Visible to the public Phishing Attack Detection using Machine Learning Classification Techniques

TitlePhishing Attack Detection using Machine Learning Classification Techniques
Publication TypeConference Paper
Year of Publication2020
AuthorsAbedin, N. F., Bawm, R., Sarwar, T., Saifuddin, M., Rahman, M. A., Hossain, S.
Conference Name2020 3rd International Conference on Intelligent Sustainable Systems (ICISS)
Date PublishedDec. 2020
ISBN Number978-1-7281-7089-3
Keywordsartificial intelligence, Classification algorithms, composability, Computer science, Conferences, Decision Tree, defense, Logistics, machine learning, Metrics, phishing, phishing attack, phishing attack detection, pubcrawl, resilience, Resiliency, Training, Uniform resource locators, Zero day attacks

Phishing attacks are the most common form of attacks that can happen over the internet. This method involves attackers attempting to collect data of a user without his/her consent through emails, URLs, and any other link that leads to a deceptive page where a user is persuaded to commit specific actions that can lead to the successful completion of an attack. These attacks can allow an attacker to collect vital information of the user that can often allow the attacker to impersonate the victim and get things done that only the victim should have been able to do, such as carry out transactions, or message someone else, or simply accessing the victim's data. Many studies have been carried out to discuss possible approaches to prevent such attacks. This research work includes three machine learning algorithms to predict any websites' phishing status. In the experimentation these models are trained using URL based features and attempted to prevent Zero-Day attacks by using proposed software proposal that differentiates the legitimate websites and phishing websites by analyzing the website's URL. From observations, the random forest classifier performed with a precision of 97%, a recall 99%, and F1 Score is 97%. Proposed model is fast and efficient as it only works based on the URL and it does not use other resources for analysis, as was the case for past studies.

Citation Keyabedin_phishing_2020