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Analysis of Equations for Malicious Program Propagation Law and Malicious Program Spread Prediction Method

A prediction method and malicious program technology, applied in the field of big data security, can solve the problem of no malicious program prediction, etc.

Active Publication Date: 2021-03-19
密信(北京)数字科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, various malicious program attacks occur frequently, and the technical means of malicious program attacks are also constantly changing and updating, and the problem of network security prevention is becoming more and more prominent.
Therefore, it is very important to detect and predict malicious programs. At present, there is no method for predicting malicious programs.

Method used

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  • Analysis of Equations for Malicious Program Propagation Law and Malicious Program Spread Prediction Method
  • Analysis of Equations for Malicious Program Propagation Law and Malicious Program Spread Prediction Method
  • Analysis of Equations for Malicious Program Propagation Law and Malicious Program Spread Prediction Method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] Example 1 The initial establishment of the method, such as Figure 4 .

[0040] use as figure 2 The four-compartment model shown, where,

[0041] Healthy Devices—Denote Healthy Devices with S

[0042] Freely infected devices (infected)—let I represent the number of infected devices that have not been confirmed to be infectious

[0043] Confirmed equipment (isolated) - use P to indicate that it has been isolated and withdrawn from the infected system, and will not infect other equipment

[0044] Repaired or damaged equipment (including "repaired equipment" and "damaged equipment") - use R to represent its number, and no longer participate in the spread of malicious programs. Note: Generally, it is rare for equipment damage caused by malicious program damage, so the impact of equipment damage is not considered in this method.

[0045] The construction process of the differential equation:

[0046] 1. Let the infection rate be λ 1 , indicating the ratio of infect...

Embodiment 2

[0056] Example 2 Confirmation of coefficients, such as Figure 4 .

[0057] The first is big data statistics: analyze and predict according to business needs, so obtain device data (in days) infected by malicious programs within a period of time (generally the time period closest to the current time).

[0058] Then obtain relevant parameters sequentially according to the actual situation.

[0059] 1) Initial value function I(t)=φ(t), t∈[-T,0]. This is the starting function of the differential equation with delay, which can be obtained by statistics and fitting based on the data in the actual environment. The processing method of this method is to set the value of the initial function based on the average value of the infected device counted by big data, which is called the initial value.

[0060] 2) T value, that is, the incubation period. In an actual network environment, the period from when a device carrying a malicious program receives a malicious program file to whe...

Embodiment 3

[0066] Example 3 method to solve, such as Figure 4 .

[0067] Repeatedly adjust the parameters λ, j, T, φ(t), so that these parameters can conform to the statistical characteristics of the actual data, so that the theoretical value and the actual value can reach the most consistent degree.

[0068] Substitute the above parameter values ​​into the differential equation system. Since we cannot find the analytical solution of the equations, we use the Runge-Kutta method in numerical analysis to solve the equations.

[0069] The solution process of the Runge-Kutta method:

[0070] Let the initial problem be stated as follows

[0071] y'=f(t,y),y(t 0 )=y 0

[0072] where y'=f(t,y) corresponds to The resulting RK4 equation is as follows:

[0073]

[0074] in

[0075] k 1 =f(t n ,y n )

[0076]

[0077]

[0078] k 4 =f(t n +h,y n +hk 3 )

[0079] Thus, the next value (y n+1 ) by the current value (y n ) plus the product of the time interval (h) and an...

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Abstract

The invention relates to an equation for analyzing the propagation law of malicious programs. As shown in Formula 1, the invention also relates to a malicious software diffusion prediction method based on a differential equation model. The method comprises the following steps: the number and information of malicious programs and the type of malicious programs are counted according to a selected area to obtain statistical data; data analysis is conducted to calculate the statistical data and to obtain the initial value, latency, infection rate and controlled rate; the initial value, incubationperiod, infection rate and controlled rate are substituted into the equations; a Kutta method is used to solve the problem, and the number of infected equipment is predicted. The invention adopts thebig data technology and the ordinary differential equation model to analyze and predict the equipment infected by the malicious program, so as to understand the spreading trend of the malicious program and formulate the related strategy.

Description

technical field [0001] The present invention relates to the technical field of big data security, and more specifically, relates to a system of equations for analyzing propagation laws of malicious programs, and a method for predicting the spread of malicious programs based on a differential equation model. Background technique [0002] Nowadays, the popularity of the Internet is getting higher and higher, and the scale of various government internal office networks is getting larger and larger. For example, the public security private network has formed a nationwide network, and there is a huge pressure on the management of various terminals within the network. At the same time, various malicious program attacks occur frequently, and the technical means of malicious program attacks are also constantly changing and updating, and the problem of network security prevention is becoming more and more prominent. Therefore, it is very important to detect and predict malicious prog...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F21/56
CPCG06F21/566G06F2221/033
Inventor 林皓吴小景胡建斌
Owner 密信(北京)数字科技有限公司
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