Hybridization Detection Research Articles

Safeguarding Iguana diversity: enabling rapid and low-effort tracking of non-native iguanas through terrestrial eDNA innovations

Reptiles have among the highest extinction risk across terrestrial vertebrates, with habitat fragmentation, habitat destruction, and invasive alien species being the primary causes of reptile species loss on a global scale. Invasive hybridization (i.e. hybridization between native and invasive alien species) is increasing globally, causing the extinction of native genotypes, and this phenomenon is particularly pervasive in Caribbean iguanas. The Lesser Antillean Iguana (Iguana delicatissima), a keystone species of Caribbean coastal ecosystems, has become critically endangered mainly due to ongoing hybridization with the invasive Common Green Iguana (I. iguana). For impactful conservation intervention, the need for early detection of invasive animals and their progeny, or detection of surviving pure native animals, is urgent. We aimed to develop a novel environmental DNA (eDNA) toolkit using Kompetitive Allele Specific PCR (KASP) technology, a method of allele-specific amplification for cost-effective and efficient sampling of terrestrial substrates to aid in mapping the distribution of native I. delicatissima, invasive I. iguana, and signal potential invasive hybridization. We demonstrate proof-of-concept and successfully identified I. delicatissima, I. iguana, and their hybrids via blood samples using our primer sets, as well as successful detection of I. delicatissima in several ex-situ (Rotterdam Zoo) and in-situ (St. Eustatius) eDNA samples, collected with environmental swabs and tape-lifting. We found that sampling potential perching spots yielded the highest number of positive detections via environmental swabbing and tape-lifting. Our toolkit demonstrates the potential of terrestrial eDNA sampling for iguana conservation, enabling faster detection of putative invasive hybridization. Additionally, the method holds promise for other terrestrial cryptic species, contributing to broader collection of population-level information.

On Hybrid Detection of Wireless Communications over Interference Channels: A Generalized Framework

On Hybrid Detection of Wireless Communications over Interference Channels: A Generalized Framework

Terahertz Hybrid Detection of Chiral Enantiomers with Intrinsic Low Absorption enabled by Metasurface

Terahertz Hybrid Detection of Chiral Enantiomers with Intrinsic Low Absorption enabled by Metasurface

DDoS Attacks on Cloud Computing and IoT Devices: Strategies for Mitigation

This study has the goal of developing security model that detected and prevented the risks of Distributed Denial of Service (DDoS) attacks in cloud computing systems and IoT devices which are gravely potent and on the increase today, especially with the rapid growth of internet during the last one and a half decades. This is more prevalent because of the several benefits that an organization enjoys after adopting cloud computing and Internet of Things (IoT). However, the harm that may result after a DDoS attack on a cloud computing infrastructure or service and IoT devices can be very huge, and all efforts must be made to secure it. Therefore, the traditional security mechanism cannot satisfy the security requirements of cloud computing and IoT. While several models exist on tackling this menace which operate on a particular layer (mostly layer 3) of the Open System Interconnection (OSI), we have developed a cloud-based hybrid defense and detection mechanism to prevent DDoS attacks in cloud computing environment and IoT that operates in layers 3 (network), 4 (transport) and 7 (application) of the OSI model. This was done using two approaches: analyzing Transmission Control Protocol/Internet Protocol (TCP/IP) header features of incoming packets in cloud computing environment in order to detect and classify spoofed IP address during DDoS attack via a custom-made Web Application Firewall (WAF); and the integration of the cloud resources with Cloudflare. In the first instance, TCP syn flood attacks were targeted to a particular webserver on port 80 through an attacking lab machine. This machine did not have this custom-made WAF (prevention/detection mechanism) against these attacks. There was 100% packet loss as no replies were received, overwhelming the system. The result shows that a total of 1,625,192 packets were transmitted in a short period which were captured and analyzed via Wireshark. Several TCP errors were observed over a very short time interval which indicated successful DDoS attack effectively crashing the system. The result varied when the custom-made WAF was put in place, and the attacking lab machine launched a TCP syn flood attack against the web server on port http port 80. A total of 2,353,585 packets were transmitted in a short period which were captured and analyzed using Wireshark and contained less TCP errors indicating successful mitigation of DDoS attacks. When the resources were hosted online and integrated with Cloudflare, integrity checks were successful before the resources were loaded, indicating complete mitigation of attacks. In the end, an enhanced, cloud-based, hybrid (WAF + Cloudflare) security model that prevented the risks of DDoS attacks on cloud computing and IoT devices was designed and implemented. The methodology adopted for this research work is Open Source Security Testing Methodology Manual (OSSTMM) and programming language used is batch script. The model will be useful in education, healthcare, ecommerce, financial, political/electoral, web hosting providers/ISPs offices, homes and online gaming sites. However, these advancements also make IoT and cloud applications vulnerable to a variety of security threats. To broaden the scope of this research, it is recommended to extend the study to include Man-in-the-Middle (MitM) attacks and routing attacks targeting IoT devices and cloud applications.

Probe-Type High-Sensitivity DNA Hybridization Detection Based on Optical Fiber SPR

Probe-Type High-Sensitivity DNA Hybridization Detection Based on Optical Fiber SPR

IDDLE: A Novel Deep Learning–Based Approach for Intrusion Detection Problem Using Feature Extraction

ABSTRACTThe internet has revolutionized access to information, enabling users to retrieve vast amounts of data effortlessly, transcending geographical and temporal barriers. This unprecedented connectivity has advanced research, education, and communication, but it has also created vulnerabilities that cybercriminals exploit to steal confidential information, interfere with processes, and carry out extensive attacks. Consequently, robust security measures are essential to safeguard the integrity and confidentiality of online data. Intrusion detection systems (IDS) are crucial in defending against such threats, employing both signature‐based and anomaly‐based models. Even though signature‐based IDS are highly effective at countering recognized threats, they struggle with novel, “zero‐day” attacks. Conversely, anomaly‐based detection systems can identify unknown threats but often generate high false positive rates. A hybrid IDS, combining elements of both approaches, offers a more comprehensive defense. This study presents a new intrusion detection model utilizing deep learning, evaluated on the KDD'99 and NSL‐KDD datasets. The proposed model (IDDLE—intrusion detection deep learning engine) incorporates advanced preprocessing techniques, including normalization, feature extraction, and categorical encoding. Empirical findings demonstrate that the proposed model outperforms existing state‐of‐the‐art approaches. This study underscores the capability of deep learning to enhance it in improving IDS performance, emphasizing the importance of continuous innovation and collaboration among security researchers, developers, and users to stay ahead of evolving cyberthreats. The findings underscore the significance of advanced feature extraction and hybrid detection strategies in developing robust intrusion detection systems.

Hybrid Detection Model Combining an Advanced Q-Learning Network (AQN) and Attention-Enriched Transfer Learning Approach for DDoS Detection

Modern network systems are seriously threatened by distributed denial of service (DDoS) attacks, which call for sophisticated mitigation strategies. The proposed Advanced Q-learning network(AQN) [1] with an Attention-Enriched Transfer Learning (AETL) [2] is designed to integrate MixNet, Recurrent Neural Network (RNN), and MobileNetV3 with the aid of insights from an advanced classifier. The comprehensive methodology includes data collection, advanced normalization using Recursive Feature Elimination (RFE)[3], anomaly detection-driven data cleaning, and Deep Packet Inspection (DPI) [4]. To improve feature representation, temporal dependencies are captured using Long Short-Term Memory (LSTM) [5] networks and attention mechanisms Adding an attention mechanism, Q-Learning and transfer learning to improve previously trained models allows the suggested method to concentrate on important network traffic features that indicate possible DDoS attacks. The current analysis shows that low false rate in accuracy, precision, and detection speed in finding the mitigation strategies. The proposed model is tested on various datasets and in real-world network environments to confirm its scalability and resilience.

Symbiosis, hybridization, and speciation in Mediterranean octocorals (Octocorallia, Eunicellidae)

Abstract Understanding how species can form and remain isolated in the marine environment still stimulates active research. Here we study the differentiation and the possibility of hybridization among three temperate octocorals: Eunicella cavolini, Eunicella singularis, and Eunicella verrucosa. Morphologically intermediate individuals have been observed between them. Among these three species, E. singularis is the only one described in mutualistic symbiosis with photosynthetic Symbiodiniaceae. The symbiosis between Symbiodiniaceae and scleractinian corals is well studied, especially in the context of the response to anthropogenic climate change. Nevertheless, the potential role of symbiotic interactions in speciation processes remains unknown in cnidaria. We tested here the possibility of hybridization between symbiotic and non-symbiotic Eunicella species. Through multivariate analyses and hybrid detection, we prove the existence of on-going gene flow between E. singularis and E. cavolini, with the observation of F1 and F2 hybrids, and backcrosses. Demographic inferences indicate a scenario of secondary contact between these two species. Despite current gene flow, these two species appear genetically well differentiated. Our data also suggest an intermediate abundance of Symbiodiniaceae in the hybrids of the two parental populations. We discuss the evolution of the Symbiodiniaceae/cnidarian symbiosis in the light of our results.

XSShield: A novel dataset and lightweight hybrid deep learning model for XSS attack detection

With the proliferation of web applications, cross-site scripting (XSS) attacks have increased significantly and now pose a significant threat to users' information security and privacy. To enhance the efficiency of XSS attack detection, the adoption of machine learning (ML) and deep learning (DL) techniques offers promising solutions, but their effectiveness is limited by the lack of comprehensive and diverse datasets. Moreover, existing approaches often prioritize detection accuracy over real-time processing capabilities, which are essential for effective defense. To address these challenges, in this paper, we propose a novel framework that automatically collects web resources, efficiently extracts informative features, and constructs an up-to-date XSS attack dataset, which is then used to train a machine learning-based XSS detection model. Using this framework, we created and published a well-structured dataset over 100,000 samples for the research community. Furthermore, we present a hybrid detection model that leverages the strengths of both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Extensive evaluations of our dataset demonstrate that the proposed model outperforms other baseline ML models across various metrics, including processing rate. Notably, our model achieves an accuracy of 99.27% while maintaining a low false positive rate of 0.06% and high processing rate of exceeding 1000 samples per second. These results highlight its high accuracy and robustness in detecting XSS, and suitability for real-time applications. Our work presents a comprehensive solution for enhancing web application security by providing a diverse dataset and a high-accuracy detection model with low latency.

Near-infrared SPR biosensor based on photonic crystal fiber for DNA hybridization detection

BackgroundSurface plasmon resonance (SPR) sensing technology has been widely used in biometrics, but the weak detection capability and low sensitivity limit the development of SPR biosensors. In this work, we propose to employ the transition metal disulfide (TMD) material MoS2 to induce the SPR effect into the near-infrared band. The aim of this work is to develop a near-infrared sensor capable of quantitatively detecting the concentration of cDNA, which is able to solve the problems of low sensitivity, parameter crosstalk and so on. ResultsThe results show that the sensitivity of the SPR sensor at infrared wavelengths is 1.69 times higher than that of visible, and the infrared wave is more suitable as an excitation source for the SPR effect because it has a stronger evanescent field. In addition, we have prepared a DNA hybridization sensor that can work in the near-infrared band to detect complementary DNA (cDNA) concentration. Moreover, a combined cascade and parallel strategy realized the temperature and PH detection. The sensor uses the photonic crystal fiber as the platform, silver film as an excitation layer, and MoS2 as a modulation layer. The optimal parameters of the sensor were determined during the experiment, and the three-parameter detection was successfully realized. The experimental results show that the three sensing channels can work independently, and the DNA hybridization probe can achieve selective detection of cDNA with a sensitivity as high as 0.22 nm/(nmol/L). SignificanceThe proposed three-channel DNA hybridization sensor has high sensitivity and accuracy. The innovation of this work is to demonstrate that infrared waves are more suitable to be used as the excitation source of SPR sensors and that the proposed three-channel sensor has a promising future in early diagnosis and biosensing.

IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification

Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample database is getting worse and worse. It is difficult for the traditional identification methods to effectively capture the sample feature information operated by the confusion method only based on the delayed database information. The research into the direction of malware detection is dedicated to surmounting the limitations of conventional detection methodologies, and delves deeply into the application of cutting-edge technologies such as data visualization, machine learning, and hybrid detection within the realm of malware detection. Through these investigations, our goal is to construct a detection system that is both more precise and efficient, capable of addressing the ever-evolving threats to cybersecurity. Pursuing research in this direction is not only vital for enhancing network security defenses and safeguarding user data, but it will also foster the advancement of related state-of-the-art technologies and further mitigate the economic and societal repercussions of malware attacks. In light of this issue, this paper proposes the Image-based Malware Classification with Multi-scale Kernels (IMCMK), a Convolutional Neural Network (CNN) architecture using multi-scale convolution kernels mixing action to improve malware variants detection capabilities. First, we propose the Multi-scale Kernels (MK) block combining deep large kernel convolution and standard small kernel convolution with shortcuts to improve the accuracy. Furthermore, we propose Multi-scale Kernel Fusion (MKF) to reduce the number of parameters that come with the large kernels. The improved Squeeze-and-Excitation (SE) block can obtain the correlation between different channels to further increase the model performance. Experimental results show that IMCMK outperforms the state-of-the-art methods in malware family classification accuracy, which has achieved 99.25 %.

Detection and classification of breast cancer in mammographic images with fine-tuned convolutional neural networks

ABSTRACT Breast cancer is cancer that forms in the cells of the breasts and is a severe health issue that affects many people around the world, especially since it is the most deadly cancer in women. By finding it early and using new treatments, patients can overcome this challenge and get back to a healthier life. This study proposed a procedure to fine-tune the Convolutional Neural Networks (CNN) model with data preprocessing and augmentation in classifying mammogram images called the Hybrid Mammogram Classification and Detection Pipeline (HMCaD). After using CNN for classification because it brings higher confidence in classifying tasks, the YOLOv8 has been applied for localization subtask to detect abnormal positions with predicted bounding boxes. The database is provided by the Mammographic Image Analysis Society (MIAS) and is protected by the United Kingdom. It comprises 330 samples, including 79 benign, 54 malignant, and 207 normal images. As a result, the classification in our model based on the custom EfficientNetB3 model and seam carving technique received a great validation accuracy, test accuracy, and F1 score throughout three scenarios at 96.73%, 97.59%, and 97.58%, respectively. Furthermore, the area under the Receiver Operating Characteristic (ROC) curve also has a surprise result of 0.96 (i.e. AUC = 0.96 ). Moreover, YOLOv8 for detecting abnormal positions in our study achieved 83.22% in Intersection over Union (IoU). This led to the research reaching good results in classifying and detecting breast cancer by considering several performance metrics.

A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (SVM-PSO).

Leukemia, a hematological disease affecting the bone marrow and white blood cells (WBCs), ranks among the top ten causes of mortality worldwide. Delays in decision-making often hinder the timely application of suitable medical treatments. Acute lymphoblastic leukemia (ALL) is one of the primary forms, constituting approximately 25% of childhood cancer cases. However, automated ALL diagnosis is challenging. Recently, machine learning (ML) has emerged as an important tool for building detection models. In this study, we present a hybrid detection model that improves the accuracy of the detection process by combining support vector machine (SVM) and particle swarm optimization (PSO) approaches to automatically identify ALL. We use SVM to represent a two-dimensional image and complete the classification process. PSO is employed to enhance the performance of the SVM model, reducing error rates and enhancing result accuracy. The input images are obtained from two public datasets (ALL-IDB1 and ALL-IDB2), and public online datasets are utilized for training and testing the proposed model. The results indicate that our hybrid SVM-PSO model has high accuracy, outperforming stand-alone algorithms and demonstrating superior performance, an enhanced confusion matrix, and a higher detection rate. This advancement holds promise for enhancing the quality of technical software in the medical field using machine learning.

Modeling and detection of false data injection attacks in cyber-physical distribution system with load aggregator interaction

With the increasing number of users and multi-type loads accessing the cyber–physical distribution system (CPDS), the bidirectional interaction between the system becomes more and more frequent. The Load aggregator (LA) also plays an increasingly important role in the interaction process. However, the LA’s high dependence on information and lack of security measures make it vulnerable to attacks, so this paper analyzes the interactive security of the LAs from two points. Considering the game process between the LAs and users from the attacker’s point of view, this paper puts forward an attack strategy aiming cost function of the LAs, establishes a bi-level multi-objective programming attack model of false data injection attacks(FDIAs), and proposes an attack detection method based on the multi-state matching method and improved similar daily data preprocessing generative adversarial network (P-GAN) from the defender’s point of view to defend against the above attack strategy. Furthermore, a hybrid detection mechanism combining event triggering and periodic detection is proposed to ensure response speed and adaptability of detection. The effectiveness of the proposed attack model and the detection method is verified by simulation analysis.

Cyber-attack Detection and Mitigation Process under Big Data Consideration: Improved Recursive Feature Elimination-based Feature Selection

Due to the rapid growth of network technology, huge volume and distinct data sent via networks is expanding constantly. The situation shows how complex and dense cyber attacks and hazards are developing. Due to the rapid advancement in network density, cyber security specialists find it difficult to monitor all network activity. Due to frequent and sophisticated cyber attacks, it is becoming more challenging to detect and identify abnormalities in network events. The use of deep learning provides a variety of tools and strategies for automated cyber-attack detection as well as quick attack-type prediction as well as evaluation. This work introduces a novel cyber-attack detection and mitigation process under the following phases including preprocessing, feature extraction via the Map Reduce framework that handles the big data, feature selection, attack detection and mitigation. The Improved Normalisation process is achieved on the preprocessing phase. The work is examined from a big data perspective; hence Map Reduce framework is utilised for this. As a result, the framework will manage the feature extraction process, where features including statistical features, raw features, improved correlation-based features, and info gain-based features will be extracted. Following feature extraction, the Improved Recursive Feature Elimination procedure is processed that selects the relevant features. The hybrid detection model, which combines Recurrent Neural Networks (RNN) Deep and Belief Networks (DBN) is used to detect the attacks. Once an attack has been detected, the attacker must be mitigated. To accomplish this, an improved BAIT-based mitigation procedure is used. The two datasets used in this work are, namely, Intrusion Detection Systems (IDS) 2018 Intrusion CSVs (CSE-CIC-IDS2018) and UNSW_NB15. Finally, the suggested model and the alternative methods are contrasted using a variety of measures such as accuracy, sensitivity, specificity, precision, FDR, FNR and FPR.

Retraction of "Highly Sensitive Detection of DNA Hybridization on Commercialized Graphene-Coated Surface Plasmon Resonance Interfaces".

Retraction of "Highly Sensitive Detection of DNA Hybridization on Commercialized Graphene-Coated Surface Plasmon Resonance Interfaces".

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.

Cascade amplification-based triple probe biosensor for high-precision DNA hybridization detection of lung cancer gene

As an essential biomarker for diagnosing and treating various diseases, low-cost, quantitative detection methods for complementary DNA (cDNA) have received much attention. The surface plasmon resonance (SPR) sensing technique is an effective measurement scheme, but the ambient temperature and pH variations have a non-negligible impact. In this work, we developed a triple-probe SPR sensing system for detecting cDNA concentration, temperature, and pH. In order to satisfy the triple parameter measurements, we used a microstructured optical fiber as the sensing platform, silver and gold films as the excitation layer, and a MoS2 film as the modulation layer. First, we explore the modulation mechanism of SPR and the conditions for excitation of triple SPR and demonstrate that the carrier concentration is a crucial factor affecting the resonance wavelength. Then, the feasibility of the sensing system for triple-probing is theoretically analyzed. Finally, in the experiment, the optimal parameters of the sensor were determined, and the triple parameter detection was successfully realized. The experimental results show that the three probes can work independently, and the hybridized DNA probe can realize the selective detection of cDNA with a sensitivity of 0.249 nm/(nmol/l). The maximum sensitivity of the pH probe and the temperature probe are 51.5 nm/pH and 6.14 nm/°C. In addition, the experimental results show that the sensing probes have excellent reproducibility. This paper’s innovation is using the fiber optic SPR effect to achieve quantitative detection for cDNA, temperature detection, and pH detection. Therefore, the sensor has a promising future in early diagnosis and biosensing.

Attention deficit hyperactivity disorder (ADHD) detection for IoT based EEG signal

ADHD is a prevalent childhood behavioral problem. Early ADHD identification is essential towards addressing the disorder and minimizing its negative impact on school, career, relationships, as well as general well-being. The present ADHD diagnosis relies primarily on an emotional assessment which can be readily influenced by clinical expertise and lacks a basis of objective markers. In this paper, an innovative IoT based ADHD detection is proposed using an EEG signal. To the input EEG signal, the min-max normalization technique is processed. Features are extracted as the subsequent step, where improved fuzzy feature, in which the entropy is estimated to increase the effectiveness of recognizing the vector along with, fractal dimension, wavelet transform and non-linear features are extracted. Also, proposes the new hybrid PUDMO algorithm to select the optimal features from the extracted feature set. Subsequently, the selected features are fed to the proposed hybrid detection system that including IDBN and LSTM classifier to detect whether it is ADHD or not. Further, the weights of both classifiers are tuned optimally as per the hybrid PUDMO algorithm to enhance the detection performance. The PUDMO achieved an accuracy of 0.9649 in the best statistical metric, compared to the SLO's 0.8266, SOA's 0.8201, SMA's 0.8060, BRO's 0.8563, DE's 0.8083, POA's 0.8537, and DMOA's 0.8647, respectively. Thus, the assessments and detection help the clinicians to take appropriate decision.

CAPZB mRNA is a novel biomarker for cervical high-grade squamous lesions

This study aimed to evaluate the potential of capping protein (actin filament) muscle Z-line subunit β (CAPZB) messenger ribonucleic acid (mRNA) levels as a biomarker for distinguishing low-grade squamous intraepithelial lesions of the cervix (LSIL) from high-grade squamous intraepithelial lesions of the cervix (HSIL). We collected a total of 166 cervical exfoliated cells and divided them into five groups based on histopathological results. Each sample was divided into two portions, one for fluorescence in situ hybridization (FISH) detection and the other for bisulfite sequencing polymerase chain reaction (BSP) detection. We found that FISH detection of CAPZB mRNA mean fluorescence intensity (MFI) and BSP detection of CAPZB deoxyribonucleic acid (DNA) percentage of methylation rate (PMR) performed as biomarkers for distinguishing HSIL from LSIL, with an area under the receiver operating characteristic curve (AUC), sensitivity, specificity and cut-off value of 0.893, 81.25%, 80.39% and 0.616, 0.794, 64.06%, 81.37% and 0.454, respectively. Furthermore, FISH detection of CAPZB mRNA exhibited a greater AUC (0.893) for the detection of HSIL than the CAPZB DNA methylation method (0.794), indicating the CAPZB mRNA levels can be used as a biomarker for assessing cervical lesions.