CN105959262B - A kind of control method for inhibiting rogue program to propagate in wireless sensor network - Google Patents
A kind of control method for inhibiting rogue program to propagate in wireless sensor network Download PDFInfo
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- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
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Abstract
The invention discloses the control methods for inhibiting rogue program to propagate in a kind of wireless sensor network, consider the sleeping/waking mechanism of wireless sensor network, the influence that the faith mechanism of wireless sensor network and the time lag time delay of network propagate rogue program in wireless sensor network, a kind of propagation model of new rogue program is constructed, propagation of the rogue program in wireless sensor network is preferably described.Simultaneously, using network to the immunization rate of self killing of rogue program and network as optimal control variable, cost functional is introduced, Optimal Control Model is established, so that infecting the quantity minimum of node and the safety measure cost minimization for resisting rogue program propagation in network at any time.
Description
Technical field
The invention belongs to technical field of communication network, and in particular to the propagation of rogue program in a kind of wireless sensor network
The design of model.
Background technique
Wireless sensor network (Wireless Sensor Network, WSN) is by largely with the sensing of specific function
Device node) it is in communication with each other by way of self-organizing, the ad hoc network system of various functions is completed in collaboration.It combines sensing
The multiple technologies such as device technology, embedded technology, the communication technology, distributed information processing can pass through various micro sensings
Device cooperation, real-time monitoring, the information of perception and the various environment of acquisition or monitoring object.Since it has low cost, low-power etc.
Characteristic has been obtained and is more and more widely used.
Rogue program is a kind of special program, it is by being embedded into code in the case where not detectable another section of journey
In sequence, so that reaching operation has invasive or destructive program, destroys the safety of infected data and the mesh of integrality
's.It is several that rogue program by its circulation way can be divided into computer virus, wooden horse, worm, mobile code and multipartite virus etc.
Kind.Broad development and application with wireless sensor network, have greatly attracted the interest of rogue program author, wireless to pass
Sensor network has become rogue program new attack target, and more and more rogue programs are begun to appear in wireless sense network,
Great threat is constituted to the safety of wireless network.
Some rogue program attacks such as worm and virus on Internet (internet) have been widely studied, especially
Complex Networks Theory is introduced to the propagation problem research in the various network environments such as Internet, community network, bio-networks
One completely new perspective in research, these researchs facilitate the propagation of rogue program in WSN.In recent years, many people combine classics
The characteristics of Epidemic Model and wireless sensor network, studies the model that rogue program is propagated in wireless sensor network,
There is biggish development.
However, the existing research to the propagation model of rogue program in wireless sensor network seldom considers wireless sensing
Sleeping/waking mechanism in device network, this prevents the network model proposed from correctly describing rogue program in wireless sensing
The dissemination of device network, to the attack of rogue program cannot be defendd to provide correct opinion very well.Moreover, currently to evil
The influence of faith mechanism in wireless sensor and actor networks is not considered in the propagation model research of meaning program, faith mechanism can make to have felt
The node of dye rogue program loses the ability communicated with surroundings nodes, cannot infect surroundings nodes.In addition, existing model also compared with
Influence of the time-lag effect to rogue program propagation model is considered less.
Summary of the invention
The present invention is in order to solve the above technical problems, propose the control for inhibiting rogue program to propagate in a kind of wireless sensor network
Method processed, it is contemplated that the sleeping/waking mechanism of wireless sensor network, the faith mechanism of network, internetwork time-lag effect (net
When time-lag effect between network is mainly manifested in node of the Node of Infection Status in network for isolation, have certain
Time delay τ), the propagation model of rogue program, more closer to reality are constructed, can be to resist rogue program in wireless sensor network
It propagates and extends efficient help in network.
The technical solution adopted by the present invention is that: the controlling party for inhibiting rogue program to propagate in a kind of wireless sensor network
Method, comprising:
S1, the network model for determining wireless sensor: determining node total number, and node is evenly distributed on the two dimension of known area
In region, and assume to will increase equal number health node replacement dead node within the set time；
S2, by the node division in wireless sensor network are as follows: sensitization node (S), Infection Status node (I), every
From state node (Q), immune state node (R), dormant state node (P) and dead state node (D)；
S3, according to the mutual conversion between each state node, obtain state transfer equation；
S4, according to the quantity of Infection Status node (I), wireless sensor network in network to Infection Status node (I)
Killing rate and objective function is constructed to the immunization rate of sensitization node (S), with the killing rate of Infection Status node (I) and easily
The immunization rate of state node (S) is felt as control variable；And using the state transfer equation that step S2 is obtained as the first constraint item
Part solves optimum control variable.
Further, the step S1 further include: when node maximum transmitted radius be r, then node can only with the section
Node of the point distance less than r communicates.
Further, between each state node of step S3 it is mutual conversion include it is following step by step:
1), the sensitization node (S) is converted into Infection Status node (I) with the first ratio, sensitization node (S)
Immune state node (R) is converted into the second ratio；
2), the Infection Status node (I) is converted into immune state node (R) with third ratio, Infection Status node (I)
Isolation node (Q) is converted into the 4th ratio；
3), the isolation node (Q) is converted into immune state node (R) with the 5th ratio；
4), the sensitization node (S) or immune state node (R) are converted into dormant state node with the 6th ratio
(P)；The dormant state node (P) is converted into sensitization node (S) or immune state node (R) with the 7th ratio；
5), the sensitization node (S) or Infection Status node (I) or isolation node (Q) or immune state section
Point (R) or dormant state node (P) are converted into dead state node (D) with the 8th ratio.
Further, the step S4 include it is following step by step:
S41, by wireless sensor network to the killing rate of Infection Status node (I) and to sensitization node (S)
Immunization rate obtains domination set as control variable；
S42, objective function；
Wherein, J (u) indicates objective function, ε1Indicate the cost parameter of network killing infection node, ε2Indicate that network is immune
The propagation cost parameter of susceptible node, and ε1、ε2For constant, I (t) indicates the quantity of Infection Status node, and α (t) indicates infection shape
The killing rate of state node (I), μ (t) indicates the immune ratio of sensitization node (S), and the value range of α (t) and μ (t) is 0
Between 1；
S43, according to the objective function of step S42, define the Lagrangian of optimization object function；
S44, Hamilton letter is defined according to the state transfer equation that the Lagrangian and step S3 of step S43 obtain
Number；
S45, the domination set obtained with step S41 are by hamilton's function it can be concluded that optimal for the second constraint condition
Control variable so that in network infect node it is minimum, resist rogue program propagation cost it is minimum.
Beneficial effects of the present invention: the controlling party for inhibiting rogue program to propagate in a kind of wireless sensor network of the invention
Method considers wireless sensor network by the way that dormant state node (P) is added in the node state by wireless sensor network
Sleeping/waking mechanism；By the way that isolation node (Q) is added, the faith mechanism of network is considered；Internetwork time-lag effect, structure
The propagation model of rogue program, more closer to reality are built, can be propagated in wireless sensor network to resist rogue program and be provided
It is effective to help；Meanwhile considering the influence of the immunization rate of network and the killing rate of infection node, cost functional is introduced, is established
Optimal Control Model finds out an optimal solution so that in wireless sensor network malicious node minimum number, and reduce malice
It is minimum that node consumes low number of nodes.
Detailed description of the invention
Fig. 1 is the solution of the present invention flow chart.
Fig. 2 is the state for the control method for inhibiting rogue program to propagate in a kind of wireless sensor network provided by the invention
Conversion figure.
Specific embodiment
For convenient for those skilled in the art understand that technology contents of the invention, with reference to the accompanying drawing to the content of present invention into one
Step is illustrated.
As shown in Figure 1 it is the solution of the present invention flow chart, inhibits to dislike in a kind of wireless sensor network proposed by the present invention
The control method of meaning program circulation, exactly in order to preferably go to resist propagation of the rogue program in wireless sensor network, i.e.,
The quantity of Infection Status node (I) in network is reduced, while also to reduce the cost for resisting rogue program propagation, is i.e. reduction network
Immune cost and killing cost, be to reduce the killing rate of Infection Status node (I) and exempting from for sensitization node (S) at all
Epidemic disease rate.
Technical solution of the present invention the following steps are included:
S1, the network model for determining wireless sensor, specifically: determine node total number, the node is evenly distributed on face
In the long-pending 2 dimensional region for being L × L, and assume to will increase b healthy node replacement dead node within the set time.
And node is propagated follow path loss model in a network, even the maximum transmitted radius of node is r, then the node
It can only be with being communicated with its node of distance less than r.
S20: the attribute of the characteristics of the application further includes determining wireless sensor network and rogue program；
The characteristics of wireless sensor network: after malicious node sends infection data packet constantly into network, network
Trust Management Mechanism to judge out the node be a unsafe node, can be to distrust node, net by the vertex ticks
Other nodes can abandon the data packet of malicious node transmission in network, will not send and communicate with it.
The attribute of the rogue program specifically: after rogue program successfully infects normal node, the infected node
Rogue program will be constantly sent, attempts to infect other nodes.
The propagation model of rogue program in S2, building wireless sensor network: the node state in wireless sensor network
It include: sensitization node (S), Infection Status node (I), immune state node (R) and dead state node (D).
Due to the finite energy ground characteristic of wireless sensor network, sleeping/waking mechanism is introduced, therefore in rogue program
A new state, dormant state state node (P) are introduced in propagation model；The trust of wireless sensor network is considered simultaneously
Administrative mechanism introduces isolation node (Q) concept.
S3, the conversion of each state node are as shown in Figure 2, wherein S (t), I (t), Q (t), R (t), P (t), D (t) are respectively
For t moment sensitization node (S), Infection Status node (I), isolation node (Q), immune state node (R), suspend mode shape
The quantity of state node (P) and dead state node (D).
Mutually converting between each state node is specific as follows:
1) it, for sensitization node (S), is evenly distributed in due to N number of node in the 2 dimensional region that area is L × L,
The maximum transmitted radius of node is r, therefore the sensitization node that an Infection Status node (I) can infect within the unit time
(S) quantity isIt is θ (when the node of a sensitization receives malice journey that Infection Status node (I), which infects success rate,
Sequence has θ probability and is converted into Infection Status node, there is 1- θ probability or sensitization), it enablesTherefore when unit
It is interior to there is the sensitization node (S) of the first ratio lambda to be converted into Infection Status node (I), i.e. a sensitization section of λ S (t) I (t)
Point (S) is converted into Infection Status node (I).
The susceptible Node for having the second ratio μ simultaneously is immune node (R)；Here the second ratio μ is from reality
Rogue program infection network in come out, it is assumed that at a certain moment have in network for the network of rogue program infection
N1A node in easy infection state, M is carved at this1The Node of a easy infection state is immune state, then μ=M1/
N1。
2), for Infection Status node (I), in the unit time, λ S (t) I (t) sensitization node (S) is converted into sense
It contaminates state node (I)；
Due to the killing of network, the Infection Status node (I) for having third ratio α is converted into immune state node (R), together
The third ratio α of reason here is come out from the network that actual rogue program infects, it is assumed that for rogue program sense
The network of dye, it is assumed that network has N in sometime2A node in Infection Status, then be carved with M at this2A infection shape
The Node of state is the node of immune state, wherein α=M2/N2)；
Due to the presence of the Trust Management Mechanism of wireless sensor network, infect node can with the 4th be converted into than γ every
From state node (Q)；γ ratio is abstracted according to actual research network and rogue program situation, different network and evil
Meaning program have different values, according to the actual situation depending on, it is assumed that for rogue program infection network, at a certain moment, network
In have N3A node in Infection Status, and M is carved at this3The node of a Infection Status be converted into after time τ every
Node from state, wherein γ=M3/N3.Assuming that having N in time instant τ3The node of a Infection Status, and M is carved in t+ τ3A sense
Contaminating Node is isolation node, so, γ=M3/N3, that is to say, that M3A Infection Status Node is isolation node
The delay for having τ is the N being directed to apart from τ before the moment3For a Infection Status node.
3), for isolation node (Q), within the unit time, immune state node can be converted into the 5th ratio beta
(R), the 5th ratio beta here is come out from the network that actual rogue program infects, it is assumed that for rogue program
The network of infection has N at a certain moment4A node in isolation, then be carved with M at this4The node of a isolation turns
The node of immune state is turned to, wherein β=M4/N4。
4), due to the characteristic of the finite energy of wireless sensor network, sleeping/waking mechanism is introduced, therefore in malice journey
A new state, suspend mode shape node (P), while only sensitization node (S) and immune shape are introduced in the propagation model of sequence
State node (R) can be with the 6th ratio lambdaSIt is converted into dormant state node (P)；Suspend mode node can be with the 7th ratio lambdaWIt is converted into simultaneously
Sensitization node (S) and immune state node (R).
Similarly, λSRatio and λWRatio has different values from rogue program in different networks, according to the actual situation and
It is fixed；λSWith λWRatio is abstracted according to actual research network and rogue program situation, different network and rogue program meeting
Have different values, according to the actual situation depending on, for λSRatio, it is assumed that for the network of rogue program infection, at a certain moment, net
There is N in network5A sensitization node (S) or immune state node (R), and M is carved at this5A sensitization node (S) and
M5A immune state node (R) is converted into dormant state node (P) simultaneously, wherein λS=M5/N5.Certainly in actual network,
The quantity of sensitization node (S) and immune state node (R) is simultaneously unequal, but they can be simultaneously with identical λSRatio turns
Turn to dormant state node (P).
5), in a network, in the unit time, the node of other states can be converted into dead state node with the 8th ratio η
(D)()；Similarly, η ratio has different values from rogue program in different networks, according to the actual situation depending on.Here its
His state node includes: sensitization node (S), Infection Status node (I), immune state node (R), dormant state node
(P) and isolation node (Q).
S (t), I (t), Q (t), R (t), P (t), D (t) are respectively the susceptible node of t moment, infection node, isolation node, exempt from
The quantity of epidemic disease node, suspend mode node, death nodes, by foregoing description can must do well between transfer equation:
S4, according to the quantity of Infection Status node (I), wireless sensor network in network to Infection Status node (I)
Killing rate and objective function is constructed to the immunization rate of sensitization node (S)；And the state transfer equation obtained with step S2
As the first constraint condition, the least cost of defence rogue program infection wireless sensor network is acquired.
Specifically include it is following step by step:
S41, in order to realize above-mentioned optimization aim, above-mentioned ask is solved using the theory of optimal control of Pontryagin
Topic.Define ε1The cost parameter of node, ε are infected for network killing2The propagation cost parameter of susceptible node is immunized for network, uses
ε1α2(t) and ε2μ2(t) cost that network carries out killing malicious node cost and immune programme, ε are respectively indicated1、ε2For constant.
Domination set is determined first, due to selecting the killing rate α (t) of Infection Status node (I) and sensitization node (S)
Immune ratio μ (t) is as control variable, therefore the value range of α (t) and μ (t) is between 0 and 1.Therefore domination set are as follows: U={ u
=(α, μ) | 0≤α (t)≤1,0≤μ (t)≤1 }, wherein α is killing rate of the network to malicious node, and μ is the immune of network
Rate.
S42, the objective function being defined as follows:
Its domination set is the U in S41, meanwhile, the state of step S3
Equation is another constraint of objective function.
S43, the Lagrangian for defining optimization object function: the objective function defined by step S42, available drawing
Ge Lang function: L (I, u)=I (t)+ε1α2(t)+ε2μ2(t)；
S44, the hamilton's function that the optimization aim is defined by the state equation of Lagrangian and model built,
It is specific as follows:
H (t)=L (I, u)+λ1(t)(b-λS(t)I(t)-μ(t)S(t)-λsS(t)+λwP(t)-ηS(t))
+λ2(t)(λS(t)I(t)-α(t)I(t)-γI(t-τ)-ηI(t))
+λ3(t)(γI(t-τ)-βQ(t)-ηQ(t))
+λ4(t)(α(t)I(t)+μ(t)S(t)+βQ(t)+λwP(t)-λsR(t)-ηR(t))
+λ5(t)(λsS(t)+λsR(t)-2λwP(t)-ηP(t))
λ in S45, above-mentioned hamilton's functioni(t) it is adjoint variable, adjoint variable meets the following conditions:
And for above-mentioned adjoint variable, transversality condition λi(T)=0, i=1,2,3,4,5.
According to hamilton's function, optimum control variable u can be obtained*=(α*(t),μ*(t)), so that minJ (u)=J
(u*), J (u) known to from step S42 indicates objective function, that is, cost function, and wherein u is dependent variable, and u=(α (t),
μ (t)), an object of the application is exactly to acquire a suitable u=u*So that J (u) is minimum.
By seeking hamilton's function the local derviation of α (t) Yu μ (t), governing equation is obtained:
By the domination set U of step S41, available following optimal control pair:
While the quantity of node can obviously be infected in reducing network by the technical solution of the application, minimum allelopathic
Contaminate state node (I) killing rate α (t) and sensitization node immune ratio μ (t), reduce defence rogue program propagate at
This.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.For ability
For the technical staff in domain, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made
Any modification, equivalent substitution, improvement and etc. should be included within scope of the presently claimed invention.
Claims (2)
1. the control method for inhibiting rogue program to propagate in a kind of wireless sensor network characterized by comprising
S1, the network model for determining wireless sensor: determining node total number, and node is evenly distributed on the 2 dimensional region of known area
It is interior, and assume to will increase equal number health node replacement dead node within the set time；
S2, by the node division in wireless sensor network are as follows: sensitization node (S), Infection Status node (I), barrier-like
State node (Q), immune state node (R), dormant state node (P) and dead state node (D)；
S3, according to the mutual conversion between each state node, obtain state transfer equation；Between each state node of step S3
It is mutual conversion include it is following step by step:
1), the sensitization node (S) is converted into Infection Status node (I) with the first ratio, and sensitization node (S) is with
Two ratios are converted into immune state node (R)；
2), the Infection Status node (I) is converted into immune state node (R) with third ratio, and Infection Status node (I) is with
Four ratios are converted into isolation node (Q)；
3), the isolation node (Q) is converted into immune state node (R) with the 5th ratio；
4), the sensitization node (S) or immune state node (R) are converted into dormant state node (P) with the 6th ratio；Institute
It states dormant state node (P) and sensitization node (S) or immune state node (R) is converted into the 7th ratio；
5), the sensitization node (S) or Infection Status node (I) or isolation node (Q) or immune state node (R)
Or dormant state node (P) is converted into dead state node (D) with the 8th ratio；
S4, the killing according to the quantity, wireless sensor network of Infection Status node (I) in network to Infection Status node (I)
Rate and objective function is constructed to the immunization rate of sensitization node (S), with the killing rate of Infection Status node (I) and susceptible shape
The immunization rate of state node (S) is as control variable；And the state transfer equation obtained using step S2 is asked as the first constraint condition
Solve optimum control variable；The step S4 include it is following step by step:
S41, by wireless sensor network to the killing rate of Infection Status node (I) and sensitization node (S) is immunized
Rate obtains domination set as control variable；
S42, objective function；
Wherein, J (u) indicates objective function, ε1Indicate the cost parameter of network killing infection node, ε2It is susceptible to indicate that network is immunized
The propagation cost parameter of node, and ε1、ε2For constant, I (t) indicates the quantity of Infection Status node, and α (t) indicates Infection Status section
The killing rate of point (I), μ (t) indicates the immune ratio of sensitization node (S), and the value range of α (t) and μ (t) is in 0 and 1
Between；
S43, according to the objective function of step S42, define the Lagrangian of optimization object function；
S44, hamilton's function is defined according to the state transfer equation that the Lagrangian and step S3 of step S43 obtain；
S45, the domination set obtained with step S41 is the second constraint conditions, by hamilton's function it can be concluded that optimal control
Variable processed, so that infecting in network, node is minimum, and the cost for resisting rogue program propagation is minimum.
2. the control method for inhibiting rogue program to propagate in a kind of wireless sensor network according to claim 1, special
Sign is, the step S1 further include: when the maximum transmitted radius of node is r, then node only with the nodal distance less than r's
Node communication.
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| CN107104965B (en) * | 2017-04-25 | 2020-12-25 | 成都瑞沣信息科技有限公司 | Malicious program propagation control method based on mobile immunity |
| CN108769939A (en) * | 2018-05-15 | 2018-11-06 | 重庆三峡学院 | A kind of wireless sensor network multipath transmitting fault-tolerance approach |
| CN109246106B (en) * | 2018-09-17 | 2022-05-24 | 平安科技（深圳）有限公司 | Method, device, equipment and storage medium for inhibiting worm virus program |
| CN111343178B (en) * | 2020-02-25 | 2022-05-06 | 广州大学 | Attack and defense method of multistage wireless chargeable sensor network on malicious programs |
| CN111343180B (en) * | 2020-02-25 | 2022-03-22 | 广州大学 | Multi-type malicious program attack and defense method based on nonlinear chargeable sensor network model |
| CN112469041B (en) * | 2020-11-30 | 2022-11-04 | 广州大学 | Malicious program isolation and control method based on wireless sensor network |
| CN113015169B (en) * | 2021-02-24 | 2022-08-09 | 广州大学 | Optimal control method, device and medium for malicious program propagation of charging wireless sensor network |
| CN115002777A (en) * | 2022-04-22 | 2022-09-02 | 广州大学 | Optimal control method for malicious software propagation in smart fishery underwater acoustic sensor network |
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