TY  - GEN
A1  - Gawron, Marian
A1  - Cheng, Feng
A1  - Meinel, Christoph
T1  - Automatic vulnerability classification using machine learning
T2  - Risks and Security of Internet and Systems
N2  - The classification of vulnerabilities is a fundamental step to derive formal attributes that allow a deeper analysis. Therefore, it is required that this classification has to be performed timely and accurate. Since the current situation demands a manual interaction in the classification process, the timely processing becomes a serious issue. Thus, we propose an automated alternative to the manual classification, because the amount of identified vulnerabilities per day cannot be processed manually anymore. We implemented two different approaches that are able to automatically classify vulnerabilities based on the vulnerability description. We evaluated our approaches, which use Neural Networks and the Naive Bayes methods respectively, on the base of publicly known vulnerabilities.
KW  - Vulnerability analysis
KW  - Security analytics
KW  - Data mining Machine learning
KW  - Neural Networks
Y1  - 2018
SN  - 978-3-319-76687-4
SN  - 978-3-319-76686-7
U6  - https://doi.org/10.1007/978-3-319-76687-4_1
SN  - 0302-9743
SN  - 1611-3349
SP  - 3
EP  - 17
PB  - Springer
CY  - Cham
ER  -