International Journal of Electrical and Computer Engineering
Vol 10, No 1: February 2020

Detecting malicious URLs using binary classification through adaboost algorithm

Firoz Khan (Higher Colleges of Technology)
Jinesh Ahamed (Higher Colleges of Technology)
Seifedine Kadry (Beirut Arab University)
Lakshmana Kumar Ramasamy (Hindusthan College of Engineering and Technology)

Article Info

Publish Date
01 Feb 2020

Abstract

Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. This study aims to discuss the exposure of malicious URLs as a binary classification problem using machine learning through an AdaBoost algorithm.

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Journal Info
International Journal of Electrical and Computer Engineering
Website

Abbrev

IJECE

Publisher

Institute of Advanced Engineering and Science

Subject

Computer Science & IT Electrical & Electronics Engineering

Description

International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of ...