Name
Title | ||
|---|---|---|
Automatic Classification Method for Software Vulnerability Based on Deep Neural Network. |
Abstract | ||
|---|---|---|
Software vulnerabilities are the root causes of various security risks. Once a vulnerability is exploited by malicious attacks, it will greatly compromise the safety of the system, and may even cause catastrophic losses. Hence automatic classification methods are desirable to effectively manage the vulnerability in software, improve the security performance of the system, and reduce the risk of the system being attacked and damaged. In this paper, a new automatic vulnerability classification model (TFI-DNN) has been proposed. The model is built upon term frequency-inverse document frequency (TF-IDF), information gain (IG), and deep neural network (DNN): the TF-IDF is used to calculate the frequency and weight of each word from vulnerability description; the IG is used for feature selection to obtain an optimal set of feature word, and; the DNN neural network model is used to construct an automatic vulnerability classifier to achieve effective vulnerability classification. The National Vulnerability Database of the United States has been used to validate the effectiveness of the proposed model. Compared to SVM, Naive Bayes, and KNN, the TFI-DNN model has achieved better performance in multi-dimensional evaluation indexes including accuracy, recall rate, precision, and Fl-score. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2900462 | IEEE ACCESS |
Keywords | Field | DocType |
Deep neural network,information gain,software security,vulnerability classification | Vulnerability (computing),National Vulnerability Database,Naive Bayes classifier,Feature selection,tf–idf,Computer science,Support vector machine,Artificial intelligence,Artificial neural network,Machine learning,Vulnerability,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 1 |
PageRank | References | Authors |
0.36 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guo-Yan Huang | 1 | 10 | 4.84 |
| Yazhou Li | 2 | 1 | 0.36 |
| Qian Wang | 3 | 6 | 4.86 |
| Jiadong Ren | 4 | 39 | 12.15 |
| Yongqiang Cheng | 5 | 133 | 29.99 |
| Xiaolin Zhao | 6 | 2 | 1.39 |