control systems to validate the performance of the proposed approach. We then evaluated the performance of the proposed hybrid detection method on a dataset with two time window settings for the composite autoencoder. According to the experimental results, the proposed method improved the precision, recall, and F1-score by up to 0.008, 0.067, and 0.039, respectively, compared to an autoencoder-only approach. Moreover, we evaluated the computational cost of the proposed method in terms of execution time. The execution time of the proposed method was reduced by up to 8.03% compared to that of an autoencoder-only approach. Through the experimental results, we show that the proposed method detected more anomalies than an autoencoder-only detection approach and it also operated significantly faster.

Active and passive hybrid detection method for power CPS false data injection attacks with improved AKF and GRU‐CNN

Influenced by deep penetration of the new generation of information technology, power systems have gradually evolved into highly coupled cyber-physical systems (CPS). Among many possible power CPS network attacks, a false data injection attacks (FDIAs) is the most serious. Taking account of the fact that the existing knowledge-driven detection process for FDIAs has been in a passive detection state for a long time and ignores the advantages of data-driven active capture of features, an active and passive hybrid detection method for power CPS FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper. First, we analyze the shortcomings of the traditional AKF algorithm in terms of filtering divergence and calculation speed. The state estimation algorithm based on non-negative positive-definite adaptive Kalman filter (NDAKF) is improved, and a passive detection method of FDIAs is constructed, with similarity Euclidean distance detection and residual detection at its core. Then, combined with the advantages of gate recurrent unit (GRU) and CNN in terms of temporal memory and feature-expression ability, an active detection method of FDIAs based on a GRU-CNN hybrid neural network is proposed. Finally, the results of joint knowledge-driven and data-driven parallel detection are used to define a mixed fixed-calculation formula, and an active and passive hybrid detection method of FDIAs is established, considering the characteristic constraints of the parallel mode. A simulation system example of power CPS FDIAs verifies the effectiveness and accuracy of the method proposed in this paper.

Hybrid islanding detection method based on the rate of change of frequency and load impedance

Interconnection of distributed generation (DG) in distribution system will result in formation of islands in the event of loss of main supply. This scenario is harmful to the power system, hence quick detection is critical to halt the formation of islands. Among the common passive and active detection methods available, the hybrid detection method is identified as the most reliable method. This paper proposes a new hybrid method using the combination of passive and active technique which is the rate of change of frequency (ROCOF) and load impedance, respectively. The passive method works when the value of ROCOF exceeds the threshold value which is set at 0.3Hz/s. The active method works when it detects low value of ROCOF and immediately inject a pre-specified load into the system to increase the ROCOF value up to its threshold value. Simulation study on different case studies is carried out on distribution test system to evaluate the performance of the proposed method. Results show that this method is effective in detecting any events that could result in islanding.

A new intelligent multilayer framework for insider threat detection

In several earlier studies, machine learning (ML) has been widely used for building insider threat detection systems. However, the selection of the most appropriate ML classification model for insider threats detection remains a challenge. Despite the prominence of ML in the domain of insider threat detection, none of the previous works have utilized ML techniques for building a hybrid solution that can take advantage of the misuse and anomaly insider threat detection. In this study, a new multilayer framework has been proposed for insider threat detection. The first layer of the framework is used for selecting the best insider threat detection classification model among many based on the multi-criteria decision making techniques. The selection procedure has been developed based on the integration of the entropy-VIKOR methods. For the second layer, a hybrid insider threat detection method has been proposed, where the Misuse Insider Threat Detection (MITD) model has been created using the random forest algorithm. Subsequently, using the K-Nearest Neighbors algorithm, an anomaly insider threat detection algorithm has been developed. The proposed multilayer framework for insider threat detection has been evaluated by using the CERT r4.2 dataset. Results of the experiment demonstrate that the validity of the results produced by the selection framework is proven by the validation procedure obtained from previous research. The proposed hybrid detection method is observed to exhibit an overall accuracy of 99% and a false positive rate of 0.29% for known insider threats, whereas it exhibits 97% accuracy and 2.88% false-positive rate for unknown insider threats.

Research on Data Security Detection Algorithm in IoT Based on K-means

The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method is aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks; the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.

Approaches to quantifying hepatitis B virus covalently closed circular DNA.

Chronic hepatitis B is a major cause of liver disease worldwide and is currently incurable. Hepatitis B virus (HBV) covalently closed circular (ccc) DNA is a key form of the virus responsible for its persistence and is the transcriptional template for all viral transcripts. The field is focussed on methods to clear HBV cccDNA but this been limited by technical difficulties in its quantification due to: identical sequence to other forms of HBV DNA; low copy number per cell; and high resistance to denaturation by heat, leading to difficulty using polymerase chain reaction or hybridization methods for detection. A number of assays have been developed in order to overcome these hurdles either directly or detecting cccDNA levels indirectly via its transcriptional products. In this review, we summarize the approaches to cccDNA quantification that are currently used, and outline key open questions in the cccDNA biology field which remain to be answered due to the limitations of current methods.

A Lightweight Hybrid Detection Method for Botnet

Botnet is a serious threat for the Internet and it has created great damage to the Internet. How to detect botnet has become an ongoing endeavor research. Series of methods have been discussed in recent research. However, one of the remaining challenges is that the high computational overhead. In this paper, a lightweight hybrid botnet detection method is proposed. Considering the features in the botnet data packets and the characteristic of employing DGA (Domain Generation Algorithm) domain names to connect to the botnet, two sensors are designed and deployed individually and parallelly. Signature detection is used on the gateway sensor to dig out known bot software and deep learning based techniques are used on the DNS (Domain Name Server) server sensor to find DGA domain names. With this method, the computational overhead would be shared by the two sensors and experiments are conducted and the results indicate that the method is effective in detecting botnet

A Hybrid Approach for Time-Varying Harmonic and Interharmonic Detection Using Synchrosqueezing Wavelet Transform

With widespread non-linear loads and the increasing penetration of distributed generations in the power system, harmonic pollution has become a great concern. The causes of harmonic pollution not only include the integer harmonics, but also interharmonics, which exacerbate the complexity of harmonic analysis. In addition, the output variability of highly non-linear loads and renewables such as electric arc furnaces and photovoltaic solar or wind generation may lead to weakly time-varying harmonics and interharmonics in both frequency and magnitude. These features present challenges for accurate assessment of associated power-quality (PQ) disturbances. To tackle such increasing time-varying PQ problems, a hybrid detection method using synchrosqueezing wavelet transform (SSWT) is proposed. The proposed method first obtains the proper parameter values for the mother wavelet according to numerical computations. The wavelet transform-based synchrosqueezing and a clustering method are applied to determine each frequency component of the waveform under assessment. The time-domain waveform and the associated magnitude of each frequency component is then reconstructed by the inverse SSWT operation. The novelty of the proposed method is that it can decompose the measured waveform containing both harmonics and interharmonics into intrinsic mode functions without the need for fundamental frequency detection. Compared to other time–frequency analysis methods, SSWT has better anti-noise and higher resolution of time–frequency curves; even the measured signal has close frequency components. Simulation results and actual measurement validations show that the proposed method is effective and relatively accurate in time-varying harmonic and interharmonic detection and is suitable for applications in power networks and microgrids that have high penetration of renewables or non-linear loads causing time-varying voltage or current waveforms.

Hybrid Method for Detection and Classification of Paddy leaf Deficiency Using Modified K-Means Image Segmentation

Hybrid Method for Detection and Classification of Paddy leaf Deficiency Using Modified K-Means Image Segmentation

Ground Fault Detection Using Hybrid Method in IT System LVDC Microgrid

Low voltage direct current (LVDC) microgrid systems have many advantages over low voltage alternating current (LVAC) systems. Furthermore, LVDC microgrids are growing in use because they are easy to link to distributed energy resources (DER) and energy storage systems (ESS), etc. Currently, IT system LVDC microgrids are widely used in direct current (DC) railways, hospitals, photovoltaic (PV) systems, and so on. When a ground fault occurs in an IT system LVDC microgrid, the ground fault may not be detected because the fault current is very small and there is no current path. In this paper, ground fault detection is proposed using a hybrid method that comprises pulsation signal generator injection and detailed coefficients of discrete wavelet transform (DWT). The LVDC microgrid was modeled and simulated using power systems computer-aided design (PSCAD). In addition, the proposed hybrid method was implemented using MATLAB’s wave menu, a script m-file, and the PSCAD library. The proper threshold was selected and tested by fault resistance change and load variation. In order to verify the superiority of the proposed hybrid method, a comparative study with the conventional method was performed. The results of various simulations show that the proposed hybrid detection method has normal operation and accurately and rapidly detects ground faults.

Human Papillomavirus E6/E7 mRNA detection by in situ hybridization in oral cavity squamous cell carcinoma

ObjectiveThe aim of this study was to evaluate the application of in situ hybridization using E6/E7 mRNA probes to identify the frequency of high-risk HPV transcriptionally active and the use of HPV status as a prognostic biomarker in oral cavity squamous cell carcinoma (OCSCC). MethodsNinety-nine OCSCC samples were evaluated from Hospital Santa Rita de Cassia, Hospital Universitário Cassiano Antônio de Moraes and University Hospitals Coventry and Warwickshire NHS Trust. After tissue microarray construction, the slides were submitted to an in situ hybridization detection method for HPV E6/E7 mRNA. HPV status was designated a binary classification. Multiple logistic regression examined the association of HPV with clinical features and other risk factors, using SPSS® software. For all hypothesis tests, a significance level of p ≤ 0.05 was considered. ResultsHPV frequency in oral squamous cell carcinoma was 8%. There was no association between HPV and clinical variables and between the main prognostic features and known risk factors. There was no difference in the prevalence of HPV for oral cavity squamous cell carcinoma by geography (Brazil vs UK). ConclusionsA low frequency of E6/E7 mRNA by RNA in situ hybridization was found in oral cavity squamous cell carcinoma, which supports the evidence that HPV-driven cancer of the oral cavity is uncommon.

A wavelet-based hybrid islanding detection system applied for distributed generators interconnected to AC microgrids

The modern distribution system widely uses renewable energy sources (RESs) as a distributed generation that introduces some problems like unintentional islanding, protection concerns, and reverse power flow. Islanding or fault occurrences must be detected and treated to ensure system stability. The literature presents several islanding detection methods classified as passive (PDM), active (ADM), or hybrid detection methods (HDM). HDMs present advantages of both PDM and ADM methods and overcome their inherent drawbacks. This paper proposes a modification on the CWT for accomplishing its real-time implementation (RT-CWT), and the use of its nonstationary signal analysis for implementing a wavelet-based hybrid islanding detection scheme (WB-HIDS). The evaluation of some power quality-related indices: power grid voltage, frequency deviation, grid impedance, and power angle enables the detection of islanding condition or fault occurrence. The simultaneous use of various indices mitigates the non-detection zone related to the PDM. Also, the use of a transient detection scheme for triggering the signal injection implementation minimizes undesired disturbances introduced by ADM schemes. Experimental results obtained from an ac microgrid laboratory setup validate the proposed solution and demonstrate its effectiveness.