.png){kind=icon}
Return
A deep learning based HTTP slow DoS classification approach using flow data
.png){kind=icon}
OA
AI
DOI：10.1016/j.icte.2020.08.005.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The popularity of the Internet introduces many network-enabled services that can be accessed by the user. But the adversaries are trying to deny these critical services to the user through Denial of Service (DoS) attacks. Presently, dealing with DoS attack which targets the application layer using slow traffic rate is one of the key challenges faced by the service providers. In this paper, a deep classification model using flow data is proposed to detect slow DoS attack on HTTP. The classifier is evaluated using CICIDS2017 dataset. The results obtained show that the classifier can obtain 99.61% accuracy. (C) 2021 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.
Keywords:
Slow DoS
Deep learning
Flow data
Denial of Service
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
1.0K
Citations:
2.5K
Organization
Cited Papers
A Deep Long Short-Term Memory based classifier for Wireless Intrusion Detection System.png)
ICT EXPRESSOAAI
ICT EXPRESSIF4.2
OAAIShareSaveMinimizing false positive rate for DoS attack detection: A hybrid SDN-based approachICT EXPRESSOAAI
ICT EXPRESSIF4.2
OAAIShareSave
ShareSaveShareSave no more
no more