Phishing Website Detection Using Machine Learning Project

Phishing Website Detection Using Machine Learning Thesis Topics

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

Keywords:

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

Keywords:

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

Keywords:

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

Keywords:

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

Keywords:

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

Keywords:

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

Keywords:

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

Keywords:

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

Keywords:

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

percent.

Keywords

Detection

            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.