A dos/ddos attack detection method
An attack detection and purpose technology, applied in the network field, to meet the real-time requirements and prevent harm
Patent Information
- Authority / Receiving Office
- CN · China
- Current Assignee / Owner
- Publication Date
- 2011-12-07
- Estimated Expiration
- Not applicable · inactive patent
Smart Images
Figure 1 Figure 2 Figure 3
Abstract
Description
technical field
[0001] The invention belongs to the field of network technology, in particular to a DOS / DDOS attack detection method. Background technique
[0002] Denial of Service (DoS) attack is a form of attack that prevents a computer or network from providing normal services by sending a large number of data packets. It may exhaust all available network resources or system resources of the attacked object in a short period of time, making legitimate user requests unable to pass through or be processed, thus hindering normal communication in the network and bringing huge damage to the attacked and even the network. harm.
[0003] A distributed denial of service (Distributed Denial of Service, DDoS) attack is a covert denial of service attack, and the data packets in the attack come from different attack sources. Compared with DoS attacks, DDoS attacks have smaller traffic on a single link and are difficult to be detected by network devices, so they are easier to form....
Examples
Embodiment Construction
[0015] The present invention will be further elaborated below in conjunction with specific examples.
[0016] The DOS / DDOS attack detection method of the present invention firstly extracts the required traffic characteristic parameters, and calculates the information entropy, according to extracting the traffic characteristic parameters-determining the abnormal time point-determining the abnormal destination IP-identifying the abnormal flow and distinguishing the attack type The detection of DoS / DDoS attacks and the identification of abnormal flows are completed sequentially. The specific flow diagram is as follows figure 1 shown.
[0017] Specifically include the following steps:
[0018] S1. Obtain flow data in the network from network devices, and extract flow characteristic parameters from the flow data;
[0019] S2. Process the traffic characteristic parameters extracted in step S1, determine the abnormal time point, and expand according to the abnormal time point to fo...