Phishing Website Detection Research Articles

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

Intelligent Phishing Website Detection Model Powered by Deep Learning Techniques

Phishing websites or URLs differ from software flaws as they exploit human vulnerabilities rather than technical weaknesses. Various methods exist to undermine the security of an internet user, but the most prevalent approach is phishing. This sort of assault aims to acquire or exploit a user's personal data, including passwords, credit card details, identity, and account information. Phishers gather user information by pretending to be authentic websites that are visually indistinguishable. Users' confidential data can be potentially retrieved, exposing them to the possibility of financial detriment or identity fraud. Consequently, there is a pressing requirement to develop a system that efficiently identifies phishing websites. This research presents three discrete deep learning methodologies for identifying phishing websites, which involve the use of long short-term memory (LSTM) and convolutional neural network (CNN) for comparison, and ultimately an LSTM-CNN-based methodology. The experimental results confirm the precision of the proposed methods, specifically 99.2%, 97.6%, and 96.8% for CNN, LSTM–CNN, and LSTM, respectively. The CNN-based technology displayed a superior phishing detection mechanism.

Phishing Website Detection Using Improved Multilayered Convolutional Neural Networks

Phishing Website Detection Using Improved Multilayered Convolutional Neural Networks

Phishing attack detection using gradient boosting

Phishing is a prevalent cyber attack that uses deceptive websites to trick individuals into revealing personal information. These sites mimic legitimate ones to steal data such as usernames, passwords, and financial details. Detecting phishing is crucial, and machine learning algorithms are effective tools for this task. Attackers favor phishing due to its effectiveness in tricking victims with authentic-looking yet malicious links, which can breach security measures. This method employs machine learning to innovate phishing website detection. However, attackers can manipulate features like HTML, DOM, and URLs using web scraping and scripting languages. A new approach using machine learning classifiers tackles these threats by analyzing internet URLs and domain names. A dataset sourced from globally recognized intelligence services and organizations facilitates streamlined feature extraction, reducing processing overhead by prioritizing URL and domain name traits. The Gradient Boosting Classifier is used on an 11,055-instance dataset with thirty-two features to classify phishing URLs, demonstrating superior accuracy compared to methods like Random Forest. Gradient boosting is highly effective across various machine learning tasks, leveraging aggregated weak learners such as decision trees for strong predictive accuracy. Its suitability for handling imbalanced datasets makes it particularly effective for phishing detection, which is crucial for distinguishing between legitimate and malicious URLs. This method enhances accuracy by extracting and comparing distinct characteristics of legitimate and phishing URLs. By focusing on URL and domain name attributes, a more effective approach to identifying phishing attempts in cybersecurity is proposed.

A Study on Adversarial Sample Resistance and Defense Mechanism for Multimodal Learning-Based Phishing Website Detection

A Study on Adversarial Sample Resistance and Defense Mechanism for Multimodal Learning-Based Phishing Website Detection

Prediction of Cybercrime using the Avinashak Algorithm

The detection and prevention of phishing websites continue to be major obstacles in the continually changing field of cybersecurity. Phishing attacks continue to use sophisticated methods to exploit user vulnerabilities, thus it is vital to predict and identify these malicious websites. Traditional techniques for detecting phishing sites frequently rely on rule-based and domain-based approaches, which might not adequately capture the dynamic nature of phishing attacks. The Avinashak Crime Prediction Algorithm appears to be a proprietary or specialized algorithm not widely known in the machine learning community. Its details and working principles are not publicly available, which makes it challenging to provide a detailed explanation without additional information.

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.

Design of Automated Website Phishing Detection using Sequential Mechanism of RCL Algorithm

The phishing outbreaks in internet has become a major problem in web safety in recent years. The phishers will be stealing crucial economic data regarding the web user to perform economic break-in. In order to predict phishing websites, many blacklist-based phishing website recognition methods are used in this study. Traditional methods of detecting phishing websites rely on static features and rule-based schemes, which can be evaded by attackers. Recently, Deep Learning (DL) and Machine Learning (ML) models are employed for automated website phishing detection. With this motivation, this study develops an automated website phishing detection using the sequential mechanism of RCL algorithm. The proposed model employs Long-Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Random Forest (RF) models for the detection of attacks in the URLs and webpages by the similarity measurement of the decoy contents. The proposed model involves three major components namely, RF for URL phishing detection, CNN based phishing webpage detection, and LSTM based website classification (i.e., legitimate and phishing). The experimental result analysis of the RCL technique is tested on the benchmark dataset of Alexa and PhishTank. A comprehensive comparison study highlighted that the RCL algorithm accomplishes enhanced phishing detection performance over other existing techniques in terms of distinct evaluation metrics.

Detecting Phishing Website using Machine Learning

Phishing attack is a simplest way to obtain sensitive information from innocent users. Aim of the phishers is to acquire critical information like username, password and bank account details. Cyber security persons are now looking for trustworthy and steady detection techniques for phishing websites detection. This paper deals with machine learning technology for detection of phishing URLs by extracting and analyzing various features of legitimate and phishing URLs. Decision Tree, random forest and Support vector machine algorithms are used to detect phishing websites. Aim of the paper is to detect phishing URLs as well as narrow down to best machine learning algorithm by comparing accuracy rate, false positive and false negative rate of each algorithm. Keywords Phishing attack, Machine learning

Improving Phishing Website Detection with Machine Learning: Revealing Hidden Patterns for Better Accuracy

Phishing attacks remain a significant threat to internet users globally, leading to substantial financial losses and compromising personal information. This research study investigates various machine learning models for detecting phishing websites, with a primary focus on achieving high accuracy. After an extensive analysis, the Random Forest Classifier emerged as the most suitable choice for this task. Our methodology leveraged machine learning techniques to uncover subtle patterns and relationships in the data, going beyond traditional URL and content-based restrictions. By incorporating diverse website features, including URL and derived attributes, Page source code-based features, HTML JavaScript-based features, and Domain-based features, we achieved impressive results. The proposed approach effectively classified the majority of websites, demonstrating the efficiency of machine learning in addressing the phishing website detection challenge with an accuracy of over 98%, recall exceeding 98%, and a false positive rate of less than 4%. This research offers valuable insights to the field of cyber security, providing internet users with improved protection against phishing attempts.

Ontology-Based Intelligent Interface Personalization for Protection Against Phishing Attacks

Millions of users on the Internet have fallen into phishing website traps. Detection tools are designed to warn users against such attacks, but often fail to achieve this purpose. One crucial reason behind this is that users rarely have a chance to interact and build a relationship with a detection tool that stealthily runs at the backend. A warning message on a rarely seen interface from such a tool hardly inspires users’ trust in its authenticity and accuracy. In this study, we propose an ontology-based intelligent interface personalization (OBIIP) design for the warning interfaces of phishing website detection tools. We first constructed an ontology of warning interface elements (OWIE), which is a comprehensive knowledgebase for warning interface design. We then used OWIE in the design and creation of an OBIIP prototype and assessed it in a laboratory experiment and an online experiment. The results show the significant value of OBIIP in improving users’ performance in terms of self-protection against website phishing attacks and building a stronger relationship with the detection tool in terms of trust in and use of the tool.

Phishing Website Detection using Machine Learning Rules with Cryptography Technique

Phishing Website Detection using Machine Learning Rules with Cryptography Technique

Deep learning based phishing website detection

Deep learning based phishing website detection

Performance Investigation of Phishing Website Detection by Improved Deep Learning Techniques

Performance Investigation of Phishing Website Detection by Improved Deep Learning Techniques

A WEB APPLICATION FOR REAL-TIME PHISHING WEBSITE DETECTION

The rapid proliferation of the internet has led to an alarming increase in cyber threats, particularly phishing attacks. Phishing websites impersonate legitimate ones, luring users into divulging sensitive information such as login credentials and financial details. Detecting and preventing such attacks are crucial for safeguarding user privacy and security. This paper presents the development of a web application for real-time phishing website detection. The application employs machine learning techniques, specifically the XGBoost algorithm, to analyze the characteristics of URLs and classify them as either legitimate or phishing. It offers users a simple and effective way to check the authenticity of websites before interacting with them.The web application provides an intuitive user interface, allowing users to enter a URL and receive an instant assessment of its legitimacy. The underlying machine learning model is trained on a diverse dataset of legitimate and phishing URLs, ensuring robust detection capabilities. Additionally, the application provides insights into the features used for classification, enhancing user awareness of potential threats. The proposed web application serves as a valuable tool in the fight against phishing attacks, empowering users to make informed decisions when navigating the web. Its real-time detection capabilities and user-friendly interface make it accessible to a wide audience, contributing to a safer online environment. KEYWORDS Web Application, Phishing Detection, Machine Learning, XGBoost, User Privacy.

Single and Hybrid-Ensemble Learning-Based Phishing Website Detection: Examining Impacts of Varied Nature Datasets and Informative Feature Selection Technique

To tackle issues associated with phishing website attacks, the study conducted rigorous experiments on RF, GB, and CATB classifiers. Since each classifier was an ensemble learner on their own; we integrated them into stacking and majority vote ensemble architectures to create hybrid-ensemble learning. Due to ensemble learning methods being known for their high computational time costs, the study applied the UFS technique to address these concerns and obtained promising results. Since the scalability and performance consistency of the phishing website detection system across numerous datasets is critical to combating various variants of phishing website attacks, we used three distinct phishing website datasets (DS-1, DS-2, and DS-3) to train and test each ensemble learning method to identify the best-performed one in terms of accuracy and model computational time. Our experimental findings reveal that the CATB classifier demonstrated scalable, consistent, and superior accuracy across three distinct datasets (attained 97.9% accuracy in DS-1, 97.36% accuracy in DS-2, and 98.59% accuracy in DS-3). When it comes to model computational time, the RF classifier was discovered to be the fastest when applied to all datasets, while the CATB classifier was discovered to be the second quickest when applied to all datasets.

ML and DL-based Phishing Website Detection: The Effects of Varied Size Datasets and Informative Feature Selection Techniques

One must interact with a specific webpage or website in order to use the Internet for communication, teamwork, and other productive activities. However, because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites, they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware. It is impossible to stop attackers from creating phishing websites, which is one of the core challenges in combating them. However, this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handover sensitive information. In this study, five ML and DL algorithms—CATB, GB, RF, MLP, and DNN—were tested with three different reputable datasets and two useful feature selection techniques, to assess the scalability and consistency of each classifier's performance on varied dataset sizes. The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets (DS-1, DS-2, and DS-3) with respective values of 97.9%, 95.73%, and 98.83%. The GB classifier achieved the second-best accuracy across all datasets (DS-1, DS-2, and DS-3) with respective values of 97.16%, 95.18%, and 98.58%. MLP achieved the best computational time across all datasets (DS-1, DS-2, and DS-3) with respective values of 2, 7, and 3 seconds despite scoring the lowest accuracy across all datasets.

A novel phishing website classification method based on hybrid sampling

ABSTRACT In real-world anomaly detection tasks such as Credit Card Fraud Detection, Cancer Patients Detection, Phishing Website Detection, etc., the training datasets often suffer from skewed class distribution. But the traditional Machine Learning (ML) classification algorithms assume balanced class distribution and equal misclassification costs. As a result, when class-imbalanced data are presented to the traditional ML algorithms they tend to produce biased and inaccurate predictive ML models. In this study, we propose four novel Phishing Website Classification models namely, SMOTEENN-XGB, SMOTEENN-RF, SMOTEENN-LR, and SMOTEENN-SVM by combining SMOTEENN (SMOTE + ENN) hybrid sampling technique with eXtreme Gradient Boosting (XGB), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) classifiers respectively. We propose the use of SMOTEENN hybrid sampling as the novel approach to address the problem of class imbalance in Phishing Website datasets prior to building classification models. To the best of our knowledge and belief, our novel proposed four models SMOTEENN-XGB, SMOTEENN-RF, SMOTEEEN-LR, and SMOTEENN-SVM for Phishing Website Detection based on SMOTEENN hybrid sampling approach have not been published in the existing studies as of now.

Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors

Although machine learning based algorithms have been extensively used for detecting phishing websites, there has been relatively little work on how adversaries may attack such "phishing detectors" (PDs for short). In this paper, we propose a set of Gray-Box attacks on PDs that an adversary may use which vary depending on the knowledge that he has about the PD. We show that these attacks severely degrade the effectiveness of several existing PDs. We then propose the concept of operation chains that iteratively map an original set of features to a new set of features and develop the "Protective Operation Chain" (POC for short) algorithm. POC leverages the combination of random feature selection and feature mappings in order to increase the attacker's uncertainty about the target PD. Using 3 existing publicly available datasets plus a fourth that we have created and will release upon the publication of this paper, we show that POC is more robust to these attacks than past competing work, while preserving predictive performance when no adversarial attacks are present. Moreover, POC is robust to attacks on 13 different classifiers, not just one. These results are shown to be statistically significant at the p < 0.001 level.

A high-accuracy phishing website detection method based on machine learning

The rapid development of e-commerce, e-banking, and social networks has made phishing attack detection one of the most critical technologies in all cyber security systems. To improve the efficiency of anti-phishing techniques, we present an improved predictive model based on machine learning. The proposed method uses six different algorithms; Logistic Regression, K-Nearest Neighbors, Naive Bayes, Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost). The experiments are based on a public dataset of 58,000 legitimate websites and 30,647 phishing ones, including 112 attributes for each sample. Our evaluations in the feature selection process show that after balancing the dataset and dropping constant features, a noticeable improvement can be achieved. We conducted our evaluation found on eight major unique scenarios. The experimental results of our phishing websites detection (PWD) method indicate remarkable performances in which each algorithm reached an accuracy of more than 93%, and the XGBoost classifier outperforms others with 99.2% overall accuracy, 99.1% precision, 99.4% recall, and 99.1% specificity. In addition, the study achieved optimal run-time of about 1500 ms for the XGBoost algorithm without dimension reduction while using Principal Component Analysis (PCA) reduces it down to just 869 ms. As a result, the proposed approach would be practical in both offline and real-time applications.