TY - CHAP
T1 - Discovering Malicious URLs Using Machine Learning Techniques
AU - Sun, Bo
AU - Takahashi, Takeshi
AU - Zhu, Lei
AU - Mori, Tatsuya
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Security specialists have been developing and implementing many countermeasures against security threats, which is needed because the number of new security threats is further and further growing. In this chapter, we introduce an approach for identifying hidden security threats by using Uniform Resource Locators (URLs) as an example dataset, with a method that automatically detects malicious URLs by leveraging machine learning techniques. We demonstrate the effectiveness of the method through performance evaluations.
AB - Security specialists have been developing and implementing many countermeasures against security threats, which is needed because the number of new security threats is further and further growing. In this chapter, we introduce an approach for identifying hidden security threats by using Uniform Resource Locators (URLs) as an example dataset, with a method that automatically detects malicious URLs by leveraging machine learning techniques. We demonstrate the effectiveness of the method through performance evaluations.
UR - http://www.scopus.com/inward/record.url?scp=85079438039&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079438039&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-38788-4_3
DO - 10.1007/978-3-030-38788-4_3
M3 - Chapter
AN - SCOPUS:85079438039
T3 - Intelligent Systems Reference Library
SP - 33
EP - 60
BT - Intelligent Systems Reference Library
PB - Springer
ER -