Javascript must be enabled to continue!
Prediction and Prevention of Malicious URL Using ML and LR Techniques for Network Security
View through CrossRef
Understandable URLs are utilized to recognize billions of websites hosted over the present-day internet. Opposition who tries to get illegal admittance to the classified data may use malicious URLs and present them as URLs to users. Such URLs that act as an entry for the unrequested actions are known as malicious URLs. These wicked URLs can cause unethical behavior like theft of confidential and classified data. By using machine learning algorithm SVM, we can detect the malicious URLs. One of the essential features is to permit the benevolent URLs that are demanded by the customer and avoid the malicious URLs. Blacklisting is one of the basic and trivial mechanisms in detecting malicious URLs.
Title: Prediction and Prevention of Malicious URL Using ML and LR Techniques for Network Security
Description:
Understandable URLs are utilized to recognize billions of websites hosted over the present-day internet.
Opposition who tries to get illegal admittance to the classified data may use malicious URLs and present them as URLs to users.
Such URLs that act as an entry for the unrequested actions are known as malicious URLs.
These wicked URLs can cause unethical behavior like theft of confidential and classified data.
By using machine learning algorithm SVM, we can detect the malicious URLs.
One of the essential features is to permit the benevolent URLs that are demanded by the customer and avoid the malicious URLs.
Blacklisting is one of the basic and trivial mechanisms in detecting malicious URLs.
Related Results
Information Security in Artificial Intelligence: A Study of the possible intersection
Information Security in Artificial Intelligence: A Study of the possible intersection
1. IntroductionArtificial Intelligence or A.I attempts to understand intelligent entities, and strives to build ones. And it is obvious that computers with human-level intelligence...
Construction of a Cybersecurity Behavior Knowledge Base for Malicious Behavior Analysis
Construction of a Cybersecurity Behavior Knowledge Base for Malicious Behavior Analysis
Facing the surge in malicious behaviors in the network environment, the existing cybersecurity knowledge graph suffers from fragmented security knowledge and limited application sc...
Construction of a Cybersecurity Behavior Knowledge Base for Malicious Behavior Analysis
Construction of a Cybersecurity Behavior Knowledge Base for Malicious Behavior Analysis
Facing the surge in malicious behaviors in the network environment, the existing cybersecurity knowledge graph suffers from fragmented security knowledge and limited application sc...
Persistence and half‐life of URL citations cited in LIS open access journals
Persistence and half‐life of URL citations cited in LIS open access journals
PurposeThe main purpose of the present study is to examine the availability and persistence of URL citations in two LIS open access journals. It also intended to calculate the half...
An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection
An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection
Abstract
Nowadays, machine learning is used in many fields. Not only in fields such as image recognition, machine learning is also used for malicious detection. Especially ...
Localisation of Attacks, Combating Browser-Based Geo-Information and IP Tracking Attacks
Localisation of Attacks, Combating Browser-Based Geo-Information and IP Tracking Attacks
<p>Accessing and retrieving users’ browser and network information is a common practice used by advertisers and many online services to deliver targeted ads and explicit impr...
Digitalization of the Russian general education system: Current state and problems
Digitalization of the Russian general education system: Current state and problems
Nazarov D.M. [Economy 2.0: neoclassics, digital transformation and evolutionary economy]. Izvestiya Sankt-Peterburgskogo gosudarstvennogo ekonomicheskogo universiteta, 2023, no. 4,...
Deep Learning-Driven Malicious URL Detection: A comprehensive analysis using Convolutional Neural Networks A feature engineering on the Phiusiil dataset
Deep Learning-Driven Malicious URL Detection: A comprehensive analysis using Convolutional Neural Networks A feature engineering on the Phiusiil dataset
Malicious URLs are a significant cybersecurity threat, which promotes phishing, malware downloading, and data breach that jeopardize the security of millions of users worldwide. Co...

