Biblio

During pandemic COVID-19 outbreaks, number of cyber-attacks including phishing activities have increased tremendously. Nowadays many technical solutions on phishing detection were developed, however these approaches were either unsuccessful or unable to identify phishing pages and detect malicious codes efficiently. One of the downside is due to poor detection accuracy and low adaptability to new phishing connections. Another reason behind the unsuccessful anti-phishing solutions is an arbitrary selected URL-based classification features which may produce false results to the detection. Therefore, in this work, an intelligent phishing detection and prevention model is designed. The proposed model employs a self-destruct detection algorithm in which, machine learning, especially supervised learning algorithm was used. All employed rules in algorithm will focus on URL-based web characteristic, which attackers rely upon to redirect the victims to the simulated sites. A dataset from various sources such as Phish Tank and UCI Machine Learning repository were used and the testing was conducted in a controlled lab environment. As a result, a chrome extension phishing detection were developed based on the proposed model to help in preventing phishing attacks with an appropriate countermeasure and keep users aware of phishing while visiting illegitimate websites. It is believed that this smart phishing detection and prevention model able to prevent fraud and spam websites and lessen the cyber-crime and cyber-crisis that arise from year to year.

A browser extension, also known as a plugin or an addon, is a small software application that adds functionality to a web browser. However, security threats are always linked with such software where data can be compromised and ultimately trust is broken. The proposed research work jas developed a security model named WebSecAsst, which is a chrome plugin relying on the Machine Learning model XGBoost and VirusTotal to detect malicious websites visited by the user and to detect whether the files downloaded from the internet are Malicious or Safe. During this detection, the proposed model preserves the privacy of the user's data to a greater extent than the existing commercial chrome extensions.