Identifying Malicious Web Domains Using Machine Learning Techniques with Online Credibility and Performance Data

02/23/2019
by   Zhongyi Hu, et al.
0

Malicious web domains represent a big threat to web users' privacy and security. With so much freely available data on the Internet about web domains' popularity and performance, this study investigated the performance of well-known machine learning techniques used in conjunction with this type of online data to identify malicious web domains. Two datasets consisting of malware and phishing domains were collected to build and evaluate the machine learning classifiers. Five single classifiers and four ensemble classifiers were applied to distinguish malicious domains from benign ones. In addition, a binary particle swarm optimisation (BPSO) based feature selection method was used to improve the performance of single classifiers. Experimental results show that, based on the web domains' popularity and performance data features, the examined machine learning techniques can accurately identify malicious domains in different ways. Furthermore, the BPSO-based feature selection procedure is shown to be an effective way to improve the performance of classifiers.

READ FULL TEXT
research
10/19/2018

Malicious Web Domain Identification using Online Credibility and Performance Data by Considering the Class Imbalance Issue

Purpose: Malicious web domain identification is of significant importanc...
research
11/07/2017

Contaminant Removal for Android Malware Detection Systems

A recent report indicates that there is a new malicious app introduced e...
research
05/12/2023

The Case for the Anonymization of Offloaded Computation

Computation offloading (often to external computing resources over a net...
research
02/19/2020

Detection and Analysis of Drive-by Downloads and Malicious Websites

A drive by download is a download that occurs without users action or kn...
research
03/04/2022

In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis

One of the burning problems lately in Japan has been cyber-bullying, or ...
research
07/06/2023

How word semantics and phonology affect handwriting of Alzheimer's patients: a machine learning based analysis

Using kinematic properties of handwriting to support the diagnosis of ne...
research
06/02/2020

Less is More: Robust and Novel Features for Malicious Domain Detection

Malicious domains are increasingly common and pose a severe cybersecurit...

Please sign up or login with your details

Forgot password? Click here to reset