Open AccessDetecting malicious URLs using binary classification through adaboost algorithm
Author(s) -
Firoz Khan,
Jinesh Ahamed,
Seifedine Kadry,
Lakshmana Kumar Ramasamy
Publication year - 2020
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v10i1.pp997-1005
Subject(s) - adaboost , computer science , malware , computer security , binary number , machine learning , artificial intelligence , binary classification , algorithm , support vector machine , mathematics , arithmetic
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.