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All Journal International Journal of Electrical and Computer Engineering
Jinesh Ahamed
Higher Colleges of Technology
Computer Science & IT Electrical & Electronics Engineering

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Detecting malicious URLs using binary classification through adaboost algorithm Firoz Khan; Jinesh Ahamed; Seifedine Kadry; Lakshmana Kumar Ramasamy
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (652.843 KB) | DOI: 10.11591/ijece.v10i1.pp997-1005

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.
Co-Authors Firoz Khan Lakshmana Kumar Ramasamy Seifedine Kadry