Comparative Analysis of Machine Learning Algorithms for Cross-Site Scripting (XSS) Attack Detection

The relentless growth of data, accelerated since the industrial revolution and now amplified by the digital age, presents unprecedented opportunities and challenges for the healthcare industry. As the global datasphere is projected to expand from 33 zettabytes to 175 zettabytes between 2018 and 2025, leveraging this data through advanced machine learning (ML) algorithms has become crucial, especially in the wake of the COVID-19 pandemic. This study explores the integration of ML and data analytics in healthcare, demonstrating their potential to revolutionize patient care, disease diagnosis, treatment personalization, administrative efficiency, and drug development. By utilizing various ML algorithms, including Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, on a diverse dataset, this research evaluates their performance in predicting health outcomes and improving healthcare services. Random Forest and KNN algorithms showed superior performance in accuracy and reliability, highlighting the importance of selecting appropriate models based on dataset characteristics. The findings underscore the transformative potential of ML and data analytics in healthcare, emphasizing the need for robust datasets, ethical considerations, and data security to maximize their benefits. This integration promises to enhance proactive care, optimize resource allocation, and personalize medical treatments, ultimately leading to improved patient outcomes and operational efficiencies.

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.

Under-five mortality remains a global health challenge with the rates of 43 deaths per every 1000 live births in Tanzania and 37 deaths per every 1000 live births globally. Although child mortality has significantly declined in the last twenty years, the current rates are far from reaching the anticipated Sustainable Development Goal of atmost 25 deaths per 1000 live births in 2030. This study intended to find the best performing classifier of under-five mortality status by comparing ten supervised machine learning algorithms. These machine learning algorithms are Decision Trees, Random Forest, Support Vector Machines, SMOTE-Based Boosted Random Forest, XGBoost, LightGBM, CatBoost, Logistic Regression, K-Nearest Neighbors and Stacked Ensemble Methods. The class imbalance of the dataset detected in the pre-processing stage was addressed using weighted categorical cross-entropy and SMOTE with a 5-folds cross validation and data splitting ratio of 80% for training set and 20% for testing set. With 20 experiments for each of the nine algorithms, the average results were reported to ensure that the findings were not by chance. Further, the stacking ensemble model was developed integrating six of the best performing algorithms using an inclusion criterion of AUC > 0.97. The findings revealed that ensemble algorithm consistently outperformed the other nine algorithms by achieving 100%, 100%, 99.97% and 99.24% for AUC, Accuracy, F1-Score and MCC respectively. This implies that stacking ensemble can uncover more insights than the individual algorithms in predicting under-five mortality status. This study recommends designing policies on under-five mortality that integrate insights from the stacking ensemble algorithm which shows the highest predictive performance.

SummaryScale precipitation in petroleum equipment is known as an important problem that causes damages in injection and production wells. Scale precipitation causes equipment corrosion and flow restriction and consequently a reduction in oil production. Due to this fact, the prediction of scale precipitation has vital importance among petroleum engineers. In the current work, different intelligent models, including the decision tree, random forest (RF), artificial neural network (ANN), K-nearest neighbors (KNN), convolutional neural network (CNN), support vector machine (SVM), ensemble learning, logistic regression, Naïve Bayes, and adaptive boosting (AdaBoost), are used to estimate scale formation as a function of pH and ionic compositions. Also, a sensitivity analysis is done to determine the most influential parameters on scale formation. The novelty of this work is to compare the performance of 10 different machine learning algorithms at modeling an extremely non-linear relationship between the inputs and the outputs in scale precipitation prediction. After determining the best models, they can be used to determine scale formation by manipulating the concentration of a variable in accordance with the result of the sensitivity analysis. Different classification metrics, including the accuracy, precision, F1-score, and recall, were used to compare the performance of the mentioned models. Results in the testing phase showed that the KNN and ensemble learning were the most accurate tools based on all performance metrics of solving the classification of scale/no-scale problem. As the output had an extremely non-linear behavior in terms of the inputs, an instance-based learning algorithm such as the KNN best suited the classification task in this study. This argumentation was backed by the classification results. Furthermore, the SVM, Naïve Bayes, and logistic regression performance metrics were not satisfactory in the prediction of scale formation. Note that the hyperparameters of the models were found by grid search and random search approaches. Finally, the sensitivity analysis showed that the variations in the concentration of Ca had the highest impact on scale precipitation.

Dynamic web pages are widely used by web applications to provide a better user experience and to attract more web users. The web applications use client-side and server-side scripts to provide dynamic behavior to the web pages. Cross-Site Scripting (XSS) attack uses malicious scripts and links injected into the trusted webpages to steal sensitive data from the victims. In this work, the experimental results obtained using machine learning algorithms (Random Forest Classifier, MLP Classifier, Gen ELM Classifier, Naïve Bayes, Support Vector Machine, and K Neighbours Classifier) for the prediction of XSS attack and phishing websites attacks are implemented. This is done using the features based on normal and malicious URLs and JavaScript. Random Forest Classifier gave better results than MLP Classifier, Gen ELM Classifier, Naïve Bayes, Support Vector Machine, and K Neighbours Classifier based on the features extracted from URL and JavaScript code. All the algorithms gave comparatively better results with discretized attributes, but a noticeable difference in performance was seen only in the case of SVM. A detailed literature survey is presented and proposes a new approach to detect phishing websites by features extraction and machine learning algorithm.

Breast cancer is the most common cancer among women (43.3 incidents per 100.000 women), with the highest mortality (14.3 incidents per 100.000 women). Early detection is critical for survival. Using machine learning approaches, the problem can be effectively classified, predicted, and analyzed. In this study, we compared eight machine learning algorithms: Gaussian Naïve Bayes (GNB), k-Nearest Neighbors (K-NN), Support Vector Machine(SVM), Random Forest (RF), AdaBoost, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). The experiment is conducted using Breast Cancer Wisconsin datasets, confusion matrix, and 5-folds cross-validation. Experimental results showed that XGBoost provides the best performance. XGBoost obtained accuracy (97,19%), recall (96,75%), precision (97,28%), F1-score (96,99%), and AUC (99,61%). Our result showed that XGBoost is the most effective method to predict breast cancer in the Breast Cancer Wisconsin dataset.

Artificial Intelligence (AI) has gained enormous prominence for recent years and is becoming a core technology in various fields. The use of AI is becoming increasingly prevalent in medical diagnosis. Many algorithms have been developed to assist the physicians and doctors in their clinical screening work. In this study, a comparative analysis of six widely used machine learning algorithms [Support Vector Machine (SVM), Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), Neural network, Decision Tree] has been done to find the most suitable model for the classification of oral cancer. The effectiveness of the ML methods are compared and evaluated in terms of accuracy, precision and recall using histopathological images of oral cancer. The experimental results shows that, Neural Network outperformed the other models by giving accuracy of 90.4%. Thus, this model can be used as a tool for the screening of binary classification of oral histopathological images.KeywordsMachine learningOral cancerHistopathologyImage embedding

Close-range technologies capable of capturing forest ecosystems in three-dimensional space with great detail are revolutionising precision forestry research and practice, mainly by increasing the level of automation for data collection and processing. Furthermore, they provide options to measure some parameters directly, for example, volume or biomass. However, automatic tree species recognition still needs to be properly solved, which is a crucial and challenging task. A couple of approaches by different authors were done to overcome the challenge when data from close-range technologies are used. The authors mainly utilised 3D structures of whole trees or, in some cases, bark structures using point clouds. Or derived 2D blueprints of whole trees from point clouds to distinguish between tree species. In our approach, we are using images of bark. Usually, images are taken during the data acquisition by close-range technologies as a resource for photogrammetry or for colourising the point clouds in the case of terrestrial laser scanning, for example. Carpentier et al. (2018) did an experiment with 23 tree species in Canada and used convolutional neural networks to classify tree species with an accuracy of almost 94%. We focused on benchmarking multiple machine learning and deep learning algorithms in our experiment. Namely: Random forest; Decision tree; Support Vector Machine; Gradient boost; K-nearest Neighbors; Gaussian Naïve Bayes; Multilayer Perceptron; Convolutional neural networks.In our first experiment, we collected two datasets of bark images using Sony alfa 7 and Canon EOS 4000D. We have collected 1755 images in Slovakia (1369) and Czechia (386); both datasets contain four tree species. The four species from Slovak datasets are European beech, sessile oak, Norway spruce, and European silver fir. Czechia data consists of the species European beech, large-leaved linden, Norway maple, and Scots pine. However, the bark images from Slovakia are from managed forests, and there is a variety of markings on bark; for that, images are cropped to small regions excluding the markings.The most accurate results were achieved by CNN, which provides 94% accuracy on Slovak exact cropped dataset with a 50% dropout and 91% on an exact cropped dataset with a 50% dropout. When CNN is not considered, the most accurate algorithm was Multilayer perceptron with an accuracy of 92%.The following research will focus on implementing such tree species classification within the point cloud processing workflow when close-range technologies are used. Secondly, Carpentier et al. (2018) created Barknet 1.0, where they stored 23,000 high-resolution bark images of 23 tree species in Canada. Our next goal is to develop a database of tree species across Europe. To achieve such a challenging task, we will do it within the 3DForEcoTech COST Action, a European collaborative project focusing on close-range technologies and their implementation for precision forestry and forest ecology.ReferencesCarpentier, M., Giguere, P. and Gaudreault, J., 2018, October. Tree species identification from bark images using convolutional neural networks. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1075-1081). IEEE.

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Detecting the presence of a disease requires laboratory tests, testing kits, and devices; however, these were not always available on hand. This study proposes a new approach in disease detection using machine learning algorithms by analyzing symptoms experienced by a person without requiring laboratory tests. Six supervised machine learning algorithms such as J48 decision tree, random forest, support vector machine, k-nearest neighbors, naïve Bayes algorithms, and artificial neural networks were applied in the “COVID-19 Symptoms and Presence Dataset” from Kaggle. Through hyperparameter optimization and 10-fold cross validation, we attained the highest possible performance of each algorithm. A comparative analysis was performed according to accuracy, sensitivity, specificity, and area under the ROC curve. Results show that random forest, support vector machine, k-nearest neighbors, and artificial neural networks outweighed other algorithms by attaining 98.84% accuracy, 100% sensitivity, 98.79% specificity, and 98.84% area under the ROC curve. Finally, we developed a web application that will allow users to select symptoms currently being experienced, and use it to predict the presence of COVID-19 through the developed prediction model. Based on this mechanism, the proposed method can effectively predict the presence or absence of COVID-19 in a person immediately without using laboratory tests, kits, and devices in a real-time manner.

A Distributed Denial of Service (DDoS) attack poses a serious threat to network security and can disrupt online services by overwhelming the target server with excessive traffic. Effective detection of DDoS attacks requires a system capable of identifying anomalies in network traffic. In this context, Machine Learning (ML) offers an effective approach for classification and anomaly detection. However, different ML algorithms have varying strengths and weaknesses when processing large and complex network data. Therefore, this study aims to evaluate the performance of three ML algorithms: Support Vector Machine (SVM), Random Forest Classifier (RFC), and K-Nearest Neighbors (KNN) in detecting DDoS anomalies. The dataset used consists of 225,745 data points with 85 attributes that describe various characteristics of network traffic, such as destination port, flow duration, packet count, and packet size. This dataset is classified into two classes, BENIGN and DDoS, representing normal traffic and DDoS attacks, respectively. Evaluation is performed using several performance metrics, including accuracy, precision, recall, MCC (Matthews Correlation Coefficient), F-Measure, ROC Area, PRC Area, True Positive Rate (TPR), and False Positive Rate (FPR). The results show that the Random Forest Classifier (RFC) delivers the best performance with an accuracy of 99.99%, precision of 99.98%, recall of 100%, and a very low FPR of 0.02%. This is followed by the Support Vector Machine (SVM) with an accuracy of 99.91%, and the K-Nearest Neighbor (KNN) with an accuracy of 99.98%. All three algorithms demonstrate strong performance in detecting DDoS anomalies, with RFC slightly outperforming others in terms of consistency and higher classification capability. The findings of this study provide valuable insights for selecting the best algorithm to detect DDoS attacks in networks.

In this study, our aim is to predict sex using machine learning (ML) algorithms with morphometric variables taken from the vidian canal. The study was performed on Computed Tomography (CT) images of 137 women and 137 men aged 18–65 years. Seventeen morphometric variables were determined and recorded from axial and coronal skull images. These numerical data were used for sex determination with ML algorithms. As a result of the ML algorithm analysis of the data as 80% training set and 20% test set, the highest Accuracy (Acc.) rate was 0.89 with the Extra Tree Classifier (ETC) algorithm, and the lowest was 0.78 with Quadratic Discriminant Analysis (QDA). Among the parameters, the distance between the optic canal and the vidian canal was found to have the highest effect on sex determination with the Shapley Additive Explanations analyser. As a result of 10-fold cross-validation analysis of ML algorithms, the highest Acc rate was 0.82 with Random Forest (RF) algorithm, and the lowest was 0.73 with QDA. The findings of the study reveal that morphometric variables from the vidian canal show high accuracy in sex determination.

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.