CAXF-LCCDE: An Enhanced Feature Extraction and Ensemble Learning Model for XSS Detection

Prediction and variable importance analysis for small-strain stiffness of soil based on ensemble learning with Bayesian optimization

The right security solutions in the information and communication world are critical to network security by providing real-time network protection against network vulnerabilities and data usage. An effective intrusion detection strategy is able to use a holistic approach to protect critical systems from unauthorized access or attacks. The paper examines the latest scientific achievements and research related to the analysis of detection of network intrusions using machine learning (ML) methods. The article describes a complex security solution based on machine learning (ML) for network intrusion detection using a complex controlled ML structure and ensemble feature selection methods. In addition, a comparative analysis of several MH models and function selection methods is provided. The article develops a general mechanism for detecting and achieving higher accuracy with a minimum frequency of false positive results (FPR). The paper uses datasets and the results show that the detection model can successfully identify 99.3% of intrusions with the lowest error rate of 0.5%, which shows better performance compared to existing solutions. The article combines the selection of ensemble functions and ensemble machine learning approaches as a detection mechanism in SBB to detect network anomalies. An experimental study was conducted with feature sets obtained from nine feature selection methods, and then these feature sets were combined to obtain the minimum number of features using majority voting. A comparative analysis of sets of functions was carried out. Controlled methods are used, which are more efficient with a balanced data set. To make the training dataset balanced, the data type (benign or attacking) with the minimum number of data instances in that training dataset was first selected. An ensemble feature selection and ensemble classification algorithm is implemented to improve the overall performance of the proposed machine learning model. Prospects for the development of further research are proposed.

The customer review is always beneficial for a company for making its product at the next level. There are thousands of products and their reviews which are impossible to analysis manually. Perspective analysis or sentiment analysis is an automated process which retrieves subjective opinion from the review and categorized as positive and negative. Through which an organization can apprehend that where their invention goes on. Living life with uncomfortable looks has a negative impact on our daily life. So, day by day increasing the use of anti-aging products is appreciable. Major portion of such data are available on Web sites like twitter, Facebook, Linked-In and also from e-commerce sites like Amazon, Flip-kart, and various types of blogs. Hence, this research paper aims to guide a sentimental analysis approach to customer reviews of anti-aging products from Amazon. Classify each review as an affirmative review; otherwise, neglect review by adopting several machine learning strategies. This research builds a model using supervised and ensemble machine learning approaches. The method of ensemble machine learning approach applied in this analysis is voting which altogether seven different classifiers: Gaussian Naive Bayes, logistic regression, random forest, bagging using decision tree algorithm, and boosting using extreme gradient boosting (XGB), SVM, k-nearest neighbor algorithm and give output according to best accuracy. The data have also been analyzed using text blob and VADER and accuracy score also compares with this model. The experiment in this research done using Python and NLTK and have created different scenarios for the evaluation of proposed methodology against the classifiers. The scenarios in feature extraction steps such as by using different types of N-grams and analysis the accuracy for each and every classifier. The result which achieved the highest accuracy in logistic regression for using unigram but for some scenario, voting classifier also gives an optimum output.KeywordsSentiment analysisEnsemble machine learningGaussian Naive BayesBaggingLogistic regressionBoostingk-nearest neighbor

BackgroundThis study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PDM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability.MethodsSixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PDM at 30 and 90 min after occlusion. We used 11 DTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PDM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability.ResultsIn the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PDM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PDM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature.ConclusionsOur study confirmed that PV can be estimated using DTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PDM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings.Relevance statementThe proposed DTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting.Key points• Penumbral volume can be estimated by DTI combined with stack-based ensemble ML.• Mean diffusivity was the most important feature used for predicting penumbral volume.• The proposed approach can be beneficial for patients with kidney dysfunction.Graphical

Hepatitis B virus (HBV) infection remains a public health concern in China, especially among middle-aged and older adults. Accurate prediction models can assist in early detection and targeted interventions. However, predicting HBV infection using population-level data is challenging due to its relatively low prevalence, which creates highly imbalanced datasets that undermine the performance of traditional predictive models. This study aimed to develop a machine learning model to predict HBV infection using an ensemble approach designed to handle class imbalance. Our study conducted a cross-sectional analysis of participants aged 45 and above from the China Health and Retirement Longitudinal Study 2011. To address the class imbalance in HBV infection, the synthetic minority oversampling technique (SMOTE) was employed. We developed machine learning models, including logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), adaptive boosting classifier (AdaBoost), extreme gradient boosting (XGBoost), and stacking ensemble model (SEM), to predict HBV infection and identify predictors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (AP), accuracy, sensitivity, specificity, precision, F1 score, and Brier score. Shapley additive explanation (SHAP) analysis was applied to interpret each predictor’s individual contribution and direction to the model’s output. In the final analysis, 8,954 participants were included, among whom 0.64% self-reported HBV infection, confirming highly imbalanced data. The SEM model achieved balanced performance (AUC: 87.93%, AP: 18.85%, F1: 15.66%, Brier score: 0.03). Machine learning models also identified several new important predictors, including birth season, depression, and knee height. An ensemble machine learning model incorporating techniques to manage data imbalance can accurately predict HBV infection in Chinese middle-aged and older adults. The novel predictors we identified offer new insights into HBV risk in ageing populations and support the development of more targeted prevention strategies.

Protein toxins are defense mechanisms and adaptations found in various organisms and microorganisms, and their use in scientific research as therapeutic candidates is gaining relevance due to their effectiveness and specificity against cellular targets. However, discovering these toxins is time-consuming and expensive. In silico tools, particularly those based on machine learning and deep learning, have emerged as valuable resources to address this challenge. Existing tools primarily focus on binary classification, determining whether a protein is a toxin or not, and occasionally identifying specific types of toxins. For the first time, we propose a novel approach capable of classifying protein toxins into 27 distinct categories based on their mode of action within cells. To accomplish this, we assessed multiple machine learning techniques and found that an ensemble model incorporating the Light Gradient Boosting Machine and Quadratic Discriminant Analysis algorithms exhibited the best performance. During the tenfold cross-validation on the training dataset, our model exhibited notable metrics: 0.840 accuracy, 0.827 F1 score, 0.836 precision, 0.840 sensitivity, and 0.989 AUC. In the testing stage, using an independent dataset, the model achieved 0.846 accuracy, 0.838 F1 score, 0.847 precision, 0.849 sensitivity, and 0.991 AUC. These results present a powerful next-generation tool called MultiToxPred 1.0, accessible through a web application. We believe that MultiToxPred 1.0 has the potential to become an indispensable resource for researchers, facilitating the efficient identification of protein toxins. By leveraging this tool, scientists can accelerate their search for these toxins and advance their understanding of their therapeutic potential.

Stress is an almost basic human instinct, especially in the internet age. People’s health is being endangered by psychological stress. It is critical to diagnose stress early on to provide proactive therapy. In the age of the Internet, social media has an enormous impact on human thinking. It can cause mental health issues such as stress but can also play an important part in detecting it. Advancements in machine learning and natural language processing have enabled information extraction from a huge amount of raw textual data. In this study machine learning approach is used to detect stress in a social media text. Data used in this study is from Reddit. Classical and Ensemble machine learning approaches were used for stress detection. Classical Techniques include Decision Tree, Logistic Regression, support vector machine, Random Forest, and Na ̈ıve Bayes. Ensemble approaches used are boosting, bagging, and voting. The ensemble approach was able to provide better results than all machine learning baselines on the dataset. Also, it was able to outperform all non-transformed-based neural network architectures discussed in the baseline. The best-performing model in this study is Logistic regression with a 76.6% F1 Score.

The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine learning model was introduced which classifies enzymes by their toxin degradation ability. In this model, two different sets of data were used which include enzymes that can catalyze the toxin degradation as a positive dataset and non-toxin-degrading enzymes as a negative dataset. Further, a comparison of multiple classifiers was performed to find the best model and a Random Forest (RF) classifier was selected due to its strong performance. To enhance the accuracy, we combined RF with a Deep Neural Network (DNN), forming an ensemble model which effectively integrated both techniques. This combination achieved 95% precision, surpassing individual models. Our ensemble model not only ensures high prediction accuracy but also reliably differentiates toxin-degrading enzymes from non-degrading ones. This study highlights the power of combining classical machine learning with deep learning to advance prediction. Our model represents a significant step in enzyme classification and serves as a valuable resource for environmental biotechnology, food nutrition, and health applications.

Considering the amount of time we spend on the internet, web pages have evolved over a period of time with rapid progression and momentum. With such advancement, we find ourselves fronting a few hostile ideologies, breaching the security levels of webpages as such. The most hazardous of them all is XSS, known as Cross-Site Scripting, is one of the attacks which frequently occur in website-based applications. Cross-Site Scripting (XSS) attacks happen when malicious data enters a web application through an untrusted source. The spam attacks happen in the form of Wall posts, News feed, Message spam and mostly when a user is open to download content of webpages. This paper investigates the use of machine learning to build classifiers to allow the detection of XSS. Establishing our approach, we target the detection modus operandi of XSS attack via two features: URLs and JavaScript. To predict the level of XSS threat, we will be using four machine learning algorithms (SVM, KNN, Random forest and Logistic Regression). Proposing these classified algorithms, webpages will be branded as malicious or benign. After assessing and calculating the dataset features, we concluded that the Random Forest Classifier performed most accurately with the lowest False Positive Rate of 0.34. This precision will ensure a method much efficient to evaluate threatening XSS for the smooth functioning of the system.

Cross-Site Scripting (XSS) attacks inject malicious code payloads into web application logs, triggering stored cross-site scripting execution when accessing the view-logs interface. The destruction produced by the XSS injection susceptibilities is especially significant since the attacker can steal sensitive data such as the stored user's cookies and tokens or control the host remotely by using remote code execution of XSS. For example, if an attacker manages to obtain the cookies of the website administrator, the whole website can be taken over. In this paper, we develop and evaluate the performance of a machine-learning-based XSS detection system for website applications. Particularly, we investigate using three supervised machine learning: optimizable k-nearest neighbours, optimizable naïve bays, and hybrid (ensemble) learning of decision trees. To validate the system's efficacy, we employed the XSS-Attacks-2019 dataset consisting of modern real-world traffic-subjected types of classes normal (benign) or anomaly (XSS attack). To verify the performance evaluation, we have used several conventional metrics, including the confusion matrix analysis, the detection accuracy, the detection precision, the detection sensitivity, the harmonic detection means, and the detection time. The experimental results demonstrated the predominance of the hybrid learning-based XSS detection system. The best performance indicators peaked at 99.8% (accuracy, precision, and sensitivity) with a very short detection time of 103.1 μSec. Conclusively, the proposed hybrid model outpaced several recent XSS-attacks detection systems in the same study area.

Nowadays online social network (OSN) is one of the most popular internet services in the world. It allows us to communicate with others and share knowledge. However, from the security's point of view, OSN is becoming the favorite target for the attackers, and is under a lot of threats such as cross-site scripting (XSS) attacks. In this paper, we present a novel approach using machine learning to do XSS detection in OSN. Firstly, we leverage a new method to capture identified features from web pages and then establish classification models which can be used in XSS detection. Secondly, we propose a novel method to simulate XSS worm spreading and build our webpage database. Finally, we set up experiments to verify the classification models using our test database. Our experiment results demonstrate that our approach is an effective countermeasure to detect the XSS attack.

Animal conservation is one of the key challenges that the biodiversity crisis is facing. One of the important factors in this crisis is climate change and global warming. Humans are interfering with regions with wildlife causing expendable damage. To curb these challenges and perform animal preservation, monitoring animals is important. To smartly utilize resources online endangered species are monitored. This monitoring is not humanly possible due to limitations to the human focus, but machines can help automate this. In this study, classical and ensemble machine learning approaches are used to identify three endangered species in the Pakistan region: Markhor, Asiatic Black Bear, and Snow Leopard. A dataset is developed, and basic machine learning operations are performed. Logistic regression was the best performing algorithm with 93% accuracy. This research tends to open the door to animal monitoring research in Pakistan.

The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.

To facilitate the detection of various vulnerabilities, there are many different tools (scanners) that can help analyze the security of web applications and facilitate the development of their protection. But these tools for the most part can only identify problems, and they are not capable of fixing them. Therefore, the knowledge of the security developer is a key factor in building a secure Web resource. To resolve application security problems, developers must know all the ways and vectors of various attacks in order to be able to develop various protection mechanisms. This review discusses two of the most dangerous vulnerabilities in the field of Web technologies: SQL injections and XSS attacks (cross-site scripting – XSS), as well as specific cases and examples of their application, as well as various approaches to identifying vulnerabilities in applications and threat prevention. Cross-site scripting as well as SQL-injection attacks are related to validating input data. The mechanisms of these attacks are very similar, but in the XSS attacks the user is the victim, and in the SQL injection attacks, the database server of the Web application. In XSS attacks, malicious content is delivered to users by means of a client-side programming language such as JavaScript, while using SQL injection, the SQL database query language is used. At the same time, XSS attacks, unlike SQL injections, harm only the client side leaving the application server operational. Developers should develop security for both server components and the client part of the web application.

Online Social Networks (OSNs) are continuously suffering from the negative impact of Cross-Site Scripting (XSS) vulnerabilities. This paper describes a novel framework for mitigating XSS attack on OSN-based platforms. It is completely based on the request authentication and view isolation approach. It detects XSS attack through validating string value extracted from the vulnerable checkpoint present in the web page by implementing string examination algorithm with the help of XSS attack vector repository. Any similarity (i.e. string is not validated) indicates the presence of malicious code injected by the attacker and finally it removes the script code to mitigate XSS attack. To assess the defending ability of our designed model, we have tested it on OSN-based web application i.e. Humhub. The experimental results revealed that our model discovers the XSS attack vectors with low false negatives and false positive rate tolerable performance overhead.