Authors :
Vedav K S; Koushik Nayak U; A Mukesh; Karthik V; Soumya Patil
Volume/Issue :
Volume 7 - 2022, Issue 12 - December
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3vIWBLd
DOI :
https://doi.org/10.5281/zenodo.7511267
Abstract :
The risk of network information insecurity is
growing rapidly in number and level of risk is very high.
The methods mostly used by hackers today is to attack
whole system and exploit human vulnerabilities. These
techniques include social engineering, phishing,
pharming, etc. One of the steps in conducting these
attacks is to deceive users with fake Uniform Resource
Locators (URLs). As a result, fake URL detection is of
great interest nowadays. There have been several
scientific studies showing a number of methods to detect
malicious URLs based on machine learning and deep
learning techniques. In this paper, we propose a Fake
URL detection method using machine learning techniques
based on our proposed URL behaviours and attributes.
Moreover, bigdata technology is also exploited to improve
the capability of detection malicious URLs based on
abnormal behaviours. In short, the proposed detection
system consists of a new set of URLs features and
behaviours, a machine learning algorithm, and a bigdata
technology. The experimental results show that the
proposed URL attributes and behaviour can help improve
the ability to detect malicious URL significantly. This is
suggested that the proposed system may be considered as
an optimized and friendly used solution for malicious
URL detection.
Keywords :
URL; Malicious URL Detection; Phishing; Machine Learning
The risk of network information insecurity is
growing rapidly in number and level of risk is very high.
The methods mostly used by hackers today is to attack
whole system and exploit human vulnerabilities. These
techniques include social engineering, phishing,
pharming, etc. One of the steps in conducting these
attacks is to deceive users with fake Uniform Resource
Locators (URLs). As a result, fake URL detection is of
great interest nowadays. There have been several
scientific studies showing a number of methods to detect
malicious URLs based on machine learning and deep
learning techniques. In this paper, we propose a Fake
URL detection method using machine learning techniques
based on our proposed URL behaviours and attributes.
Moreover, bigdata technology is also exploited to improve
the capability of detection malicious URLs based on
abnormal behaviours. In short, the proposed detection
system consists of a new set of URLs features and
behaviours, a machine learning algorithm, and a bigdata
technology. The experimental results show that the
proposed URL attributes and behaviour can help improve
the ability to detect malicious URL significantly. This is
suggested that the proposed system may be considered as
an optimized and friendly used solution for malicious
URL detection.
Keywords :
URL; Malicious URL Detection; Phishing; Machine Learning
