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A ddos ​​attack detection method based on intelligent bee colony algorithm

A bee colony algorithm and attack detection technology, applied in the field of cloud security, can solve problems such as no very suitable solution, rare, wide range of attacks, etc.

Active Publication Date: 2020-11-06
SHANGHAI MARITIME UNIVERSITY
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Problems solved by technology

[0004] Today's network attackers are constantly improving DDoS attack technology, but currently there is no very suitable solution for the characteristics of DDoS such as wide range of DDoS attacks, good camouflage and erratic attack sources, such as traditional clustering algorithms to detect DDoS methods Research on improvements is rare

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  • A ddos ​​attack detection method based on intelligent bee colony algorithm
  • A ddos ​​attack detection method based on intelligent bee colony algorithm
  • A ddos ​​attack detection method based on intelligent bee colony algorithm

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Embodiment Construction

[0060] The present invention will be further elaborated below by describing a preferred specific embodiment in detail in conjunction with the accompanying drawings.

[0061] Such as figure 1 As shown, a kind of DDoS attack detection method based on the intelligent bee colony algorithm of the present invention, this method has obviously improved the performance effect in terms of intra-class compactness, inter-class separation, clustering accuracy, algorithm time-consuming and DDoS detection accuracy . The detection method includes the following process:

[0062] Step S1, merging the clustering algorithm K-means and DFSABC_elite, using the advantage of DFSABC_elite to jump out of the local optimum to improve the defect that the clustering algorithm K-means is overly dependent on the original cluster center.

[0063] Step S2. According to the clustering result, the normal flow data flow and the abnormal flow data flow are respectively clustered, and each is classified into one...

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Abstract

The invention provides a DDoS attack detection method based on an intelligent bee colony algorithm. Through fusion of a clustering algorithm and an intelligent bee colony algorithm, the DDoS attack detection accuracy is effectively improved. The fusion of the intelligent bee colony algorithm and the clustering algorithm can eliminate the defect that the clustering algorithm excessively relies on an original clustering center and thus improve the data flow clustering effect. The IP addresses of exceptional data flows clustered after improvement are statistically analyzed and the flow characteristic entropy H (x) of the IP addresses are calculated; if H (x) is greater than and equal to a discriminating factor RM (x) of a primary clustering data flow, it is judged that the data flows are DDoSattack data flows; otherwise, it is determined that the data flows are other exceptional data flows. The method has the advantages of less time consumption, high DDoS attack detection accuracy and low false alarm rate.

Description

technical field [0001] The invention relates to the field of cloud security, in particular to a DDoS attack detection method based on an intelligent bee colony algorithm. Background technique [0002] Distributed Denial of Service attack (Distributed Denial of Service, DDoS) is the most common and most difficult network attack on the network. In December 2014, a DDoS attack on the operator's DNS network broke out. From the early morning of December 10th, network monitoring saw a sudden increase in attack traffic, and by 11:00 am, the attack became active, and web page access was slow or even unable to open in many places. The attacker not only initiated a query request with a peak value greater than 6G bps in a short period of time (an attack greater than 100G across the country), but also continuously changed the second-level domain name, which caused the delay of DNS recursive servers in various places to increase, and the core analysis business was affected. Serious imp...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/06
CPCH04L63/1416H04L63/1425H04L63/1458
Inventor 余学山韩德志王军田秋亭毕坤
Owner SHANGHAI MARITIME UNIVERSITY
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