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Masquerade Detection Using Machine Learning
Abstract: The Internet has become an indispensable part of our life, However, It also has provided opportunities to anonymously perform malicious activities like Phishing. Phishers try to deceive their victims by social engineering or creating mockup websites to steal information such as account ID, username, password from individuals and organizations. Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. One of the most successful methods for detecting these malicious activities is Machine Learning. This is because most Phishing attacks have some common characteristics which can be identified by machine learning methods. In this paper, we compared the results of multiple machine learning methods for predicting phishing websites.
Notices, Deadlines & Correspondence
Patent Information
Applicants
Revathi
Inventors
1. S.Shruthi
2. S.Shruthi
3. Dr.G.Kumaresan
4. Revathi
Specification
Description:The design of masquerade detection using machine learning,covers the modules like Data Preprocessing and Validation,Data Visualization,Logistic Regression,Random Forest,Decision Tree ,Naïve bayes,Support Vector Classifier,K Nearest neigbour.
The data validation process a visualization is used to customize the raw data with
the help of data science techniques.
The Logistic regression algorithm comparison is the process of checking accuracy of Logistic regression from the dataset with the help of machine learning algorithm.
The Random Forest algorithm comparison is the process of checking accuracy of Random Forest from the dataset with the help of machine learning algorithm.
The Decision Tree algorithm comparison is the process of checking accuracy of Random Forest from the dataset with the help of machine learning algorithm.
The Naive Bayes algorithm comparison is the process of checking accuracy of Naive Bayes from the dataset with the help of machine learning algorithm.
The Support Vector Classifier algorithm comparison is the process of checking accuracy Support Vector Classifier from the dataset with the help of machine learning algorithm.
The K Nearest Neighbour algorithm comparison is the process of checking accuracy K Nearest Neighbour from the dataset with the help of machine learning algorithm.
Finally the overall process is deployed in FLASK using Python
, Claims:• With respect to existing system, our model of prediction has produced more accurate results.
• Accuracy obtained in Logistic regression is 82.8 %
• Accuracy obtained in Random Forest is 89.57%.
• Accuracy obtained in Decision tree is 87.2%
• Accuracy obtained in Naive Bayes is 81.8%.
• Accuracy obtained in Support Vector Classifier is 86.6%.
• Accuracy obtained in K Nearest Neighbour is 87.1%
Documents
Application Documents
| # | Name | Date |
|---|---|---|
| 1 | 202241028131-FER.pdf | 2022-09-14 |
| 1 | 202241028131-STATEMENT OF UNDERTAKING (FORM 3) [16-05-2022(online)].pdf | 2022-05-16 |
| 2 | 202241028131-COMPLETE SPECIFICATION [16-05-2022(online)].pdf | 2022-05-16 |
| 2 | 202241028131-REQUEST FOR EXAMINATION (FORM-18) [16-05-2022(online)].pdf | 2022-05-16 |
| 3 | 202241028131-DRAWINGS [16-05-2022(online)].pdf | 2022-05-16 |
| 3 | 202241028131-REQUEST FOR EARLY PUBLICATION(FORM-9) [16-05-2022(online)].pdf | 2022-05-16 |
| 4 | 202241028131-FORM 1 [16-05-2022(online)].pdf | 2022-05-16 |
| 5 | 202241028131-DRAWINGS [16-05-2022(online)].pdf | 2022-05-16 |
| 5 | 202241028131-REQUEST FOR EARLY PUBLICATION(FORM-9) [16-05-2022(online)].pdf | 2022-05-16 |
| 6 | 202241028131-COMPLETE SPECIFICATION [16-05-2022(online)].pdf | 2022-05-16 |
| 6 | 202241028131-REQUEST FOR EXAMINATION (FORM-18) [16-05-2022(online)].pdf | 2022-05-16 |
| 7 | 202241028131-FER.pdf | 2022-09-14 |
| 7 | 202241028131-STATEMENT OF UNDERTAKING (FORM 3) [16-05-2022(online)].pdf | 2022-05-16 |
Search Strategy
| 1 | SearchStrategyE_08-09-2022.pdf |