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DDOS (Distributed Denial of Service) effect evaluation method based on BP (Back Propagation) neural network

A BP neural network and DDOS technology, applied in biological neural network models, data exchange networks, digital transmission systems, etc., can solve the problem of low evaluation accuracy and achieve accurate evaluation results

Active Publication Date: 2018-11-27
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the existing DDOS effect evaluation based on AHP needs to be manually determined weight and the evaluation accuracy is low, and proposes a DDOS effect evaluation method based on BP neural network

Method used

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  • DDOS (Distributed Denial of Service) effect evaluation method based on BP (Back Propagation) neural network
  • DDOS (Distributed Denial of Service) effect evaluation method based on BP (Back Propagation) neural network
  • DDOS (Distributed Denial of Service) effect evaluation method based on BP (Back Propagation) neural network

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specific Embodiment approach 1

[0025] Specific implementation mode one: the specific process of a kind of DDOS effect evaluation method based on BP neural network in this embodiment mode is:

[0026] Step 1. Establish a DDOS effect evaluation index system;

[0027] The DDOS effect evaluation index system includes attack strength, system resources and network resources;

[0028] Attack intensity includes the number of attack sources of DDOS attack aircraft and the attack frequency (attack interval time) of DDOS attack aircraft;

[0029] System resources include the memory usage of the destination host and the CPU usage of the destination host;

[0030] Network resources include bandwidth occupancy rate and packet loss rate;

[0031] In view of the diversified characteristics of the factors involved in the evaluation of denial of service attack effects, and each factor has different impacts on the attack and defense effects at different levels and dimensions, it is necessary to deeply study the characterist...

specific Embodiment approach 2

[0054] Specific implementation mode two: the difference between this implementation mode and specific implementation mode one is: the specific establishment process of the cloud environment is:

[0055] Experimental simulation network topology such as figure 2 , first build a simple cloud environment, the cloud environment is composed of three physical servers, a virtual network is established on the three physical servers, the topology of the virtual network is composed of three parts, namely N DDOS attack machines and a user virtual machine , a destination host;

[0056] The value of N is a positive integer.

[0057] figure 2 All nodes in the network are virtual nodes. The virtual machine cluster on the left side of the network is an attack proxy machine, on which an attack program is deployed for DDOS attacks. The upper right side of the network is a common user virtual machine, on which a service request program is deployed to collect Web The response of the server is...

specific Embodiment approach 3

[0059] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that: the specific process of establishing a virtual network on three physical servers is as follows:

[0060] Create a KVM virtual machine on each physical server and connect to the cloud network to obtain a virtual network.

[0061] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention provides a DDOS (Distributed Denial of Service) effect evaluation method based on a BP (Back Propagation) neural network and relates to the DDOS effect evaluation method based on the BPneural network, aiming at solving the problems of existing DDOS effect evaluation based on an analytic hierarchy process that weight needs to be manually judged and the evaluation accuracy is low. A specific process comprises the following steps: 1, establishing a DDOS effect evaluation index system; 2, simulating HTTP (Hyper Text Transfer Protocol) flood attack to acquire data of the DDOS effectevaluation index system in a cloud environment; 3, establishing an initial BP neural network model and optimizing an initial BP neural network model; inputting the quantity of attach sources of a DDOSattach machine, the attack frequency of the DDOS attach machine, the memory usage rate of a target host, the CPU (Central Processing Unit) usage rate of the target host, the bandwidth usage rate andthe packet loss rate, which are acquired in step 2, into the optimized BP neural network model; outputting an evaluation grade by the BP neural network model. The DDOS effect evaluation method based on the BP neural network, provided by the invention, is used for the field of DDOS effect evaluation.

Description

technical field [0001] The invention relates to a DDOS effect evaluation method based on BP neural network. Background technique [0002] Among the current mainstream network attacks, distributed denial-of-service attacks (DDOS attacks) are getting more and more attention. In recent years, DDOS attacks have also attracted widespread attention in cyberspace security. With the rise of cloud services, the target of DDOS attackers has gradually shifted to cloud-based services. According to the latest security intelligence, cloud and SaaS are the main attacks of DDOS Target. In response to new types of network attacks, the research on network security experiments has made rapid progress. All countries in the world regard network security experiments as key research objects. Network security experiments can improve people's awareness of network security and make people more familiar with the operating mechanism of new network attacks. , to study the countermeasures, through the ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06H04L12/24G06N3/02
CPCG06N3/02H04L41/145H04L63/1458
Inventor 张伟哲方滨兴何慧余翔湛张宇刘亚维李星晨王德胜
Owner HARBIN INST OF TECH
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