Phishing Website Detection Using Machine Learning Project

Phishing Website Detection Using Machine Learning Project

Machine Learning methods can be used for detecting phishing websites by us and is one of the important regions in cybersecurity. In our work the phishing websites aim to impersonate legitimate sites to step out users by offering sensitive information like passwords or credit card numbers. To find these malicious websites machine learning aids on the basis of different features. Be with us we will solve all your thesis issues into a fruitful one. Stay in touch with our team we will constantly update our trending ideas.  Here we give a guidance to initialize a project in this region:

  1. Define your Objective:
  • Binary Classification: Is a given website phishing or legitimate classified by us.
  • Multi-class Classification: Legitimate, phishing or suspicious are the websites that are categorized into classes.
  1. Data Collection:

Our work gathers a dataset of URLs or website data, both legitimate and phishing.

  • Public Datasets: Several Datasets are available online. Example: The UCI Machine Learning Repository has a phishing website dataset.
  • Web Scraping: To scrape websites for data, our work incorporates tools like Scrapy or Beautiful Soup in Python.
  1. Feature Engineering:

Detects Features that aids to differentiate among phishing and legitimate websites. Some of the feature ideas are:

  • URL-Based Features:
  • URLs length.
  • Number of subfields.
  • Existence of doubtful tokens or characters.
  • Instead of field names we utilize IP addresses in the URL.
  • Content-Based Features:
  • Forms existence request for passwords or credit card details.
  • Number of external links is examined by us.
  • SSL feature details like issuer and expiration date.
  • Domain-Based Features:
  • Registration length of the field.
  • Age of the field.
  • Field reputation.
  • External Features:
  • In our work we check the reputation of the website by employing Google Safe Browsing API.
  • Rankings of web traffic.
  1. Model Selection and Training:

Different machine learning frameworks are investigated by us.

  • Traditional ML models: Our work uses some of the traditional ML frameworks like Decision Trees, Random Forest, Logistic Regression, Gradient Boosting Machines, SVM, etc.
  • Deep Learning: Methods in Deep Learning like Neural Networks are valuable to us; particularly we choose to identify the raw content or even visual representations of web pages.
  1. Evaluation:

Incorporating relevant metrics:

  • Accuracy, Precision, Recall, and F1-score are the metrics incorporated by us.
  • Our work uses the metrics like the ROC curve and AUC.
  1. Deployment:

Our model deploys as a service or combines into browser extensions or other privacy answers.  

  1. Continuous Learning:

            To make sure that our model is retrained over time with novel data that may involve the phishing approaches.

Project Ideas:

  1. Real-time Phishing Detection Browser Extension: To generate a browser extension that utilizes our trained framework to estimate websites in real-time.
  2. Phishing vs. Pharming Detection: We lengthen our project to differentiate among phishing and pharming attacks.
  3. Visual-Based Detection: Our work identifies precise variations among legitimate and phishing sites by extracting visual features like (logo placement, page layout).
  4. Ensemble Learning: Our work integrates multiple frameworks or incorporates approaches like stacking to enhance the overall accuracy.


  • Dynamic Content: In many latest websites load content dynamically, generating static analysis is difficult for us.
  • Data Privacy: Particularly if we identify user browsing data, make sure that we follow user security.
  • False Positives: Excessively aggressive frameworks block legitimate websites that can be very troublesome for users.

Our work continuously remembers to handle data responsibly, make sure user security, and follow ethical guidelines when working on such projects.

Phishing Website Detection Using Machine Learning Ideas

Phishing Website Detection Using Machine Learning Thesis Topics

                   Phishing Website Detection Using Machine Learning Thesis topics that we share for scholars will be catchy and have the right variables, allow us to help you in your research work. As our research team are always reliable, you will be always updated regarding your research work and can share with us any queries that you have, go through our topics where we have best ideas.

1. Privacy Preserving Secure and Efficient Detection of Phishing Websites Using Machine Learning Approach


Neural Networks, Deep Learning, Uniform Resource Locator Length, Recall, Precision

            Our work focuses on three-stage spoofing series to identify the difficulties. We used three input variables namely Uniform resource locators, circulation and internet content based on phishing attack and non-phishing website approach. We used classification accuracy for phishing recognition by utilizing ML based classification techniques like NN, SVM and RF. The result shows ML gives the good phishing detection. 

2. Website Phishing Detection of Machine Learning Approach using SMOTE method


SMOTE, Phishing, Legitimate, CatBoost, Random Forest and XGBoost

            Our paper uses Synthetic Minority Over-Sampling Technique (SMOTE) to balance the dataset. We proposed a SMOTE approach to find the genuine website from phishing sites. Our proposed technique performs better in binary-classification methods like CatBoost, Random Forest, XG Boost utilizing data from phish tank website’s dataset to find phishing website. Phishing effort and early detection both be helpful. CatBoost gives the better performance.   

3. Phish Me If You Can – Lexicographic Analysis and Machine Learning for Phishing Websites Detection with PHISHWEB


Phishing Websites, Lexicographic Analysis, DNS, Machine Learning

            Our paper uses a PHISHWEB to detect phishing website and classifies malicious websites along a progressive, multi-layered analysis. PHISHWEB’s detection involves forged domains namely homoglyph and typo squatting and automatically generated domain through DGA technology. The focus of PHISHWEB is on lexicographic based analysis to increase the scalability of the method and we also used ML based PHISHWEB detection of DGA domains. ML-prolongation of PHISHWEB increases non-ML PHISHWEB DGA.

4. Phishing Website Detection using Hyper-parameter Optimization and Comparison of Cross-validation in Machine Learning Based Solution


Cross validation, hyper parameter Optimisation

            To detect the phishing techniques successful we used ML and Anti-Phishing software. Hackers can develop new methods to overcome this. Our paper detects phishing websites utilises ML classifiers and combines various cross validation methods to get the high accuracy. At last we used Random Forest to get the better outcome. The phishing tank repository, collection of authentic and phishing websites are utilized to get the effectiveness of the proposed system.   

5. Phishing Website Detection using Machine Learning Techniques


Decision Tree

            The majority of the cyber-attacks spread along the methods that yield benefit of end user weakness and the security chain is weak. Many approaches have been utilized to safeguard various types of assaults since the complexity of phishing problem. We completely recognise the different type of phishing mitigation tactics, including detection, offensive defence, rectification and prevention are important to offer a high level

6. Machine Learning Technique for Phishing Website Detection


Phishing attack, website detection, malware

            In both our personal and professional life internet has appeared as a necessary tool. This can lead to purchase over internet can fastly improved.  Internet users can be sensitive in variety of web threats, these threats can effect on monetary loss, fraudulent in credit cards, personal data loss, potential change to brand’s reputation and online banking. We used ML methods to detect a phishing attack.     

7. A Comparative Analysis of Machine Learning-Based Website Phishing Detection Using URL Information


Website phishing attacks, information security, cybercriminals

            Anti- phasing ML methods used to find a legitimate website from a phishing website by retrieving various features from different sources namely URL, page content, search engine etc. Our paper presents a comparative analysis of ML based phishing detection. We have also compared five ML methods namely Decision tree, Random Forest, K neighbors, Gaussian Naïve Bayes and XGBoost. Random Forest gives the best performance. 

8. Logistic Regression based Machine Learning Technique for Phishing Website Detection


Logistic regression, online, E-commerce, security

            Our paper utilizes the ML based prediction method to analyse and predict the phishing websites. We can use classification methods and techniques to analyse and retrieve the datasets can cause phishing. The important characters are supportive to find type of phishing sites namely URL and encryption method that detect malicious data. We use a Logistic regression method to detect phishing website.

9. Phishing Websites Detection using Machine Learning with URL Analysis


URL Analysis, Multilayer Perceptron Algorithm

            Our paper uses URL’s as a dataset to detect phishing websites. We have to retrieve the features from the dataset and are used to verify that website is phishing or not. Eight machine learning methods were suggested for our work. Out of this Multilayer perceptron (MLP) achieves the better performance.

10. A comparitative study of machine learning models for the detection of Phishing Websites




            Hackers can use phishing techniques to induce company’s digital access and networks. Our aim is to propose a unique, robust machine learning method that provides high prediction accuracy with low error rate. Our random Forest method gives the increased accuracy. But we also implement a hybrid model with 3 classifiers namely Decision tree, random forest and gradient boosting classifiers to get increased accuracy.

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