Authors :
Venkatesh Maduri; Ziaul Haque Choudhury
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/yvwnhdnw
Scribd :
http://tinyurl.com/bde25y3z
DOI :
https://doi.org/10.5281/zenodo.10474017
Abstract :
The internet has grown to be a significant part
of our daily lives for communication and dissemination of
information. It facilitates the unexpected and seamless
execution of knowledge. Understanding break-ins and
individual deception as different parts of bad behavior
mean that software engineers and bad customers get the
personal information of present good customers to try
deception or foxy motivation for unauthorized related
gain. Malignant URLs have unconstrained substance (like
junk mail, phishing, pressure-by-using exploits, and so
forth) and phishing the customer to turn out to be losses
from stunts (financial adversity, theft of personal facts,
and malware basis), furthermore, cause mishaps of bil-
lions of bucks reliably. To resolve the existing problem,
we have proposed the algorithm Linear Support Vector
Machine (LSVM) with One-against-approach and Convo-
lutional Neural Network (CNN). In this study, we use a
clear estimation to determine whether a URL is good or
bad. When compared to the existing study, our proposed
method attained more accuracy.
Keywords :
Malicious Detection, Cyber Security, Big Data, CNN, Linear SVM, URL Dataset, Classification.
The internet has grown to be a significant part
of our daily lives for communication and dissemination of
information. It facilitates the unexpected and seamless
execution of knowledge. Understanding break-ins and
individual deception as different parts of bad behavior
mean that software engineers and bad customers get the
personal information of present good customers to try
deception or foxy motivation for unauthorized related
gain. Malignant URLs have unconstrained substance (like
junk mail, phishing, pressure-by-using exploits, and so
forth) and phishing the customer to turn out to be losses
from stunts (financial adversity, theft of personal facts,
and malware basis), furthermore, cause mishaps of bil-
lions of bucks reliably. To resolve the existing problem,
we have proposed the algorithm Linear Support Vector
Machine (LSVM) with One-against-approach and Convo-
lutional Neural Network (CNN). In this study, we use a
clear estimation to determine whether a URL is good or
bad. When compared to the existing study, our proposed
method attained more accuracy.
Keywords :
Malicious Detection, Cyber Security, Big Data, CNN, Linear SVM, URL Dataset, Classification.
