Flow Correlation Degree Optimization Driven Random Forest for Detecting DDoS Attacks in Cloud Computing
Joint Authors
Sheng, Victor S.
Cheng, Jieren
Li, Mengyang
Tang, Xiangyan
Liu, Yifu
Guo, Wei
Source
Security and Communication Networks
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-19
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Information Technology and Computer Science
Abstract EN
Distributed denial-of-service (DDoS) has caused major damage to cloud computing, and the false- and missing-alarm rates of existing DDoS attack-detection methods are relatively high in cloud environment.
In this paper, we propose a DDoS attack-detection method with enhanced random forest (RF) optimized by genetic algorithm based on flow correlation degree (FCD) feature.
We define the FCD feature according to the asymmetric and semidirectivity interaction characteristics and use the two-tuples FCD feature consisting of packet-statistical degree (PSD) and semidirectivity interaction abnormality (SDIA) to describe the features of attack flow and normal flow.
Then we use a genetic algorithm based on the FCD feature sequences to optimize two key parameters of the decision tree in the RF: the maximum number of decision trees and the maximum depth of every single decision tree.
We apply the trained RF model with optimized parameters to generate the classifier to be used for DDoS attack-detection.
The experiment shows that the proposed method can effectively detect DDoS attacks in cloud environment with a higher accuracy rate and lower false- and missing-alarm rates compared to existing DDoS attack-detection methods.
American Psychological Association (APA)
Cheng, Jieren& Li, Mengyang& Tang, Xiangyan& Sheng, Victor S.& Liu, Yifu& Guo, Wei. 2018. Flow Correlation Degree Optimization Driven Random Forest for Detecting DDoS Attacks in Cloud Computing. Security and Communication Networks،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1214295
Modern Language Association (MLA)
Cheng, Jieren…[et al.]. Flow Correlation Degree Optimization Driven Random Forest for Detecting DDoS Attacks in Cloud Computing. Security and Communication Networks No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1214295
American Medical Association (AMA)
Cheng, Jieren& Li, Mengyang& Tang, Xiangyan& Sheng, Victor S.& Liu, Yifu& Guo, Wei. Flow Correlation Degree Optimization Driven Random Forest for Detecting DDoS Attacks in Cloud Computing. Security and Communication Networks. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1214295
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214295