Phishing Website Detection Research Articles

Deep Learning Based Phishing Websites Detection

Phishing is a crime that involves the theft of confidential user information. Those targeted by phishing websites include individuals, small businesses, cloud storage providers, and government organisations and websites. The majority of phishing prevention techniques involve hardware-based solutions, although software-based options are preferred due of cost and operational considerations. There is no answer to the problem of zero-day phishing assaults from the present phishing detection approaches since there is no solution to the problem. The Phishing Attack Detector based on Web Crawler, a three-phase attack detection system, was designed to handle these issues and accurately detect phishing incidences using a recurrent neural network in order to resolve these issues. It covers the input aspects of web traffic, web content, and Uniform Resource Locator (URL) based on the classification of phishing and non-phishing pages, as well as the output features of phishing and non-phishing pages.

Machine Learning and Deep Learning Based Phishing Websites Detection: The Current Gaps and Next Directions

There are many phishing websites detection techniques in literature, namely white-listing, black-listing, visual-similarity, heuristic-based, and others. However, detecting zero-hour or newly designed phishing website attacks is an inherent property of machine learning and deep learning techniques. By considering a promising solution of machine learning and deep learning techniques, researchers have made a great deal of effort to tackle the this problem, which persists due to attackers constantly devising novel strategies to exploit vulnerability or gaps in existing anti-phishing measures. In this study, an extensive effort has been made to rigorously review recent studies focusing on Machine Learning and Deep Learning Based Phishing Websites Detection to excavate the root cause of the aforementioned problems and offer suitable solutions. The study followed the significant criterion to search, download, and screen relevant studies, then to evaluate criterion-based selected studies. The findings show that significant research gaps are available in the rigorously reviewed studies. These gaps are mainly related to imbalanced dataset usage, improper selection of dataset source(s), the unjustified reason for using specific train-test dataset split ratio, scientific disputes on website features inclusion and exclusion, lack of universal consensus on phishing website lifespans and on what is defining a small dataset size, and run-time analysis issues. The study clearly presented a summary of the comparative analysis performed on each reviewed research work so that future researchers could use it as a structured guideline to develop a novel solution for anti-phishing website attacks.

Machine learning approach for phishing website detection : A literature survey

The past year saw our world afflicted by COVID-19 undergo a digital transformation which led to a majority of people and organizations gravitate towards the internet. A remote working environment complicated the pre-existent crisis of phishing where the vulnerable population incurred huge losses at the hands of internet miscreants. A phishing attack comprises an attacker that creates fake websites to fool users and steal client-sensitive data which may be in form of login, password, or credit card details. Timely detection of phishing attacks has become more crucial than ever. Hence in this paper, we provide a thorough literature survey of the various machine learning methods used for phishing detection. This thesis will discuss in detail, different approaches used by various authors over the past few years. This survey aims to identify and narrow down the best machine learning algorithms that can be adopted to develop a hybrid model which can be implemented to detect whether a website is legitimate or phishing in nature.

Detection of URL Based Phishing Websites Using Machine Learning in Django Framework

Abstract: In this modern world, Phishing website detection is one of the most critical tasks in the world. In the recent times, a lot of people have suffered phishing attack due to phishing website. Machine Learning plays an important role in prediction of phishing website in the network. The proposed method predicts the URL based phishing websites based on features and also gives maximum accuracy to predict the result. This method uses uniform resource locator (URL) features to detect. It identified features that phishing site URLs contain. The proposed method takes those features for phishing detection. Security of the phishing detection website is also a major concern which is solved by providing administration who can manage the phishing detection website. Keywords: Phishing site, Machine learning, Legitimate, Prediction

Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.

Lightweight URL Phishing Detection System Using SVM and Similarity

Abstract: As a criminal offense of employing technical method to thieve sensitive information of customers, phishing is presently an important risk facing the net, and losses because of phishing are developing regularly. function engineering is essential in phishing website detection answers; however, the accuracy of detection critically relies upon on earlier knowledge of features. furthermore, even though features extracted for one-of-a-kind dimensions are more comprehensive, a disadvantage is that extracting those functions calls for a massive amount of time. To address those barriers, we propose a multidimensional characteristic phishing detection method based on a quick detection approach by way of the usage of deep mastering (MFPD). within the first step, character sequence functions of the given URL are extracted and used for brief class by means of deep mastering, and this step does not require 1/3-party assistance or any prior know-how approximately phishing. inside the 2d step, we combine URL statistical functions, webpage code features, website text capabilities and the short classification end result of deep studying into multi-dimensional features. The technique can lessen the detection time for putting a threshold.

Phishing Websites Detection by Using Optimized Stacking Ensemble Model

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent.

PhishSim: Aiding Phishing Website Detection With a Feature-Free Tool

In this paper, we propose a feature-free method for detecting phishing websites using the Normalized Compression Distance (NCD), a parameter-free similarity measure which computes the similarity of two websites by compressing them, thus eliminating the need to perform any feature extraction. It also removes any dependence on a specific set of website features. This method examines the HTML of webpages and computes their similarity with known phishing websites, in order to classify them. We use the Furthest Point First algorithm to perform phishing prototype extractions, in order to select instances that are representative of a cluster of phishing webpages. We also introduce the use of an incremental learning algorithm as a framework for continuous and adaptive detection without extracting new features when concept drift occurs. On a large dataset, our proposed method significantly outperforms previous methods in detecting phishing websites, with an AUC score of 98.68%, a high true positive rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of 0.58%. Our approach uses prototypes, eliminating the need to retain long term data in the future, and is feasible to deploy in real systems with a processing time of roughly 0.3 seconds.

Sufficiency of Ensemble Machine Learning Methods for Phishing Websites Detection

Phishing is a kind of worldwide spread cybercrime that uses disguised websites to trick users into downloading malware or providing personally sensitive information to attackers. With the rapid development of artificial intelligence, more and more researchers in the cybersecurity field utilize machine learning and deep learning algorithms to classify phishing websites. In order to compare the performances of various machine learning and deep learning methods, several experiments are conducted in this study. According to the experimental results, ensemble machine learning algorithms stand out among other candidates in both detection accuracy and computational consumption. Furthermore, the ensemble architectures still provide impressive capability when the amount of features decreases sharply in the dataset. Subsequently, the paper discusses the factors why ensemble machine learning methods are more suitable for the binary phishing classification challenge in up-date training and real-time detecting environment, which reflects the sufficiency of ensemble machine learning methods in anti-phishing techniques.

A Deep Learning-Based Framework for Phishing Website Detection

Phishing attackers spread phishing links through e-mail, text messages, and social media platforms. They use social engineering skills to trick users into visiting phishing websites and entering crucial personal information. In the end, the stolen personal information is used to defraud the trust of regular websites or financial institutions to obtain illegal benefits. With the development and applications of machine learning technology, many machine learning-based solutions for detecting phishing have been proposed. Some solutions are based on the features extracted by rules, and some of the features need to rely on third-party services, which will cause instability and time-consuming issues in the prediction service. In this paper, we propose a deep learning-based framework for detecting phishing websites. We have implemented the framework as a browser plug-in capable of determining whether there is a phishing risk in real-time when the user visits a web page and gives a warning message. The real-time prediction service combines multiple strategies to improve accuracy, reduce false alarm rates, and reduce calculation time, including whitelist filtering, blacklist interception, and machine learning (ML) prediction. In the ML prediction module, we compared multiple machine learning models using several datasets. From the experimental results, the RNN-GRU model obtained the highest accuracy of 99.18%, demonstrating the feasibility of the proposed solution.

Edge Colouring of Neutrosophic Graphs and Its Application in Detection of Phishing Website

Graph colouring enjoys many practical as well as theoretical uses. Graph colouring is still a very active subject of research. This article introduces a new concept of the chromatic number of the neutrosophic graph (NG). There is a discussion of the difference between the fuzzy colouring of the fuzzy graph (FG) and the NG. Finally, a real‐life application for detecting phishing websites has been demonstrated by NG colouring.

PDGAN: Phishing Detection With Generative Adversarial Networks

Phishing is a harmful online attack that could lead to identity theft and financial damages. The demand for high-accuracy phishing detection tools has risen due to the increase of online electronic services and payment systems. Most phishing detection techniques depend on features related to webpage content, which necessitates crawling the webpage and relying on third-party services. Relying on features related to webpage content could not provide high detection accuracy and leads to high false detection rates. Recently, deep learning has become a popular approach for detecting phishing websites. However, limited attention has been given to the generative adversarial network (GAN). This paper proposes a phishing detection model called PDGAN that depends only on a website’s uniform resource locator (URL) to achieve reliable performance. We use a long short-term memory network (LSTM) network as a generator of synthetic phishing URLs and a convolutional neural network (CNN) as a discriminator to decide whether the URLs are phishing or legitimate. We use a dataset containing nearly two million phishing and legitimate URLs obtained through PhishTank and DomCop. The experimental results show that the PDGAN achieves a detection accuracy of 97.58% and a precision of 98.02% without depending on third-party services and with greater accuracy than the state-of-the-art models.

HDP-CNN: Highway deep pyramid convolution neural network combining word-level and character-level representations for phishing website detection

Phishing has become a prevailing method for attackers to steal users’ private data and commit fraud, posing a serious threat to Internet users. How to detect phishing websites has attracted great interests from both academia and industry. A popular approach is to use support vector machine (SVM) to detect phishing websites. However, this approach relies on extracting features designated by experts, and the prediction effectiveness of the model is greatly affected by the quality of feature extraction. In addition, it cannot handle features that are not identifiable. Deep learning methods therefore become popular as they do not require manual feature engineering. However, many deep learning methods can only learn feature information of uniform resource locators (URLs) at the character level, while ignoring the intrinsic connections of words. To address these limitations, we propose a novel highway deep pyramid convolution neural network (HDP-CNN), a deep convolutional network that combines character-level and word-level representation information. HDP-CNN first receives the URL string sequences as input, then performs character-level embedding and word-level embedding respectively. Afterward, it uses the Highway network to connect the character-level embedding representation and word-level embedding representation of the URL and extracts local features of different sizes from the region embedding layer. Finally, it passes them into the designed deep pyramid structure network to capture the global representation of the URL. Our experiments illustrate that the information expressed by embedding vectors of different granularities has subtle differences. By combining embedding feature information of different granularities, HDP-CNN exhibits better performance than methods based on single embedding feature information. In our experiments, we construct an imbalanced dataset that has the ratio of benign websites to phishing websites is close to 5:1. The experimental results demonstrate that our method outperforms other methods, with accuracy at 98.30%, true positive rate (TPR) at 99.18%, and true negative rate (TNR) at 94.34%.

Phishing Website Detection Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Phishing has become one of the biggest and most effective cyber threats, causing hundreds of millions of dollars in losses and millions of data breaches every year. Currently, anti-phishing techniques require experts to extract phishing sites features and use third-party services to detect phishing sites. These techniques have some limitations, one of which is that extracting phishing features requires expertise and is time-consuming. Second, the use of third-party services delays the detection of phishing sites. Hence, this paper proposes an integrated phishing website detection method based on convolutional neural networks (CNN) and random forest (RF). The method can predict the legitimacy of URLs without accessing the web content or using third-party services. The proposed technique uses character embedding techniques to convert URLs into fixed-size matrices, extract features at different levels using CNN models, classify multi-level features using multiple RF classifiers, and, finally, output prediction results using a winner-take-all approach. On our dataset, a 99.35% accuracy rate was achieved using the proposed model. An accuracy rate of 99.26% was achieved on the benchmark data, much higher than that of the existing extreme model.

Phisher Fighter: Website Phishing Detection System Based on URL and Term Frequency-Inverse Document Frequency Values

Fundamentally, phishing is a common cybercrime that is indulged by the intruders or hackers on naive and credible individuals and make them to reveal their unique and sensitive information through fictitious websites. The primary intension of this kind of cybercrime is to gain access to the ad hominem or classified information from the recipients. The obtained data comprises of information that can very well utilized to recognize an individual. The purloined personal or sensitive information is commonly marketed in the online dark market and subsequently these information will be bought by the personal identity brigands. Depending upon the sensitivity and the importance of the stolen information, the price of a single piece of purloined information would vary from few dollars to thousands of dollars. Machine learning (ML) as well as Deep Learning (DL) are powerful methods to analyse and endeavour against these phishing attacks. A machine learning based phishing detection system is proposed to protect the website and users from such attacks. In order to optimize the results in a better way, the TF-IDF (Term Frequency-Inverse Document Frequency) value of webpages is employed within the system. ML methods such as LR (Logistic Regression), RF (Random Forest), SVM (Support Vector Machine), NB (Naive Bayes) and SGD (Stochastic Gradient Descent) are applied for training and testing the obtained dataset. Henceforth, a robust phishing website detection system is developed with 90.68% accuracy.

Application of word embedding and machine learning in detecting phishing websites

Application of word embedding and machine learning in detecting phishing websites

Sustaining accurate detection of phishing URLs using SDN and feature selection approaches

Phishing is an online fraud that deceives visitors by impersonating a legitimate website to steal their confidential or personal information. This is a well-known form of cybercrime. With the aim of detecting phishing sites, several phishing site detection techniques have recently been created. However, it fails to achieve the desired goal and has a large number of drawbacks, including low accuracy, long learning curve, and low-power embedded hardware. For covering such drawbacks, this work proposes an efficient URLs Phishing detection technique. Our technique depends on Software Defined Network (SDN) technology, clustering and feature method, and Conventional Neural Network (CNN) algorithm. Feature selection technique is based on Recursive Feature Elimination (RFE) with Support Vector Machine (SVM) algorithm. The SDN is used to transfer the URLs phishing detection process out of the user's hardware to the controller layer, continuously train on new data, and then send its outcomes to the SDN-Switches. RFE-SVM and CNN are used to increase accuracy of phishing detection. Therefore, the proposal model does not require retrieving the content of the target website or using any third-party services. It captures the information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features to quickly classify the actual URL. The experimental results showed that our proposal highlighted the robustness and accuracy in distinguishing between phishing and legitimate sites. Our suggestion achieves 99.5% phishing detection accuracy.

Intelligent Ensemble Learning Approach for Phishing Website Detection Based on Weighted Soft Voting

The continuous development of network technologies plays a major role in increasing the utilization of these technologies in many aspects of our lives, including e-commerce, electronic banking, social media, e-health, and e-learning. In recent times, phishing websites have emerged as a major cybersecurity threat. Phishing websites are fake web pages that are created by hackers to mimic the web pages of real websites to deceive people and steal their private information, such as account usernames and passwords. Accurate detection of phishing websites is a challenging problem because it depends on several dynamic factors. Ensemble methods are considered the state-of-the-art solution for many classification tasks. Ensemble learning combines the predictions of several separate classifiers to obtain a higher performance than a single classifier. This paper proposes an intelligent ensemble learning approach for phishing website detection based on weighted soft voting to enhance the detection of phishing websites. First, a base classifier consisting of four heterogeneous machine-learning algorithms was utilized to classify the websites as phishing or legitimate websites. Second, a novel weighted soft voting method based on Kappa statistics was employed to assign greater weights of influence to stronger base learners and lower weights of influence to weaker base learners, and then integrate the results of each classifier based on the soft weighted voting to differentiate between phishing websites and legitimate websites. The experiments were conducted using the publicly available phishing website dataset from the UCI Machine Learning Repository, which consists of 4898 phishing websites and 6157 legitimate websites. The experimental results showed that the suggested intelligent approach for phishing website detection outperformed the base classifiers and soft voting method and achieved the highest accuracy of 95% and an Area Under the Curve (AUC) of 98.8%.

HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks

Internet users have suffered from phishing attacks for a long time. Attackers deceive users through malicious constructed phishing websites to steal sensitive information, such as bank account numbers, website usernames, and passwords. In recent years, many phishing detection solutions have been proposed, which mainly leverage whitelists or blacklists, website content, or side channel-based techniques. However, with the continuous improvement of phishing technology, current methods have difficulty in achieving effective detection. Hence, in this paper, we propose an effective phishing website detection approach, which we call HinPhish. HinPhish extracts various link relationships from webpages and uses domains and resource objects to construct a heterogeneous information network. HinPhish applies a modified algorithm to leverage the characteristics of different link types in order to calculate the phish-score of the target domain on the webpage. Moreover, HinPhish not only improves the accuracy of detection, but also can increase the phishing cost for attackers. Extensive experimental results demonstrate that HinPhish can achieve an accuracy of 0.9856 and F1-score of 0.9858.

Detecting phishing websites using machine learning technique.

In recent years, advancements in Internet and cloud technologies have led to a significant increase in electronic trading in which consumers make online purchases and transactions. This growth leads to unauthorized access to users’ sensitive information and damages the resources of an enterprise. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. In terms of website interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Various strategies for detecting phishing websites, such as blacklist, heuristic, Etc., have been suggested. However, due to inefficient security technologies, there is an exponential increase in the number of victims. The anonymous and uncontrollable framework of the Internet is more vulnerable to phishing attacks. Existing research works show that the performance of the phishing detection system is limited. There is a demand for an intelligent technique to protect users from the cyber-attacks. In this study, the author proposed a URL detection technique based on machine learning approaches. A recurrent neural network method is employed to detect phishing URL. Researcher evaluated the proposed method with 7900 malicious and 5800 legitimate sites, respectively. The experiments’ outcome shows that the proposed method’s performance is better than the recent approaches in malicious URL detection.