1 |
DOSHI R, APTHORPE N, FEAMSTER N. Machine learning DDoS detection for consumer internet of things devices[C]// Proceedings of the 2018 IEEE Security and Privacy Workshops. Piscataway: IEEE, 2018: 29-35. 10.1109/spw.2018.00013
|
2 |
腾讯云T-Sec DDoS防护团队,绿盟科技威胁情报团队. 2021年全球DDoS威胁报告[R/OL]. [2022-09-14]..
|
|
Tencent Cloud T-Sec DDoS Protection Group, NSFOCUS Threat Intelligence Group. Global DDoS threat report 2021[R/OL]. [2022-09-14]..
|
3 |
PRIYA S S, SIVARAM M, YUVARAJ D, et al. Machine learning based DDoS detection[C]// Proceedings of the 2020 International Conference on Emerging Smart Computing and Informatics. Piscataway: IEEE, 2020: 234-237. 10.1109/esci48226.2020.9167642
|
4 |
SUTHAHARAN S. Decision tree learning[M]// Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, ISIS 36. Cham: Springer, 2016:237-269. 10.1007/978-1-4899-7641-3_10
|
5 |
JIA B, HUANG X, LIU R, et al. A DDoS attack detection method based on hybrid heterogeneous multiclassifier ensemble learning[J]. Journal of Electrical and Computer Engineering, 2017, 2017: No.4975343. 10.1155/2017/4975343
|
6 |
NAJAFIMEHR M, ZARIFZADEH S, MOSTAFAVI S. A hybrid machine learning approach for detecting unprecedented DDoS attacks[J]. The Journal of Supercomputing, 2022, 78(6): 8106-8136. 10.1007/s11227-021-04253-x
|
7 |
孟曈. 基于机器学习与可逆Sketch的DDoS攻击检测[D]. 西安:西安电子科技大学, 2020:92-92.
|
|
MENG T. DDoS intrusion detection based on machine learning and reversible sketch[D]. Xi’an: Xidian University, 2020: 92-92.
|
8 |
OSANAIYE O, CAI H, CHOO K K R, et al. Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing[J]. EURASIP Journal on Wireless Communications and Networking, 2016, 2016: No.130. 10.1186/s13638-016-0623-3
|
9 |
GU Y, LI K, GUO Z, et al. Semi-supervised k-means DDoS detection method using hybrid feature selection algorithm[J]. IEEE Access, 2019, 7: 64351-64365. 10.1109/access.2019.2917532
|
10 |
PANDE S, KHAMPARIA A, GUPTA D, et al. DDOS detection using machine learning technique[M]// KHANNA A, SINGH A K, SWAROOP A. Recent Studies on Computational Intelligence: Doctoral Symposium on Computational Intelligence (DoSCI 2020), SCI 921. Singapore: Springer, 2021: 59-68. 10.1007/978-981-15-8469-5_5
|
11 |
CHENG J, LI M, TANG X, et al. Flow correlation degree optimization driven random forest for detecting DDoS attacks in cloud computing[J]. Security and Communication Networks, 2018, 2018: No.6459326. 10.1155/2018/6459326
|
12 |
LOURENÇO P, GODINHO S, SOUSA A, et al. Estimating tree aboveground biomass using multispectral satellite-based data in Mediterranean agroforestry system using random forest algorithm[J]. Remote Sensing Applications: Society and Environment, 2021, 23: No.100560. 10.1016/j.rsase.2021.100560
|
13 |
RIGATTI S J. Random forest[J]. Journal of Insurance Medicine, 2017, 47(1): 31-39. 10.17849/insm-47-01-31-39.1
|
14 |
HESTERBERG T. Bootstrap[J]. WIREs: Computational Statistics, 2011, 3(6): 497-526. 10.1002/wics.182
|
15 |
BREIMAN L, FRIEDMAN J H, OLSHEN R A, et al. Classification And Regression Trees (CART) [M]// Biometrics. [S.l]: Wadsworth, 1984: 358. 10.2307/2530946
|
16 |
BREIMAN L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140. 10.1007/bf00058655
|
17 |
李郅琴,杜建强,聂斌,等. 特征选择方法综述[J]. 计算机工程与应用, 2019, 55(24):10-19. 10.3778/j.issn.1002-8331.1909-0066
|
|
LI Z Q, DU J Q, NIE B, et al. Summary of feature selection methods[J]. Computer Engineering and Applications, 2019, 55(24): 10-19. 10.3778/j.issn.1002-8331.1909-0066
|
18 |
KIRA K, RENDELL L A. The feature selection problem: traditional methods and a new algorithm[C]// Proceedings of the 10th AAAI Conference on Artificial intelligence. Menlo Park, CA: AAAI Press, 1992: 129-134. 10.1016/b978-1-55860-247-2.50037-1
|
19 |
MIKA S, RATSCH G, WESTON J, et al. Fisher discriminant analysis with kernels[C]// Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop. Piscataway: IEEE, 1999: 41-48. 10.1109/nnsp.1999.788116
|
20 |
VERLEYSEN M, FRANÇOIS D. The curse of dimensionality in data mining and time series prediction[C]// Proceedings of the 2005 International Work-Conference on Artificial Neural Networks, LNCS 3512. Berlin: Springer, 2005: 758-770.
|
21 |
TANGIRALA S. Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm[J]. International Journal of Advanced Computer Science and Applications, 2020, 11(2): 612-619. 10.14569/ijacsa.2020.0110277
|
22 |
RAO H, SHI X, RODRIGUE A K, et al. Feature selection based on artificial bee colony and gradient boosting decision tree[J]. Applied Soft Computing, 2019, 74: 634-642. 10.1016/j.asoc.2018.10.036
|