CN103049789A - Method for spreading malicious information flow in complex network - Google Patents
Method for spreading malicious information flow in complex network Download PDFInfo
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- CN103049789A CN103049789A CN2012105467571A CN201210546757A CN103049789A CN 103049789 A CN103049789 A CN 103049789A CN 2012105467571 A CN2012105467571 A CN 2012105467571A CN 201210546757 A CN201210546757 A CN 201210546757A CN 103049789 A CN103049789 A CN 103049789A
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Abstract
The invention belongs to a technology of spreading malicious information in a complex network and discloses a method for spreading malicious information flow in a complex network based on public sentiment spreading model. The method comprises the following steps of: firstly establishing nodes in a two-dimensional cellular automata simulation network; then applying the nodes to the complex network by virtue of a public sentiment spreading method of a public sentiment spreading model in sociology; and traversing all the nodes in the complex network, and exiting from circulation till meeting set conditions. The method disclosed by the invention can be used for simulating the spreading process of malicious information in the internet in time and space, thereby mastering the malicious information flow spreading rule and effectively reducing the damages caused by the malicious information flow spreading.
Description
Technical field
The invention belongs to the fallacious message communications in the complex network, particularly a kind of complex network fallacious message based on the public sentiment propagation model spreads broadcasting method.
Background technology
Fast development along with the complicated and infotech of social system, the pattern of Information Communication is more and more polynary change and complicated also, the pattern that diffuses information from the past interpersonal relation or mass media is different, and the propagation of the network information has very strong disguise, thereby may cause larger harm.Development along with society; people also increase rapidly the degree of dependence of network thereupon; so that various fallacious message streams can propagate in the world each corner easily by means of network; and scale trend with compared in the past more obviously, the network of hence one can see that today has been faced with various security threats.Therefore, the propagation dynamic behavior of research fallacious message stream is grasped its propagation law, and proposes on this basis effectively to reduce fallacious message and spread the measure that works the mischief of broadcasting, and all important realistic meaning will be arranged to the mankind's development and the progress of society.
Complex Networks Theory research be the various common attributes between the mutual incoherent complication system of it seems from the teeth outwards, and process the universal method that they are taked.The research of Complex Networks Theory makes people begin to notice the complicacy of network topology structure itself, and pays close attention to emphatically the substantial connection that exists between this complicacy and the network dynamic behavior.
The problem of propagating in complex network for fallacious message stream both at home and abroad at present mainly is confined to simulate the propagation law of biological virus, is used for describing the propagation dynamic process of computer virus, and result of study is comparative maturity.The evolutionary process of Public Opinion Transmission has the features such as complicacy highly, opening, uncertainty, non-equilibrium property, self-organization, the propagating characteristic that this and computer network fallacious message flow is very similar, can be by Computer Simulation, foundation is based on the fallacious message flow model of public sentiment diffusion model, with the achievement in research application domain natural science field in sociology field.
For the fallacious message stream of public sentiment Network Based, both be subject to the impact of network structure, also be subject to the impact of propagation model rule, and then affect the propagation of the network information.Because the emergentness that public sentiment and rumour are propagated, be difficult to gather and preserve real data in moment, therefore, existing method all is to simulate communication process from the mathematical model that the local feature design meets convention mostly.
The concept of cellular automaton (CA) is proposed by Von Neumann as far back as the 1950's, it is the mathematical model that a kind of time, space and variable all disperse, be mainly used in simulating the self-replication function that life system has, the simple model of this class can copy complicated phenomenon or the attractive force in the Dynamic Evolution, self-organization and chaos phenomenon very easily, thereby is widely used in every field.The advantage of simulating a physical process with CA is to have saved the usefulness differential equation as transition, and directly by laying down a regulation to simulate the nonlinear physics phenomenon.In these practical applications, the CA model has disclosed abiogenous macroscopic behavior by simple microcosmic local rule, is to study at present the discrete desirable physical model of space-time, is being considered to one of a kind of the most effective instrument aspect the research complication system.
Summary of the invention
The objective of the invention is to propose fallacious message in a kind of complex network and spread the method for broadcasting, setting up fallacious message spreads and broadcasts model, the propagation kinetic character of research fallacious message stream in complex network, spread propagation law thereby grasp fallacious message, effectively reduce fallacious message and spread to broadcast and work the mischief.
The technical solution that realizes the object of the invention is:
The step that the method broadcast of spreading fallacious message in the complex network adopts is as follows:
The first step: set up two dimensional cellular automaton, each cellular is a node in the complex network, and a cellular automaton system is A={C, and Q, V, f}, mesh space C are the quadrilateral cellular space in the two dimensional model; Discrete finite state Q; The neighborhood V of cellular represents four neighbours of center cellular; Determine the local evolution rule f of cellular automaton based on the Hacken model of public sentiment propagation;
Second step: determine local evolution rule f based on the Hacken model that public sentiment is propagated, set up the propagation model of fallacious message stream in the complex network; Discrete finite state Q={H, I}, H represent the node health status, I represents the node Infection Status; Definition t constantly state is the grid of H, and constantly becoming state at t+1 is that the probability of the grid of I is p (H → I); The probability that keeps original state is 1-p (H → H); T constantly state is that the grid of I becomes the Probability p that state is H (I → H); The probability that keeps original state is 1-p (I → I);
Can draw thus:
p (H→I)=v exp{-(kq+h)}/exp(k/2)
p (I→H)=v exp{+(kq+h)}/exp(k/2)
K represents the epidemic prevention degree of nodes itself; H represents the safety assessment grade of nodes self, i.e. health degree; What v represented is the time scale that a node state changes.The value of q is: q=(n
+---n
-)/2n
5, n
5=n
++ n
-
The 3rd step: definition neighbours territory local state probability:
A
+For H number in the current cellular neighbours territory is the probability of m: A
+=m/5
A
-For I number in the current cellular neighbours territory is the probability of j: A
-=j/5
Initial healthy density p
+(t) be the number ratio of healthy node and total node during t constantly in the complex network.
Magnetic susceptibility M (t) is the general inclination of complex network safe condition, M (t)=2p
+(t)-1
The 4th step: utilize at last Computer Simulation that all nodes in the complex network are carried out respectively again and again traversal, jump out circulation until satisfy the condition of setting.
Utilize the node in the Cellular Automata Simulation network, all cellulars are traveled through in each circulation according to its transfer function f, the specific implementation process is as follows:
1. the number of times t that defines cycle index T and circulated, initial t=0, definition cellular number is n;
2. at moment t, for all cellulars, sharply calculate the global transfer probability of cellular space N: the P (H → I) constantly at t
t, P (I → H)
t
3. calculate neighbours territory local state probable value: A
+And A
-
If 4. 5. center cellular value value (m)=H then judges, otherwise carry out 6.;
If 5. P (H → I)
t〉=A
-, then center cellular value value (m) is set to I, otherwise keeps value (m)=H;
If 6. 7. center cellular value value (m)=I then judges;
If 7. P (I → H)
t〉=A
-, then center cellular value value (m) is set to H, otherwise keeps value (m)=I;
8. draw magnetic susceptibility M (t)=(1/m) the Σ value (i) of traversal after once;
9. when M (t)-M (t-1)＜0.0005, stop circulation, otherwise t=t+1, the rebound step is 2..
Described node health status and node Infection Status are the systemic presupposition value.
The beneficial effect that the present invention produces:
The present invention proposes a kind of fallacious message flow model based on the public sentiment diffusion model, by setting up the cellular automaton system, utilize the Hacken model to determine the local evolution rule, all nodes in the complex network are traveled through, can simulate the communication process of fallacious message in the internet in time and space well, spread propagation law thereby grasp fallacious message, effectively reduce fallacious message and spread to broadcast and work the mischief.
Description of drawings
Fig. 1 is the quadrilateral cellular space of cellular automaton
Fig. 2 is the Von Neumann neighbourhood model of cellular automaton
Fig. 3 is fallacious message stream cellular node traversal process flow diagram in complex network
Embodiment
Below in conjunction with accompanying drawing the present invention is described in further detail.
Embodiment 1:
One, set up a kind of two dimensional cellular automaton:
Each cellular is a node in the complex network, a cellular automaton system can be expressed as A={C, Q, V, f}, comprise the mesh space C of regular partition, discrete finite state set Q, the neighborhood V of cellular and the four-tuple of local transfer function f, f is also referred to as the local evolution rule of cellular automaton, and it is the core of cellular automaton.
Mesh space C is the quadrilateral cellular space in the two dimensional model, as shown in Figure 1; Discrete finite state Q={H, I} represents respectively health and the Infection Status of node; The neighborhood V of cellular adopts classical Von Neumann neighbourhood model, as shown in Figure 2; The Hacken model that the definition of the local evolution rule f of cellular automaton is propagated based on public sentiment.
Two, the Hacken model of propagating based on public sentiment is set up the propagation model that fallacious message flows in the complex network.
The Hacken model is based upon on the cubic grid of two dimension rule, and each grid can have 2 kinds of states: if the grid state is+1, represent that the people of this grid holds concurring opinion; If be-1, represent that the people of this point dissents.Use n
+The grid number of concurring opinion is held in expression, uses n
-The grid number that expression is dissented.Because the interchange of information, personnel's attitude can change, and brings the change of grid state.Suppose t constantly state be+1 grid, becoming state constantly is p (+1 →-1) in t+1 for the probability of-1 grid, keeping the probability of original state is 1-p (+1 →-1); T constantly state is+1 Probability p (1 →+1) for-1 grid becomes state; Keeping the probability of original state is 1-p (1 →+1).
Hacken thinks, grid by+1 state change to-1 state probability and by-1 state change to+probability of 1 state is respectively:
p ( + 1→-1)=v exp{ (kq+h)}
p (-1→+1)=v exp{+(kq+h)}
K is the adaptation intensity to environment in the formula; H is tendentiousness parameter (h〉0 mean that suggestion "+" is better than "-"); V is the frequency of " turning to " process; Q=(n
+-n
-)/2n, n=n
++ n, wherein n represents system's cellular sum.
The present invention is based on existing Hacken model, according to the fallacious message stream propagating characteristic in complex network, wherein rule and factor of influence are being applied to fallacious message in the complex network spread and broadcast, specific as follows:
1. the adaptation intensity k of environment, it is one and is obedient to parameter, points out that people easily in compliance with the trend of public opinion in vogue, make a kind of suggestion that overwhelming dominance more and more be arranged.Correspond in the complex network, k represents the epidemic prevention degree of nodes itself, represents that a node itself is for the resistance degree of fallacious message stream.
2. tendentiousness parameter h, it has embodied personnel because intrinsic tendency, and judgement and view to situation that distinctive interchange and observation produce make some people to a kind of generation Preference in two kinds of suggestions.Correspond in the complex network, h represents the safety assessment grade of nodes self, i.e. health degree.
3. " turn to " the frequency v of process to embody the time scale that public opinion changes.Larger in the frequency of not considering " turning to " process in the idiosyncratic gap situation of personnel's individuality, personnel just more lack resolution, just easier change of attitude; " turn to " frequency of process less, personnel are just more firm.In the propagation of complex network fallacious message stream, what v represented is the time scale that a node state changes.
Utilize at last Computer Simulation that all nodes in the complex network are carried out respectively again and again traversal, jump out circulation until satisfy the condition of setting.Simulation process can reflect the communication process of fallacious message stream in complex network.
Embodiment 2
One, the rule use of cellular automaton Hacken Consensus Model is placed in the middle in the Von of the local of system Neumann neighbours, namely in renewal each time, randomly draw a grid, the Von Neumann neighbours that form centered by this grid occupy and are a group.Then according to Hacken Public Opinion Transmission model, just like drawing a conclusion:
p(H→I)=v exp{-(kq+h)}
p(I→H)=v exp{+(kq+h)}
The public sentiment diffusion model is corresponded in the mode of fallacious message stream in complex network, and k represents the epidemic prevention degree of nodes itself; H represents the safety assessment grade of nodes self, i.e. health degree; What v represented is the time scale that a node state changes.The value of q is once chosen 5 cellulars as goal in research, so be defined as because the present invention gets is neighbours' domain models:
q=(n
+---n
-)/2n
5 , n
5=n
++n
-
Simulate for the ease of the random number of utilizing computing machine to produce, we are rewritten as the change probability of grid state:
p (H→I)=v exp{-(kq+h)}/exp(k/2) (1)
p (I→H)=v exp{+(kq+h)}/exp(k/2) (2)
Two, utilize conclusions and definition, use some definition for describing and calculate the communication process of fallacious message stream at complex network:
1. define neighbours territory local state probability:
A
+For H number in the current cellular neighbours territory is the probability of m: A
+=m/5 (3)
A
-For I number in the current cellular neighbours territory is the probability of j: A
-=j/5 (4)
2. define initial healthy density p
+The number ratio of healthy node and total node when (t), being illustrated in the complex network t constantly.
3. definition magnetic susceptibility M (t) represents the general inclination of complex network safe condition, M (t)=2p
+(t)-1
Three, based on above definition, begin to carry out the traversal calculating of whole network.As shown in Figure 3, the process of propagation cycle calculations is as follows:
1. the number of times t that defines cycle index T and circulated, initial t=0, definition cellular number is n;
2. at moment t, for all cellulars, utilize formula (1) and (2) to calculate respectively the global transfer probability of cellular space N: P (t of H → I), the P (t of I → H) constantly at t;
3. utilize formula (3) and (4) to calculate neighbours territory local state probable value: A
+And A
-
If 4. 5. center cellular value value (m)=H then judges, otherwise carry out 6.;
If the 5. P (t 〉=A of H → I)
-, then center cellular value value (m) is set to I, otherwise keeps value (m)=H;
If 6. 7. center cellular value value (m)=I then judges.;
If the 7. P (t 〉=A of I → H)
-, then center cellular value value (m) is set to H, otherwise keeps value (m)=I;
8. draw magnetic susceptibility M (t)=(1/m) the Σ value (i) of traversal after once;
9. when M (t)-M (t-1)＜0.0005, stop circulation, otherwise t=t+1, the rebound step is 2..
Claims (3)
1. the fallacious message in the complex network spreads broadcasting method, it is characterized in that the step that adopts is as follows:
The first step: set up two dimensional cellular automaton, each cellular is a node in the complex network, and a cellular automaton system is A={C, and Q, V, f}, mesh space C are the quadrilateral cellular space in the two dimensional model; Discrete finite state Q; The neighborhood V of cellular represents four neighbours of center cellular; Determine the local evolution rule f of cellular automaton based on the Hacken model of public sentiment propagation;
Second step: determine local evolution rule f based on the Hacken model that public sentiment is propagated, set up the propagation model of fallacious message stream in the complex network; Discrete finite state Q={H, I}, H represent the node health status, I represents the node Infection Status; Definition t constantly state is the grid of H, and the probability that becomes state in t+1 constantly and be the grid of I is p (H → I); The probability that keeps original state is 1-p (H → H); T constantly state is that the grid of I becomes the Probability p that state is H (I → H); The probability that keeps original state is 1-p (I → I);
Can draw thus:
p (H→I)=v exp{-(kq+h)}/exp(k/2)
p (I→H)=v exp{+(kq+h)}/exp(k/2)
K represents the epidemic prevention degree of nodes itself; H represents the safety assessment grade of nodes self, i.e. health degree; What v represented is the time scale that a node state changes;
The value of q is: q=(n
+---n
-)/2n
5, n
5=n
++ n
-
The 3rd step: definition neighbours territory local state probability:
A
+For H number in the current cellular neighbours territory is the probability of m: A
+=m/5
A
-For I number in the current cellular neighbours territory is the probability of j: A
-=j/5
Initial healthy density p
+(t) be the number ratio of healthy node and total node during t constantly in the complex network;
Magnetic susceptibility M (t) is the general inclination of complex network safe condition, M (t)=2p
+(t)-1
The 4th step: utilize at last Computer Simulation that all nodes in the complex network are carried out respectively again and again traversal, jump out circulation until satisfy the condition of setting.
2. the fallacious message in the complex network according to claim 1 spreads broadcasting method, it is characterized in that: utilize the node in the Cellular Automata Simulation network, all cellulars are traveled through in each circulation according to its transfer function f, the specific implementation process is as follows:
1. the number of times t that defines cycle index T and circulated, initial t=0, definition cellular number is n;
2. at moment t, for all cellulars, sharply calculate the global transfer probability of cellular space N: the P (H → I) constantly at t
t, P (I → H)
t
3. calculate neighbours territory local state probable value: A
+And A
-
If 4. 5. center cellular value value (m)=H then judges, otherwise carry out 6.;
If 5. P (H → I)
t〉=A
-, then center cellular value value (m) is set to I, otherwise keeps value (m)=H;
If 6. 7. center cellular value value (m)=I then judges;
If 7. P (I → H)
t〉=A
-, then center cellular value value (m) is set to H, otherwise keeps value (m)=I;
8. draw magnetic susceptibility M (t)=(1/m) the Σ value (i) of traversal after once;
9. when M (t)-M (t-1)＜0.0005, stop circulation, otherwise t=t+1, the rebound step is 2..
3. the fallacious message in the complex network according to claim 1 spreads broadcasting method, and it is characterized in that: described node health status H and node Infection Status I are the systemic presupposition value.
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| CN108881282A (en) * | 2018-07-12 | 2018-11-23 | 北京航空航天大学 | A kind of automotive networking fallacious message transmission method based on Epidemic Model |
| CN109299808A (en) * | 2018-06-04 | 2019-02-01 | 西安电子科技大学 | Netizen's viewpoint evolution trend prediction technique of force modeling is advised based on group |
| CN113052713A (en) * | 2021-03-25 | 2021-06-29 | 陕西师范大学 | Negative information cascade blocking method based on sensitive node immunity |
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| US9342692B2 (en) | 2013-08-29 | 2016-05-17 | International Business Machines Corporation | Neutralizing propagation of malicious information |
| US10116689B2 (en) | 2013-08-29 | 2018-10-30 | International Business Machines Corporation | Neutralizing propagation of malicious information |
| CN104734870B (en) * | 2013-12-19 | 2019-03-29 | 南京理工大学 | A kind of software fault propagation law discovery method based on cellular automata |
| CN104734870A (en) * | 2013-12-19 | 2015-06-24 | 南京理工大学 | Software fault spreading method based on cellular automaton |
| CN104156462B (en) * | 2014-08-21 | 2017-07-28 | 上海交通大学 | Complex network community method for digging based on cellular Learning Automata |
| CN107402997A (en) * | 2017-07-20 | 2017-11-28 | 中国电子科技集团公司电子科学研究院 | Safety evaluation method, terminal and the computer-readable storage medium of network public-opinion situation |
| CN107402997B (en) * | 2017-07-20 | 2020-08-07 | 中国电子科技集团公司电子科学研究院 | Security assessment method, terminal and computer storage medium for network public opinion situation |
| CN109299808A (en) * | 2018-06-04 | 2019-02-01 | 西安电子科技大学 | Netizen's viewpoint evolution trend prediction technique of force modeling is advised based on group |
| CN109299808B (en) * | 2018-06-04 | 2021-04-06 | 西安电子科技大学 | Netizen viewpoint evolution trend prediction method based on group persuasion modeling |
| CN108881282A (en) * | 2018-07-12 | 2018-11-23 | 北京航空航天大学 | A kind of automotive networking fallacious message transmission method based on Epidemic Model |
| CN108881282B (en) * | 2018-07-12 | 2020-08-25 | 北京航空航天大学 | Automobile network malicious information transmission method based on infectious disease model |
| CN113052713A (en) * | 2021-03-25 | 2021-06-29 | 陕西师范大学 | Negative information cascade blocking method based on sensitive node immunity |
| CN113052713B (en) * | 2021-03-25 | 2023-06-23 | 陕西师范大学 | Negative information cascade blocking method based on sensitive node immunity |
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Application publication date: 20130417 |