Malicious URLs Detection Using Data Streaming Algorithms

Jurnal Teknologi dan Sistem Komputer

View Publication Info
Field Value
Title Malicious URLs Detection Using Data Streaming Algorithms
Creator Adewole, Kayode Sakariyah
Raheem, Muiz Olalekan
Abdulraheem, Muyideen
Oladipo, Idowu Dauda
Balogun, Abdullateef Oluwagbemiga
Baker, Omotola Fatimah
Subject Data streaming; Phishing; Naïve Bayes; Machine learning; Hoeffding Tree.
Description As a result of the advancement in technology and technological devices, data is now spawned at an infinite rate, emanating from a vast array of networks, devices as well daily operations like credit card transactions and mobile phones. Data stream entails sequential and real-time continuous data in the inform of evolving stream. However, the traditional machine learning approach is characterized by a batch learning model in which labelled training data are given apriori to train a model based on some machine learning algorithms. This technique necessitates the entire training samples to be readily accessible before the learning process. In this setting, the training procedure is mostly done in an offline environment owing to the high cost of training. Consequently, traditional batch learning technique suffers from some serious drawbacks, such as poor scalability for the real-time phishing websites detection, because the model mostly requires re-training from scratch using new training samples. Thus, this paper presents the application of streaming algorithms for detecting malicious URLs based on some selected online learners which include: Hoeffding Tree (HT), Naïve Bayes (NB), and Ozabag. Hence, experimental results on two prominent phishing datasets showed that Ozabag produced promising results in terms of accuracy, Kappa and Kappa Temp on the dataset with large samples while HT and NB have the least prediction time with comparable accuracy and Kappa with Ozabag algorithm for the real-time detection of phishing websites.
Publisher Departemen Teknik Komputer, Fakultas Teknik, Universitas Diponegoro
Date 2021-10-31
Type info:eu-repo/semantics/article

Source Jurnal Teknologi dan Sistem Komputer; 2021: Publication In-Press
Jurnal Teknologi dan Sistem Komputer; 2021: Publication In-Press
Language en
Rights Copyright (c) 2021 Jurnal Teknologi dan Sistem Komputer

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us


Copyright © 2015-2018 Simon Fraser University Library