USING MACHINE LEARNING TECHNIQUES TO DETECT MALICIOUS URLS FOR MOBILE USER

Abstract

The use of malicious URLs to exploit websites for the execution of criminal activities is a growing cybersecurity issue in contemporary cyber-space. Malicious URL developers aim to lure unsuspecting victims into scams or fraudulent activities. In most cases, the motive is for financial gain. Over the past years, these criminal activities have led to the loss of billions of dollars or the destruction of devices. Most often, internet users have employed traditional techniques to detect malicious URLs. However, due to the new technological advancements, the conventional methods of detection have been rendered ineffective increasing their susceptibility to an online scam. As a result, telecommunication engineers and website developers have continued to invest in research and development for advanced techniques that can overcome the vulnerability of traditional mechanisms. Notably, the use of anti-malicious software to caution online presence against exploitative and malicious URLs has proved ineffective against malicious injections. This is attributed to the increasing complexity of cyberattacks and cybercrimes. However, the adoption of different and advanced machine learning techniques that are strategically dynamic is increasingly proving effective to counter online criminal endeavors executed through malicious URLs. A mobile application is developed and has utilized machine learning to detect malicious URLs using logistic regression to predict good and bad URLs.

Date of Award	2020
Original language	American English
Awarding Institution	HBKU College of Science and Engineering