Detection Methods of Slow Read DoS Using Full Packet Capture Data
| Autor: | Clifford Kemp, Taghi M. Khoshgoftaar, Chad Calvert |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Web server Hypertext Transfer Protocol Exploit business.industry Computer science computer.internet_protocol Network packet Denial-of-service attack 02 engineering and technology computer.software_genre 020901 industrial engineering & automation Packet analyzer 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing The Internet False alarm business computer Computer network |
| Zdroj: | IRI |
| DOI: | 10.1109/iri49571.2020.00010 |
| Popis: | Detecting Denial of Service (DoS) attacks on web servers has become extremely popular with cybercriminals and organized crime groups. A successful DoS attack on network resources reduces availability of service to a web site and backend resources, and could easily result in a loss of millions of dollars in revenue depending on company size. There are many DoS attack methods, each of which is critical to providing an understanding of the nature of the DoS attack class. There has been a rise in recent years of application-layer DoS attack methods that target web servers and are challenging to detect. An attack may be disguised to look like legitimate traffic, except it targets specific application packets or functions. Slow Read DoS attack is one type of slow HTTP attack targeting the application-layer. Slow Read attacks are often used to exploit weaknesses in the HTTP protocol, as it is the most widely used protocol on the Internet. In this paper, we use Full Packet Capture (FPC) datasets for detecting Slow Read DoS attacks with machine learning methods. All data collected originates in a live network environment. Our approach produces FPC features taken from network packets at the IP and TCP layers. Experimental results show that the machine learners were quite successful in identifying the Slow Read attacks with high detection and low false alarm rates using FPC data. Our experiment evaluates FPC datasets to determine the accuracy and efficiency of several detection models for Slow Read attacks. The experiment demonstrates that FPC features are discriminative enough to detect such attacks. |
| Databáze: | OpenAIRE |
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