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Detection of Phishing URL using Ensemble Learning Techniques

Parmar, Sharad Rajendra (2020) Detection of Phishing URL using Ensemble Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Phishing is one of the prevailing means of performing cyber-attacks. Spoofed email, social media, development of clone website are the main medium used by various phishers in order to steal the private information of an individual. Uniform Resource Locators (URLs) are the main source for sharing malwares, trojans and false information. Therefore, the accurate classification between legit and phishing url is very much important. Traditional methods of detecting phishing url were mainly rely on the blacklisting and signature based methods. Both of these methods are time consuming process and can not work effectively on new set of URL. Many machine learning classifiers also have been used, to classify the URL as phishing or legit. But, with traditional machine learning approaches, low accurate results have been achieved. Therefore, in this work we propoosed the use of ensemble learning methods. Where we have used the Bagging, AdaBoost, Random Forest and Gradient boosting algorithms. Later, the results were compared with Non-ensemble learning algorithms such as Decision tree, K nearest neighbour and Logistic regression. After Training the models we have achieved a highest accuracy of 96.15% using random forest classifier, which is an ensemble learning method.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Dan English
Date Deposited: 27 Jan 2021 17:41
Last Modified: 27 Jan 2021 17:41

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