Detecting Phishing Websites using Machine Learning Project

Detecting phishing websites is essential for making sure the network privacy. We develop a well-defined framework that prevents users from fraudulent websites aiming to steal their personal information. So, hurry up and book yours lots now to get expertise solution for all your research work. Research Proposal will be framed under experts care a detailed plan flow will be explained of the methodology we will be using, further if you are not satisfied, we can remodel it as per your customized requirement. We serve as an one point solution for all your research needs.

Here are the steps to be followed to setup a phishing website detection project using machine learning (ML):

  1. Objective Definition:
  • We detect when a website URL and its attributes are symbolizing of a phishing threat.
  1. Data Collection:
  • Various datasets for phishing URLs and legitimate URLs which we can access from the environments such as Kaggle and UCI ML repository.
  • Features that we include URL length, IP address, domain registration length, special symbols, etc.
  1. Data Pre-processing:
  • Feature Extraction from URLs: When we begin with open URLs for retrieving informative features like domain name, path length, number of subdomains, etc.
  • Encoding: We change categorical features into numerical approach using one-hot encoding and label encoding.
  • Handling Missing Values: To maintain the lost data we use suggestion approaches and remove records with missing values.
  • Feature Measuring: Making sure that the feature has relevant scales we normalize for distance-based methods like KNN.
  1. Feature Engineering:
  • We design latest features that used to indicate phishing such as a feature finds usual phishing terms in the URL.
  1. Model Selection and Training:
  • Logistic Regression: This can be assisting us in simplicity and understandability.
  • Decision Trees & Random Forest: For capturing non-linear formats we utilize this framework.
  • Gradient Boosting Machine (e.g. XGBoost): To enhance the accuracy we implement boosted trees.
  • Neural Networks: When there is a huge volume of data we ensure that the deep learning is an excess model for our issue.
  1. Evaluation:
  • Accuracy: We test how often the framework is appropriate.
  • Precision: For better precision we evaluate proportion of positive findings that were actually perfect.
  • Recall: To validate the proportion of real positives detected properly in our work.
  • F1-Score: Balancing the precision and recall.
  • ROC Curve & AUC: When the classes are imbalance we make use of this method.
  1. Deployment:
  • Integrate our project into browser extensions, firewall outcomes and as a separate service to check URLs.
  • To retrieve fast outcomes from we ensure low latency.
  1. Monitoring:
  • Routinely we update the framework with recent data to handle its accuracy.
  • We figure out false positives and false negatives to retrain out model.


  • Dynamic Nature of Phishing Sites: Phishing websites continuously alter to avoid detection so we make some alternative for this.
  • Feature Redundancy: When we extract the features not entire feature will provide information to us.
  • Class Imbalance: There are several more legitimate URLs which we come across than phishing ones.

Extensions & Advanced Methods:

  • Deep Learning on Raw URLs: To alter URLs into dense vectors and for training our model in neural network we implement embedding.
  • Active Learning: When a user marks a site as phishing, we learn from the user review which model is missed from that instance.
  • Integrate with Other Data: We collaborate with data from website content, SSL certificates and WHOIS database for richer features.

     Combining with cybersecurity professionals can provide domain-specific understandings to us and makes the framework more powerful. We focus for a high recall while balancing accuracy in the real-time scenario, because the consequences of false negatives (not identifying a phishing website) are remarkable.

Detecting Phishing Websites using Machine Learning Project Thesis Ideas

The best thesis topics that we have built are described below get inspired by our professional’s work. As per your specifications we work out your research activities or we do take care and undertake your entire research work.

Detecting Phishing Websites Using Machine Learning Project Ideas
  1. Phishing Website Detection Using Machine Learning


Phishing, Personal Information, Machine Learning, Malicious Links, Phishing Domain Characteristics

            An implementation of features and ML techniques are suggested in our paper to detect the phishing attempts. Here we described the differentiation of phishing domains or illegitimate domains from the legitimate domains. We also explained about the significance of detecting phishing content. We investigated about the utilization of ML techniques and natural language processing approaches in our study.

  1. Phishing Website Detection with and Without Proper Feature Selection Techniques: Machine Learning Approach


Cat-Boost, Phishing website detection, PCA, UFS, RFE, MI, PCC, Feature selection technique

            An investigation on various ML methods before and after utilizing several Feature Selection (FS) approaches is carried out in our research. We demonstrated that, the Cat-Boost method achieved better results after utilizing UFS approach for FS. But when utilizing PCA approach for FS, methods like Cat-Boost, Gradient-Boost, and RF are unable to improve the accuracy. We conclude that, in future, utilization of integrated FS approach, DL technique and hyper parameters assist to attain successful outcomes in phishing website identification.

  1. Detection of Phishing Website Using Support Vector Machine and Light Gradient Boosting Machine Learning Algorithms


ELM, SVM, Light GBM algorithm

            An innovative technique is proposed by utilizing extreme learning machine (ELM) in our article to categorize the phishing websites. To identify the phishing websites by considering the size of the URL and the existence of capital letters and HTML attributes, we employed SVM, and light GBM techniques. Results illustrates that, ELM method provides greater efficiency in phishing websites categorization and also enhance the user’s security.   

  1. Intelligent phishing website detection using machine learning


Logistic regression, MultinomialNB, Phishing websites, Classification

            A development of approach to distinguish and identify the phishing websites from the legitimate websites is the major aim of our study.  Apart from Random Forest, Artificial Neural Network and SVM methods, we utilized Linear Regression and MultinomialNB as main techniques for categorization. Development of real time working framework is the main goal of our study. As a consequence, Linear Regression offers highest end results. 

  1. A Novel Phishing Website Detection Model Based on LightGBM and Domain Name Features


Domain name feature, symmetry, feature engineering

            A ML related technique is suggested in our research for phishing website detection and to handle the smart system’s safety by utilizing LightGBM and features of domain name. We extracted the features from domain name of acquired website. We filtered the features to enhance the precision of framework and to ease the categorization process. We conclude that, our suggested framework with combination of two features provides efficient outcomes.

  1. Detecting Phishing Websites Using Machine Learning


Phishing detection, Random Forest

            An ultimate goal of our study is to discover whether the URL is secured or not by employing ML techniques. We utilized various methods like Logistic Regression, Support Vector Machine (SVM), and Random Forest. We examined and compared these methods in terms of various metrics to find out the optimal one that can identify and categorize the secured and phishing websites.

  1. Determining the Most Effective Machine Learning Techniques for Detecting Phishing Websites


Web security, Decision tree, Gradient boost classifiers

            Several ML approaches are analyzed and evaluated in our paper to discover the best method for the detection of phishing websites. We utilized various methods including RF, GB, DT, LR, KNN and SVM for phishing website identification. From the analysis, we demonstrate that, RF method achieved better performance than others and DT and GB provide somewhat identical results.

  1. Website Phishing Detection Using Machine Learning Classification Algorithms


URL features, Data mining, Classification algorithms

            A URL feature related website phishing identification methodology is recommended in our study to forecast the illegitimate websites. For identifying fake websites, we investigated several ML techniques such as extreme gradient boosting, random forest, AdaBoost, decision trees, K-nearest neighbors, support vector machines, logistic regression and naïve bayes. As a consequence, extreme gradient boosting method efficiently categorizes websites than others.

  1. Modeling Hybrid Feature-Based Phishing Websites Detection Using Machine Learning Techniques


Cybersecurity, Hyperlink feature, Anti-phishing, XG Boost, Hybrid feature

            A real time phishing websites identification framework is proposed in our article by considering URL and hyperlink related integrated features through the utilization of ML methods. Here, by utilizing an integrated feature related anti-phishing concept, we extracted features from URL and hyperlink data. We performed experimental analysis by employing various ML methods. In that, XGBoost achieved better end results than other conventional methods.

  1. Detecting Phishing Websites Using Machine Learning


Legitimate websites, features, detection

            A main objective of our research is to identify phishing or illegitimate websites through the employment of ML techniques. We applied various approaches such as Decision Tree (DT), Random Forest (RF), XGBoost, Multilayer Perceptron, K-Nearest Neighbors, Naive Bayes, AdaBoost, and Gradient Boosting on the dataset comprises of equal amount of safe and fake URLs. As a result, XGBoost outperformed the others.


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