Legitimate Websites Research Articles

Identifying legitimate websites selling medicines in South Africa

Background: The online sale of medicines has skyrocketed globally, where medicines are purchased in the comfort and privacy of one’s own home. However, there are an increasing number of illegal websites selling counterfeit and falsified medicines.Aim: The aim of this study was to review the information and medicines sold online through pharmacy websites in South Africa (SA) and their compliance with local legislation.Setting: The study setting comprised online pharmacy websites in SA.Methods: This quantitative descriptive study used a purposeful questionnaire based on the South African Pharmacy Council inspection questionnaire for pharmacies operating websites. Websites claiming to be an online pharmacy in SA were included.Results: There were 25 websites reviewed, which claimed to be online pharmacies. Majority (n = 22) were found to be legal websites, operated by a registered community pharmacy and required a prescription for the purchase of prescription only medicines. Few websites complied with legislation such as displaying the Y number (n = 5) and name of the responsible pharmacist (n = 10). The remaining three websites were not linked to physical pharmacies, none complied with legislation and supplied medicines without a prescription.Conclusion: Although the online sale of medicines is regulated in SA, not all websites complied with legislation. The study highlighted the importance of monitoring websites claiming to be online pharmacies and their compliance with legislation.Contribution: Four stakeholders, consumers; pharmacy websites; regulators and healthcare providers, were identified and their role outlined in promoting the safe online purchasing of medicines.

Phishing URL detection with neural networks: an empirical study

Cybercriminals create phishing websites that mimic legitimate websites to get sensitive information from companies, individuals, or governments. Therefore, using state-of-the-art artificial intelligence and machine learning technologies to correctly classify phishing and legitimate URLs is imperative. We report the results of applying deterministic and probabilistic neural network models to URL classification. Key achievements of this work are: (1) The development of a unique approach based on probabilistic neural networks that improves classification accuracy. (2) We show for the first time in URL phishing research that a machine learning model trained on a combination of open source and private datasets is successful in production. The dataset is constructed from open sources like Alexa, PhishTank, or OpenPhish and, most importantly, real-world production data from EasyDMARC. The daily validation of the model using daily reported URL data and corresponding labels, both from open-source platforms and private production, reach on average a 97% accuracy on the validation dataset, labeled by PhishTank, OpenPhish and EasdDMARC where possible mislabeled data can not be excluded and was not possible to check due to large number of URLs. Feature engineering was done without third-party dependencies. Lastly, the evaluation of both deterministic and probabilistic models shows high accuracy on short and long URLs, where short URLs are defined as having less than 50 characters.

Explainable artificial intelligence in web phishing classification on secure IoT with cloud-based cyber-physical systems

IoT is the technology that aids the interconnection of all kinds of devices over the internet to replace data, monitor devices and enhance actuators to create outcomes. Cyber-physical systems (CPS) contain control and computation components, which are compactly assured with physical procedures. The internet plays a prominent role in modern lives, and the cybersecurity challenge caused by phishing attacks is significant. This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. Leveraging feature extraction and innovative algorithms, the projected method aims to distinguish between malicious and legitimate websites by features of phishing attempts and analyzing inherent patterns. Phishing is a significant threat that causes extensive financial losses for internet users yearly. This fraudulent act includes identity hackers utilizing clever approaches to deceive individuals into revealing sensitive data. Generally, phishers use strategies such as advanced phishing software and fake emails to illegally acquire confidential details like usernames and passwords from financial accounts. This article develops an Explainable Artificial Intelligence with Aquila Optimization Algorithm in Web Phishing Classification (XAIAOA-WPC) approach on secure Cyber-Physical Systems. The developed XAIAOA-WPC approach mainly emphasizes the effectual classification and recognition of web phishing based on CPS. In the first phase, preprocessing is carried out on three levels: data cleaning, text preprocessing, and standardization. Furthermore, the Harris' Hawks optimization-based feature selection (HHO-FS) method is applied to derive feature subsets. The XAIAOA-WPC method utilizes a multi-head attention-based long short-term memory (MHA-LSTM) model for web phishing recognition. Besides, the detection outcomes of the MHA-LSTM approach are enhanced by using the Aquila optimization algorithm (AOA) model. At last, the XAIAOA-WPC method incorporates the XAI model LIME for superior perception and explainability of the black-box process for precise identification of intrusions. The simulation outcome of the XAIAOA-WPC method is examined on a benchmark database. The experimental validation of the XAIAOA-WPC method exhibited a superior accuracy value of 99.29 % over existing techniques.

Kingdom of Fintech: crowdfunding shaping the future in the kingdom of Saudi Arabia

Purpose This study aims to assess that whether Islamic real estate crowdfunding (RECF) can offer a compelling alternative investment that can attract substantial funds from traditional securities and other conventional methods or otherwise. Design/methodology/approach The current study draws on secondary data that was published on legitimate website, Twitter and official documents. Document analysis is conducted using the statements of privacy policy, Sharia compliance, terms and conditions disclosers and the established facts. Second, to achieve in-depth knowledge, a qualitative analysis was conducted for the published interviews and presentations with Aseel CEO Majed Abalkhail on YouTube. Thematic analysis is adapted; it is among the most popular types of analyzing qualitative data. Findings The findings show that the Aseel platform has been successful in providing simple access to investment opportunities by minimizing the obstacles, reducing entry and exit costs, streamlining the process and widening the investor’s base. Originality/value This paper seeks to contribute to the literature on crowdfunding, Islamic crowdfunding and RECF. Its objectives include exploring the concept of crowdfunding, its growth and various types. Furthermore, the paper aims to examine the expansion of the Islamic crowdfunding system, its current market position and a focus on the Saudi Arabian market. Lastly, the paper investigates the first RECF in Saudi Arabia, Aseel Company, which has achieved remarkable success with seven investment funds completed within its first year of establishment.

The Harms of Imprisonment and Envisioning a Desistance-Supporting ‘Good Society’

Research evidence in the 21st century strongly indicates that prisons are inherently harmful environments in which to try to support rehabilitation, because of their destructive impact on identity, self-actualisation, mental health and social relationships. Yet HM Prison and Probation Service (HMPPS) continues to propose that prisons can be legitimate sites of rehabilitation as well as centres of punishment. This article begins by interrogating this proposition by looking at research conducted in an adult male prison. By specifically focusing on the interpersonal conflicts and tensions that the men experience in their daily lives, the research highlights the ‘relational’ and ‘self-actualising’ harms that undermine the development of a successful rehabilitative culture within a prison. The article then moves on to consider a utopian vision of a ‘desistance-supporting’ rehabilitative ‘good society’ by drawing on a second piece of research that captures the voices and experiences of participants in a very successful community-based resettlement project. Lessons learned from this are then collated to consider how to build a rehabilitative ‘good society’ in prisons. The article concludes with a cautionary note about the possible difficulty of creating such a society in neoliberal times.

Phishing Website Detection Using Bidirectional Gated Recurrent Unit Model and Feature Selection

Phishing attacks continue to be a significant threat to internet users, necessitating the development of advanced detection systems. This study explores the efficacy of a Bidirectional Gated Recurrent Unit (BiGRU) model combined with feature selection techniques for detecting phishing websites. The dataset used for this research is sourced from the UCI Machine Learning Repository, specifically the Phishing Websites dataset. This approach involves cleaning and preprocessing the data, then normalizing features and employing feature selection to identify the most relevant attributes for classification. The BiGRU model, known for its ability to capture temporal dependencies in data, is then applied. To ensure robust evaluation, we utilized cross-validation, dividing the data into five folds. The experimental results are highly promising, demonstrating a Mean Accuracy, Mean Precision, Mean Recall, Mean F1 Score, and Mean AUC of 1.0. These results indicate the model's exceptional performance distinguishing between phishing and legitimate websites. This study highlights the potential of combining BiGRU models with feature selection and cross-validation to create highly accurate phishing detection systems, providing a reliable solution to enhance cybersecurity measures.

DETECTION OF UNHEALTHY WEBSITES USING MACHINE LEARNING

In recent years, advancements in Internet and cloud technologies have led to a signicant increase in electronic trading in which consumers make online purchases and transactions. Accompanying this achievement are vices like unauthorized access to users' sensitive information and damages to enterprise resources. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. This study aims to develop an efcient machine-learning program to detect phishing websites with high accuracy. Most phishing webpages look identical to the actual web pages and various strategies for detecting phishing websites, such as blacklisting, and heuristics, among others have been suggested. Existing research works showed that the performance of the phishing detection system is limited and there is a demand for intelligent techniques to protect users from cyber-attacks. A Uniform resource locator (URL) detection technique based on a supervised machine learning approach – Naïve Bayes is employed and implemented in Python programming language. The efcacy of this approach was determined on a phishing dataset made up of 7900 malicious and 5800 legitimate sites, respectively. The results show that using the proposed methodology an accuracy of 96% can be achieved by using stacking, ltering along the Naïve Bayes and logistic regression. This study thoroughly investigates the use of machine laearning with features extracted from the URLs and was able to showcase common words for the identication of either phishing (unhealthy) or good websites and proffered a guide to end users against the recent approaches in malicious URLs detection.

Enhancing Phishing Detection: A Novel Hybrid Deep Learning Framework for Cybercrime Forensics

Abstract: Security concerns in internet communication, particularly phishing attacks, pose significant challenges to safeguarding user information. Phishing attacks aim to steal personal data by mimicking legitimate websites, making traditional detection methods less effective. Machine learning-based approaches, though widely used, often struggle with concealed phishing websites and evolving tactics by attackers. Key advancements include the integration of more layers and larger training datasets, along with feature extraction from the Phish Tank dataset. Our proposed model comprises seven layers, culminating in a specialized output layer structure. Experiment results showcase the efficacy of our approach, underscoring its potential to significantly enhance internet security. Furthermore, our model's ability to adapt to highly concealed phishing websites and the dynamic nature of attackers' tactics marks a significant improvement over existing methodologies. By incorporating a comprehensive feature set and leveraging deep learning techniques, our approach achieves state-of-the-art performance in phishing website detection.

Phishing URL Detection Using XGBoost

Abstract: Phishing attacks are a major threat to cybersecurity, affecting individuals and organizations around the world. In this project we are developing a phishing site detection system using XGBoost, a widely used machine learning algorithm that is well-known for its effectiveness and precision in classification tasks. Our approach involves extracting features from URLs and related domains, preprocessing that data, and training XGBoost’s model. We test our system’s performance by using a dataset of both phishing and normal websites to see how well our system detects phishing attempts. The approach involves the extraction of features from URLs and associated domains, followed by data preprocessing and the training of XGBoost’s model. Performance evaluation is conducted using a dataset comprising both phishing and legitimate websites to assess the system’s efficacy in detecting phishing attempts. The project aims to enhance cybersecurity measures by providing an efficient and accurate solution for identifying and mitigating phishing attacks, ultimately contributing to the protection of online users and organizations against malicious activities

Phishing URL Detection: A Basic Machine Learning Approach

Phishing is currently one of the most trending scam attacks in information transmission worldwide. Such occurrences can cause severe network disruption to corporate organizations, financial and/or educational institutions and even individual network subscribers. Phishing attacks would normally involve developing a malicious webpage mimicking a legitimate website. This lures unsuspecting users into giving out personal information, such as banking details, social security numbers, passwords, usernames, etc. A phishing fraudster sends an e-mail or text message to a target webpage that contains its Universal Resource Locator (URL). The result could be monetary loss, data breach, intellectual property theft, damaged reputation, and loss of customers. In this paper, we propose a simple phishing detection approach. Moreover, because the impostor is always evolving attacking techniques in a bid to evade detection, our method addresses this using a supervised machine learning system. From the website content, we extract the stochastic dynamical patterns and then use this to predict the authenticity of the website. The Extreme Gradient Boosting (XGBoost) algorithm is used to model the website features to obtain a better prediction result. The proposed technique can detect phishing websites with an accuracy of 86.6%.

Machine Learning Approaches For Phishing Detection: A Comparative Analysis

In this study, the effectiveness of four machine learning models in detecting phishing websites is evaluated. Utilizing a diverse dataset, the analysis reveals that Random Forest emerges as the top performer, achieving a test accuracy of 91.49%. Notably, Random Forest exhibits robustness in distinguishing between legitimate websites and malicious ones. While Decision Tree, K-Nearest Neighbors, and Naive Bayes also demonstrate promise, they encounter difficulties in accurately classifying phishing URLs, especially within certain categories. The findings underscore the pivotal role of machine learning in cybersecurity defence against phishing attacks. The study suggests avenues for future research, such as enhanced feature engineering and exploration of advanced ensemble techniques and deep learning approaches for improved phishing detection. This research contributes to the ongoing endeavours to develop more resilient anti-phishing tools and bolster digital security against evolving cyber threats. Key Words: Phishing, Machine Learning, Cybersecurity, Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes, Website Detection, Feature Engineering, Ensemble Techniques, Model Comparison, Fine-Tuning.

Developing a Context-Aware Convolutional Neural Network (CACNN) for Enhanced Phishing Website Detection

This paper presents the development of a Context-Aware Convolutional Neural Network (CACNN) aimed at improving the detection of phishing websites. Given the escalating sophistication of phishing attacks, traditional detection methods have become less effective, necessitating more advanced solutions. This research addresses this need by proposing a novel CACNN model that integrates visual and textual analysis of websites using advanced machine learning techniques. The CACNN model is designed to understand the context of website content, making it adept at identifying subtle cues of phishing attempts that might elude conventional detection systems. The methodology involves training the CACNN with a comprehensive dataset comprising both phishing and legitimate websites, followed by rigorous evaluation using standard performance metrics. The results demonstrate a significant improvement in phishing detection accuracy compared to existing methods, highlighting the efficacy of the context-aware approach. This research contributes to the cybersecurity field by providing a more robust and intelligent tool for safeguarding against phishing threats.

Safeguarding Cyber Ecosystems: Machine Learning and Data Mining Approaches for Proactive Phishing Detection in Websites

Abstract: The prevalence of cyber threats, notably phishing attacks, presents a significant challenge in the digital realm with the increasing reliance on the internet. Phishing, where victims' credentials are illicitly acquired through deceptive websites resembling legitimate ones, is a particularly concerning form of cybercrime. This paper proposes an innovative system utilizing Machine Learning and Data Mining techniques to detect both established and newly generated phishing URLs without historical behavioral data. The system, embodied in a Chrome browser plugin, operates in real-time to provide immediate protection as users browse web pages. A distinctive feature of this system is its capability to identify phishing websites lacking prior behavioral patterns, ensuring adaptability to evolving cyber threats. Through comprehensive training with an extensive dataset, the model powering the plugin aims for high accuracy in detecting phishing attempts. By deploying advanced Machine Learning methodologies, the system discerns subtle patterns and characteristics intrinsic to phishing URLs, effectively distinguishing them from legitimate websites. The ultimate objective is to enhance cyber security by providing users with a robust and proactive tool against the growing sophistication of phishing attacks, thereby contributing to the ongoing efforts to create a secure online environment amidst the dynamic landscape of cyber threats.

Detection of malicious URLs using machine learning

AbstractThe detection of fraudulent URLs that lead to malicious websites using addresses similar to those of legitimate websites is a key form of defense against phishing attacks. Currently, in the case of Internet of Things devices is especially relevant, because they usually have access to the Internet, although in many cases they are vulnerable to these phishing attacks. This paper offers an overview of the most relevant techniques for the accurate detection of fraudulent URLs, from the most widely used machine learning and deep learning algorithms, to the application, as a proof of concept, of classification models based on quantum machine learning. Starting from an essential data preparation phase, special attention is paid to the initial comparison of several traditional machine learning models, evaluating them with different datasets and obtaining interesting results that achieve true positive rates greater than 90%. After that first approach, the study moves on to the application of quantum machine learning, analysing the specificities of this recent field and assessing the possibilities it offers for the detection of malicious URLs. Given the limited available literature specifically on the detection of malicious URLs and other cybersecurity issues through quantum machine learning, the research presented here represents a relevant novelty on the combination of both concepts in the form of quantum machine learning algorithms for cybersecurity. Indeed, after the analysis of several algorithms, encouraging results have been obtained that open the door to further research on the application of quantum computing in the field of cybersecurity.

Detection of Phishing Website using Machine Learning

Phishing is a widespread cybercrime where scammers trick people into sharing personal or confidential information by pretending to be a legitimate website. Despite various machine learning methods developed for detecting phishing websites using features from web samples, not much attention has been given to picking the right features efficiently. This study aims to figure out the crucial features necessary for effective phishing detection, improving the accuracy and efficiency of machine learning systems. By identifying and prioritizing these features, our research contributes to creating simpler methods that keep users safe from phishing threats. We focus on pinpointing the key characteristics that consistently set phishing websites apart from legitimate ones, enhancing the precision and reliability of phishing detection algorithms through careful analysis and experimentation. This work aligns with broader goals of strengthening cybersecurity measures, protecting individuals and organizations from falling victim to online deception, and giving users more robust tools for secure online navigation.To evaluate the feature selection in developing ageneralizable phishing detection, these classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a fast phishing detection which is also robust toward zero-day attacks

Phishing Detection Using Machine Learning: A Model Development and Integration

This study aimed to develop a robust machine learning-based phishing detection system using algorithms such as K-nearest neighbour (KNN), artificial neural network (ANN), and random forest (RF). It utilised datasets from Ariyadasa et al. (2021) and UNB (2016) to discern patterns distinguishing legitimate from phishing websites. Furthermore, an objective was to integrate the optimal model into a Django-based web application, facilitating real-time phishing detection. A comprehensive literature review on phishing detection techniques was also undertaken. Datasets chosen underwent rigorous pre-processing to address missing values and imbalance. Feature selection was achieved manually and automatically using mutual information classification. Three machine learning algorithms, RF, KNN, and ANN, were explored. Their hyper-parameters were optimised using GridSearchCV. Performance results highlighted RF's accuracy at 99.78%, KNN's at 99.67%, and ANN's at 99.11%. While RF and KNN models perfectly identified legitimate websites, ANN showcased an impeccable detection of phishing websites. The RF model, with the highest accuracy, was integrated into a Django application, providing a user interface for real-time phishing detection. All models exhibited high accuracy rates, demonstrating their efficacy in phishing detection. While RF was integrated into the web application for this study, the choice between models depends on specific user or business requirements and priorities. Feedback mechanisms within the Django application further promise refinement in future recommendations. The study provides a foundational step toward enhancing web safety through effective phishing detection.

A Review on Online Phishing Detection Using Machine Learning

Abstract: The rapid growth of the internet and the increasing dependence on online services have led to a significant rise in cyber threats, with phishing attacks being a prevalent and pervasive threat. Phishing attacks often involve the use of deceptive techniques to trick users into divulging sensitive information, such as login credentials, credit card details, and personal information. Detecting and preventing phishing attacks in real-time has become a critical challenge for individuals, businesses, and organizations. This research paper presents an approach for online phishing detection using machine learning techniques. The primary objective is to develop a system that can automatically identify and classify phishing websites and emails, thereby enhancing cyber security and protecting users from falling victim to these malicious activities. Online phishing detection has gained significant importance, which will only grow with the amount of dependency on cyberspace, the proposed system provides an easy solution, during cases when the user is unsure about the authenticity of the website visited, they can try to copy the Uniform Resource Locator (URL) and paste the link into the online phishing detection system. Through the system process, it will help the user to identify whether given links were legitimate website or it is a phishing website. Therefore, the user will not be in a doubtful situation the whole day in wondering whether the information they gave in a certain website is safe or not. Providing complex decision with simplicity, the system will help the user to detect each variable of URL given accurate.

HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction

Phishing techniques are the most frequently used threat by attackers to deceive Internet users and obtain sensitive victim information, such as login credentials and credit card numbers. So, it is important for users to know the legitimate website to avoid the traps of fake websites. However, it is difficult for lay users to distinguish legitimate websites, considering that phishing techniques are always developing from time to time. Therefore, a legitimate website detection system is an easy way for users to avoid phishing websites. To address this problem, we present a hybrid deep learning model by combining a convolution neural network and long short-term memory (HCNN-LSTM). A one-dimensional CNN with a LSTM network shared estimation of all sublayers, then implements the proposed model in the benchmark dataset for phishing prediction, which consists of 11430 URLs with 87 attributes extracted of which 56 parameters are selected from URL structure and syntax. The HCNN-LSTM model was successful in binary classification with accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, 95.00%, successively outperforming the CNN and LSTM. Thus, the results show that our proposed model is a competitive new model for the legitimate web prediction tasks.

PhishTransformer: A Novel Approach to Detect Phishing Attacks Using URL Collection and Transformer

Phishing attacks are a major threat to online security, resulting in millions of dollars in losses. These attacks constantly evolve, forcing the cyber security community to improve detection systems. One major problem with current detection systems is that they cannot detect new phishing attacks, such as Browser in the Browser (BiTB) and malvertising attacks. These attacks hide behind legitimate Uniform Resource Locators (URLs) and can evade detection systems that only analyze a web page URL without exploring the page content. To address this problem, we propose PhishTransformer, a deep-learning model that can detect phishing attacks by analyzing URLs and page content. We propose only using URLs embedded within a webpage, such as hyperlinks and JFrames, to train PhishTransformer. This helps reduce the number of features that need to be extracted from the page content, which makes training the model more efficient. PhishTransformer combines convolutional neural networks and transformer encoders to extract features from website URLs and page content. These features are then used to train a classifier that can distinguish between phishing attacks and legitimate websites. We tested PhishTransformer on a dataset of 10,000 URLs. Our results show that PhishTransformer can achieve an F1-score of 99%, precision of 99%, and recall of 99%. This result suggests that PhishTransformer is a promising new approach to phishing detection.

Phishing like the first step to gaining access

Phishing as a term that means the technique of sending phishing messages will be re-searched based on findings in public access and using the listed links. The process of a phish-ing attack will be analyzed, and then we will pay attention to the technical vectors of how us-ers become victims of the attack. Finally, existing research on phishing attacks and related prevention approaches will be reviewed. Mitigating phishing attacks is an important research topic worth exploring. Although a lot of research has been done, this threat still exists in the real world, and its prevalence is constantly increasing. According to research results, detecting phishing attacks is a difficult problem. There are two main strategies used to mitigate phishing attacks; or improving the performance of phishing detection technology or improving people's awareness of these at-tacks. Developing human expertise is a key way to defeat phishing attacks, as phishing attacks exploit human weaknesses rather than network weaknesses. Also, humans are always the weakest link in social engineering attacks. Compared to phishing website detection, phishing email detection may require user in-volvement to achieve better detection results. Because the success of a phishing email de-pends on its context. Specifically, when the premise of the phishing email is consistent with the user's work context (or current situation). Most anti-phishing solutions are implemented to mitigate general phishing attacks, but they ignore some specific situations, such as advanced phishing attacks. To prevent advanced phishing attacks, phishing websites are difficult to detect if a victim is attacked using stolen DNS data because the URL content and website content are the same as legitimate websites. Most content-based approaches may not work because the content of the accessed URL is an important factor in the decision. To prevent subdomain hijacking attacks, it is difficult to detect a phishing website if the phishers have hosted the website on a subdomain taken from a legitimate website. Regardless of the web content, URL, and SSL certificate information, they will all be the same as the le-gitimate website. Moreover, the approach to enumeration of subdomains needs improvement, as most current tools are based on rough enumeration, existing dictionaries may not cover all instances of subdomains, as some subdomains may be meaningless.