A novel phishing website classification method based on hybrid sampling

Phishing websites are a problem for cybersecurity because they pretend to be real online services to get sensitive information from users like login details and financial information. The people who make these phishing websites can. Shut them down quickly which makes it hard for traditional methods to detect them. To solve this problem this paper talks about a system that uses machine learning to detect phishing websites by looking at the website address and domain. This way the system can detect phishing websites without having to look at the webpage. The system uses a set of data with 11,055 website addresses and 31 special features to test how well it works. These features look at things like the structure of the website address, who registered the domain and things that might indicate phishing. The system uses two kinds of classifiers Logistic Regression and Random Forest to test how well it can detect phishing websites. The system also uses an ensemble model that combines the results from both classifiers to make the predictions more reliable. The results show that thisensemble model is really good at detecting phishing websites and can balance being precise and catching all the phishing websites. The system is also good at telling the difference between fake websites. The people who made the system also created a user interface that can analyze website addresses in time which makes it more useful and practical. The system is a solution for detecting phishing websites in the real world and can be used in many different situations. Phishing websites are a problem but this system can help solve it. The system uses machine learning and a special ensemble model to detect phishing websites. It is really good, at it. Phishing websites will continue to be a problem. With this system we can detect them more easily. Keywords: Phishing Website Detection, Machine Learning, URL-Based Feature Extraction, Cybersecurity, URL Classification, Random Forest Classifier, Logistic Regression, XGBoost Algorithm, Fusion Model, Soft Voting Ensemble, Attack Type Identification, Credential Phishing, Financial Fraud Detection, ROC Curve Analysis, AUC PerformanceMetric

The application of supramolecular chemistry on solid surfaces has received extensive attention in the past few decades. To date, combining experiments with quantum mechanical or molecular dynamic methods represents the key strategy to explore the molecular self-assembled structures, which is, however, often laborious. Recently, machine learning (ML) has become one of the most exciting tools in material research, allowing for both efficiency and accuracy in predicting molecular properties. In this work, we constructed a graph neural network to predict the self-assembly of functional polycyclic aromatic hydrocarbons (PAHs) on metal surfaces. Using scanning tunneling microscopy (STM), we characterized the self-assembled nanostructures of a homologous series of PAH molecules on different metal surfaces to construct an experimental data set for model training. Compared with traditional ML algorithms, our model exhibits better predictive performance. Finally, the generalization of the model is further verified by comparing the ML predictions and experimental results of different functionalized molecule. Our results demonstrate training experimental data sets to produce a predictive ML model of molecular self-assembly with generalization performance, which allows for the predictive design of nanostructures with functional molecules.

Social engineering attacks pose a major threat to an internet user’s sensitive information, such as credit card information and passwords. One of the most common of these attacks are phishing websites. These websites appear to be legitimate in hopes that a user will unknowingly input their sensitive information to the malicious website. This paper attempts to identify and characterize the coding style of phishing websites using machine learning models. We used web scraping to extract the HTML content of around 29,000 phishing websites. The phishing websites were collected from PhishTank, which publicly tracks such websites. To compare the HTML coding styles and syntax in phishing websites and legitimate websites, we used a dataset of around 36,000 legitimate websites. We eliminated websites with missing basic content. From the cleaned datasets of phishing and legitimate websites, we processed 10,800 websites’ source codes (5,400 websites per category), extracting 11 features from every website’s content. Our Random Forest model achieved the best accuracy of 94.16% in detecting phishing websites.

Due to their damage to Internet security, malware and phishing website detection has been the Internet security topics that are of great interests. Compared with malware attacks, phishing website fraud is a relatively new Internet crime. However, they share some common properties: 1) both malware samples and phishing websites are created at a rate of thousands per day driven by economic benefits; and 2) phishing websites represented by the term frequencies of the webpage content share similar characteristics with malware samples represented by the instruction frequencies of the program. Over the past few years, many clustering techniques have been employed for automatic malware and phishing website detection. In these techniques, the detection process is generally divided into two steps: 1) feature extraction, where representative features are extracted to capture the characteristics of the file samples or the websites; and 2) categorization, where intelligent techniques are used to automatically group the file samples or websites into different classes based on computational analysis of the feature representations. However, few have been applied in real industry products. In this paper, we develop an automatic categorization system to automatically group phishing websites or malware samples using a cluster ensemble by aggregating the clustering solutions that are generated by different base clustering algorithms. We propose a principled cluster ensemble framework to combine individual clustering solutions that are based on the consensus partition, which can not only be applied for malware categorization, but also for phishing website clustering. In addition, the domain knowledge in the form of sample-level/website-level constraints can be naturally incorporated into the ensemble framework. The case studies on large and real daily phishing websites and malware collection from the Kingsoft Internet Security Laboratory demonstrate the effectiveness and efficiency of our proposed method.

Current vegetation indices and biophysical parameters derived from optical satellite data for forest monitoring are widely used in various applications but can be limited by atmospheric effects like clouds. Synthetic aperture radar (SAR) data can offer insightful and systematic forest monitoring with complete time series due to signal penetration through clouds and day and night image acquisitions. This study explores the use of SAR data, combined with ancillary data and machine learning (ML), to estimate forest parameters typically derived from optical satellites. It investigates whether SAR signals provide sufficient information for the accurate estimation of these parameters, focusing on two spectral vegetation indices (Normalized Difference Vegetation Index – NDVI and Enhanced Vegetation Index – EVI) and two biophysical parameters (Leaf Area Index – LAI and Fraction of Absorbed Photosynthetically Active Radiation – FAPAR) in healthy and disturbed temperate forests in Czechia and Central Europe in 2021. Vegetation metrics derived from Sentinel-2 multispectral data were used to evaluate the results. A paired multi-modal time-series dataset was created using Google Earth Engine (GEE), including temporally and spatially aligned Sentinel-1, Sentinel-2, DEM-based features and meteorological variables, along with a forest type class. The inclusion of DEM-based auxiliary features and additional meteorological information improved the results. In the comparison of ML models, the traditional ML algorithms, Random Forest Regressor and Extreme Gradient Boosting (XGB) slightly outperformed the Automatic Machine Learning (AutoML) approach, auto-sklearn, for all forest parameters, achieving high accuracies (R2 between 70% and 86%) and low errors (0.055–0.29 of mean absolute error). XGB was the most computationally efficient. Moreover, SAR-based estimations over Central Europe achieved comparable results to those obtained in testing within Czechia, demonstrating their transferability for large-scale modeling. A key advantage of the SAR-based vegetation metrics is the ability to detect abrupt forest changes with sub-weekly temporal accuracy, providing up to 240 measurements per year at a 20 m resolution.

The number of phishing sites is increasing and becoming a problem. General phishing sites often have very short lives. Phishers are thought to construct phishing sites using tools such as phishing kits. Phishing sites constructed using the same tools have similar website structures. We propose a new method based on the similarity of website structure information defined by the types and sizes of web resources that make up these websites. Our method can detect phishing sites that is not registered with blocklists or do not have similar URL strings with targeting legitimate sites. In addition, our method can identify phishing sites that differed in appearance but have similar website structures. Our method is particularly effective for detecting phishing sites constructed by the same phishers or using the same tools, as our method identifies structural similarity between websites. We conducted an evaluation to confirm the correctness of our assumption using phishing sites constructed using phishing kits and the PhishTank dataset. We found a large number of phishing sites that were structurally similar to phishing sites constructed using phishing kits. We applied our method to web access logs provided by ordinary Japanese citizens, and detected some unknown phishing sites. We have also examined the possibility of improving our method based on the importance of web resources, determined using the number of occurrences in web access logs.

The internet and its various services have made users to easily communicate with each other. Internet benefits including online business and e-commerce. E-commerce has boosted online sales and online auction types. Despite their many uses and benefits, the internet and their services have various challenges, such as information theft, which challenges the use of these services. Information theft or phishing attacks are internet attacks that are major approach to success it is social engineering that the phisher has used. In these types of attacks, the attacker deceives the users and steals their valuable information by using a fake website that looks like real websites. The damage caused by fake websites and phishing attacks is so high that researchers are trying to identify these types of websites in different ways. So far, various methods have been developed to identify phishing web sites which most of them based on data- mining and learning machine are trying to identify these malicious websites. Artificial neural network is a data-mining method for identifying phishing websites which is used in most studies; however the error rate of this can be significant in detecting these websites, so learning-based optimization algorithm is used as a Swarm intelligence algorithm to reduce its error. In the proposed method, the error rate of multi-layer artificial neural network in detecting phishing websites is considered as a target function which minimized by using learning-based optimization algorithm. In the proposed method, learning- based optimization algorithm selects weights and bias of multi-layer artificial neural network optimally to minimize the error of clssification as an objective function. The datasets used to evaluate the proposed method are Phishing Websites explaind by others. The results of evaluating phishing attack dataset indicate that the rate of error of fake website detection in the proposed method is constantly reduced by repetition. The results of our assessment also indicate that the average accuracy, sensitivity, specificity, precision of the proposed method are 93.42, 92.27, 93.19 and 92.78%, respectively. The decision tree and regression are more accurate in detecting fake websites than artificial neural network.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

This paper presents a high-spatial-resolution dataset with remote sensing images of the Brazilian Cerrado for land use and land cover classification. The Biome Cerrado Dataset (CerraData) is a large database created from 150 scenes of the CBERS-4A satellite. Images were created by merging the near-infrared, green, and blue bands. Moreover, pan-sharpening was performed between all the scenes and their respective panchromatic bands, resulting in a final spatial resolution of two meters. A total of 2.5 million tiles of 256x256 pixels were derived from these scenes. From this total, 50 thousand tiles were labeled. We also conducted a few-shot learning experiment considering a training set with only 100 samples, 11 deep neural networks (DNNs), and two traditional machine learning (ML) algorithms, i.e., support vector machine (SVM) and random forest (RF). Results show that the DNN DenseNet-161 was the best model but its performance can be improved if it is used only as a feature extractor, leaving the classification task for the traditional ML algorithms. However, by decreasing the size of the training set, smarter approaches are needed. The labeled subset of CerraData as well as the source code we developed to support this study are available on-line: https://github.com/ai41uc/CerraData-code-data.

An exhaustive measurement of re-sampling detection in lossy compressed images using deep learning approach

Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricemia. We aimed to evaluate the performance of different machine learning models for the prediction of 1-year mortality in STEMI patients with hyperuricemia. We compared five machine learning models (logistic regression, k-nearest neighbor, CatBoost, random forest, and XGBoost) with the traditional global (GRACE) risk score for acute coronary event registrations. We registered patients aged >18 years diagnosed with STEMI and hyperuricemia at the Affiliated Hospital of Zunyi Medical University between January 2016 and January 2020. Overall, 656 patients were enrolled (average age, 62.5 ± 13.6 years; 83.6%, male). All patients underwent emergency percutaneous coronary intervention. We evaluated the performance of five machine learning classifiers and the GRACE risk model in predicting 1-year mortality. The area under the curve (AUC) of the six models, including the GRACE risk model, ranged from 0.75 to 0.88. Among all the models, CatBoost had the highest predictive accuracy (0.89), AUC (0.87), precision (0.84), and F1 value (0.44). After hybrid sampling technique optimization, CatBoost had the highest accuracy (0.96), AUC (0.99), precision (0.95), and F1 value (0.97). Machine learning algorithms, especially the CatBoost model, can accurately predict the mortality associated with STEMI complicated by hyperuricemia after a 1-year follow-up.

Detection of unexpected findings in radiology reports: A comparative study of machine learning approaches

Machine learning (ML) algorithms are increasingly used to predict outcomes in orthopaedic surgery, but their utility in hip arthroscopy remains unclear. This study aimed to (1) evaluate ML models for predicting outcomes, complications, and resource use following hip arthroscopy, (2) compare ML performance to traditional statistical methods, and (3) critically appraise the quality of existing literature. A systematic search of PUBMED, MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials was conducted on 4 April 2023. Included studies used ML models for clinical prediction in hip arthroscopy. Data on study design, outcomes, model development, validation and adherence to TRIPOD guidelines were extracted. Seventeen studies involving 37,549 patients (52.8% female; mean age 34.4 ± 6.1 years) were included. ML was used to predict clinical outcomes (47.1%), adverse events (47.1%) and resource utilisation (5.9%). The median area under the curve was 0.66 (range 0.5-0.94) for clinical outcomes and 0.7 (range 0.6-0.9) for adverse events. Resource utilisation was reported with a root-mean-square error of $3800, a logarithmic error of 0.2, and R² of 0.8. Only four studies compared ML to traditional regression; two found no difference, while two favoured ML. On average, studies met 12.3 of the 22 TRIPOD criteria, with only 29.4% meeting more than two-thirds. ML models in hip arthroscopy show variable performance in predicting outcomes and complications. Furthermore, most studies comparing ML models to traditional regression did not report any significant difference. Lastly, variability in adherence to TRIPOD guidelines highlights the need for future studies to improve transparency and reporting in the development of ML algorithms for hip arthroscopy. Given the potential of ML models in enhancing clinical decision-making for hip arthroscopy, along with the findings of the current study, their application should be used with caution. Level IV, systematic review of level II-IV studies.

One of the ways criminals steal identity in the cyberspace is using phishing. Attackers host phishing websites that resemble a legitimate website and entice users to click on hyperlinks which directs them to these fake websites. Attackers use these fake sites to capture personal information such as login, passwords and social security numbers from innocent victims, which they later use to commit crimes. We propose here a robust methodology to detect phishing websites that employs for semantic analysis a topic modeling technique, Latent Dirichlet Allocation, and for classification, AdaBoost. The methodology developed is a content driven approach that is device independent and language neutral. The website content of mobile and desktop clients are collected by employing an intelligent web crawler. The website contents that are not in English are translated to English using Google's language translator. Topic model is built using the translated contents of desktop and mobile clients. The phishing website classifier is built using (i) distribution probabilities for the topics found as features using Latent Dirichlet Allocation and (ii) AdaBoost voting technique. Experiments were conducted using one of the large public corpus of website data containing 47500 phishing websites and 52500 good websites. Results show that our method achieves a F-measure of 99%.

Datasets for phishing websites detection