文章基本信息

标题：Detection of Suspicious URL with Naive Bayes and Support Vector Machine Learning Approac
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作者：ASHWINI NHAVI ; SHRUTIKA JAWALE ; PRAGATI PATIL 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2021
卷号：9
期号：7
页码：8531-8535
DOI：10.15680/IJIRCCE.2021.0907119
语种：English
出版社：S&S Publications
摘要：Suspicious URLs are widely used for performing various cyber attacks such as spamming, phishing and malware. Detection of malicious URL and threat type is critical and it finds difficult to beat these attacks.Knowing the type of threat helps in adoption of an effective countermeasure. Many existing methods are available to detect malicious URL and threat type but only for single type of attack at a time. The proposed hybrid machine learning approach is used to detect suspicious URLs. Supervised machine learning algorithms, Naive Bayes and Support Vector Machine alongwith a novel hybrid approach is used for better detection of suspicious URLs.
关键词：Cyber security;suspicious URL;Heuristic detection;Machine Learning (ML);Naive Bayes;Support;Vector Machine (SVM)