CN106453217A - Network attack path behavior prediction method based on path revenue calculation - Google Patents
Network attack path behavior prediction method based on path revenue calculation Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- 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
- H04L63/1433—Vulnerability analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/02—Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- 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
- H04L63/1441—Countermeasures against malicious traffic
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Abstract
The invention provides a network attack path behavior prediction method based on path revenue calculation. The method comprises the steps of acquiring a to-be-evaluated network; acquiring all nodes in the to-be-evaluated network; calculating each ratio between revenue and cost of a path between two nodes; eliminating the paths with ratios which are smaller than one between the revenue and the cost from all the nodes, obtaining the paths after redundant path elimination as attack paths; calculating confidences of the nodes contained in the attack paths by means of a likelihood weighting algorithm; and outputting the attack paths and the confidences of the nodes contained in the attack paths. The network attack path behavior prediction method improves path prediction accuracy and can be widely applied in a network safety field.
Description
Technical field
The present invention relates to the Forecasting Methodology of network attack path behavior, particularly relate to a kind of based on path income calculation
The Forecasting Methodology of network attack path behavior.
Background technology
The attack that computer network system faces in essence, is owing to computer system itself exists leak,
And the threat of outside utilizes these leaks existing or fragility offensive attack, thus cause the generation of attack.In order to answer
To the network security problem becoming increasingly conspicuous, except designing as far as possible in addition to security system, also need the fragility of coupling system to net
Network is attacked and is effectively predicted and defend.
In recent years, researcher starts application Bayesian network and attack graph in the prediction of attack.Bayesian network
Network has the advantages that to process uncertain data, and attack graph can assess system CVSS based on tender spots, automatically identifies possible
Tender spots, and then by analyzing tender spots dependence, show all attack paths, finally play integrated estimation system and become safely
The effect of gesture.Relative to attack graph, Bayesian network node and directed edge describe attack and the cause and effect of node resource depends on
The relation of relying, describes uncertainty relation between node by confidence level, possesses the ability processing uncertainty relation.Thus, how will
Bayesian network effectively merges with attack graph, and then Forecast attack path becomes the problem needing to solve accurately.
In a word, in prior art, with increasing of target network node, the attack path of generation exponentially increases, and
Along with substantial amounts of path redundancy, and then affect the forecasting accuracy of attack path.
Content of the invention
In view of this, a kind of network attack path row based on path income calculation of offer is provided
For Forecasting Methodology, improve path prediction accuracy.
In order to achieve the above object, the technical scheme that the present invention proposes is：
A kind of Forecasting Methodology of the network attack path behavior based on path income calculation, including：Obtain network to be assessed；
Obtain all resource nodes in described network to be assessed and attack node；Intermediary resources node is connected with more than one forerunner
Attacking node and attacking node with more than one rear-guard, starting resource node is connected with more than one rear-guard and attacks node, mesh
Mark resource node is connected with more than one predecessor attack node；Wherein, resource node includes as network attack starting point
Starting resource node, save with target as being positioned at start node on the target resource node of network attack impact point, network path
Intermediary resources node between point；It according to network attack path, according to sequence of attack, is arranged on before the resource node being hacked
And the attack node being directly connected to the resource node being hacked is as predecessor attack node, is arranged on the resource node being hacked
The attack node being directly connected to below and with the resource node being hacked is as predecessor attack node；Calculate each described attack to save
Ratio between path income between point and resource node and cost；From between above-mentioned all of attack node and resource node
Path in remove the path less than 1 for the ratio between income and cost, obtain the path after removing redundant path, as attack
Path；Utilize likelihood weighting algorithm, calculate the confidence level of contained attack node and resource node on described attack path；Output institute
State the confidence level of contained attack node and resource node on attack path and described attack path.
In sum, the Forecasting Methodology based on the network attack path behavior of path income calculation of the present invention is passed through
The calculating of node belief is converted into the calculating of attack cost-income by NVAG model, and by relatively low to some weights
Resource node with attack node mark reduce redundant path.Further, the node identification for AND relation node, this
Invention is added up mode by weights, ensure not increase redundant path and do not lose may the active path of attack on the premise of,
Improve the accuracy of node belief.
Brief description
Fig. 1 is the flow chart of the Forecasting Methodology of the network attack path behavior based on path income calculation of the present invention.
Fig. 2 is network attack path NAP schematic diagram of the present invention.
Fig. 3 is the schematic diagram that the attack graph based on fragility of the present invention generates prototype.
Fig. 4 is NVAG model schematic of the present invention.
Fig. 5 is the generation process schematic of NAP of the present invention.
Fig. 6 is attack graph schematic diagram of the present invention.
Fig. 7 is NAP1 node belief of the present invention contrast schematic diagram.
Fig. 8 is NAP2 node belief of the present invention contrast schematic diagram.
Detailed description of the invention
For making the object, technical solutions and advantages of the present invention clearer, right below in conjunction with the accompanying drawings and the specific embodiments
The present invention is described in further detail.
Fig. 1 is the Forecasting Methodology of the network attack path behavior based on path income calculation of the present invention.Such as Fig. 1 institute
Showing, the present invention, based on the Forecasting Methodology of the network attack path behavior of path income calculation, comprises the steps：
Step 11, obtains network to be assessed.
Step 12, obtains all resource nodes in described network to be assessed and attack node；Intermediary resources node connects
Having more than one predecessor attack node to attack node with more than one rear-guard, starting resource node is connected with more than one
Rear-guard attacks node, and target resource node is connected with more than one predecessor attack node；Wherein, resource node includes as net
Network is attacked the starting resource node of starting point, has been positioned on the target resource node of network attack impact point, network path
Intermediary resources node between beginning node and destination node；It according to network attack path, according to sequence of attack, is arranged on and is hacked
Resource node before and the attack node that is directly connected to the resource node being hacked as predecessor attack node, be arranged on by
The attack node being directly connected to after the resource node attacked and with the resource node being hacked is as predecessor attack node.
In the present invention, network attack path NAP (Network Attack Path) is that network objectives resource is entered by assailant
When row is attacked, first starting resource node is launched a offensive, change its state, then on this basis other resource nodes are entered
Row is attacked.It is so repeated, until finally occupying the set of paths of destination node.As in figure 2 it is shown, assailant is from starting resource
Node r1Set out, need to be through attacking node a1, intermediary resources node r2, attack node a2Or through attacking node a1, intermediary resources
Node r3, expose one's past misdeeds node a2, eventually arrive at target resource node r4.Wherein, by r1、a1、r2、a2、r4The joint forming sequentially
Point sequence is a NAP.Same r1、a1、r2、a2、r4This ordered nodes is also a NAP.
Step 13, calculates the ratio between the path income between each described attack node and resource node and cost；
Remove the road less than 1 for the ratio between income and cost from the path between above-mentioned all of attack node and resource node
Footpath, obtains the path after removing redundant path, as attack path.
Step 14, utilizes likelihood weighting algorithm, calculates putting of contained attack node and resource node on described attack path
Reliability.
Step 15, exports the confidence of contained attack node and resource node on described attack path and described attack path
Degree.
In the present invention, step 11 obtains the mode of assessment network as it is shown on figure 3, first, utilize network scanner to mesh
Mark computer and network of relation are scanned, and the data obtaining related node fragility add system mode queue to；Secondly,
System extracts any bar status information automatically in the middle of state queue, and this information is carried out information pair by network knowledge security vault module
Ratio：If this information verifies as dangerous, then analyzing this information, and then determining tender spots feature, record is in tender spots list storehouse
In CVSS.The present invention combines tender spots scanning tools, accelerates the unsafe factor in the middle of discovery main frame and network, improves system true
Determine speed and the degree of accuracy of non-safety information.Tender spots feature in tender spots list storehouse in CVSS is carried out by intrusion feature database
Extract and sum up, obtain the attack signature of non-safety information, determine its harmfulness.Finally, attack graph maker is special according to attacking
Levy storehouse and generate attack graph, and export attack graph by visualization tool.
Step 13 includes：
Step 131, generates network vulnerability attack graph NVAG according to all attack nodes of described network with resource node.
Step 132, from described network vulnerability attack graph NVAG (NVAG, network vulnerability attack
Graph), in, the path attacking status indicator without false is traveled through out, as attack path.
In the present invention, NVAG=(R, A, E, Val (ri), P, C) for having having of one or more AND-OR node composition
To acyclic figure.Wherein, R={ri| i=0,1,2,3 ... N} is each resource node set, r0Represent starting resource node, rgRepresent
Target resource node；Resource node riValue is 1 or 0, especially, ri=1 expression assailant successfully occupies this resource node,
ri=0 represents unsuccessful this resource node that occupies of assailant, 1≤g≤N, and sets r0=1, g, N are integer.A={aj|j
=1,2,3 ... N}, for attacking the set of node, is the AND-OR node set of a nonempty finite, node variable ajValue is
True or false.E={e | e ∈ (R × A) ∪ (A × R) } represent all attack nodes and resource node in network attack map
Directed edge between connection.e1=<aj, ri>∈ A × R represents ajFor for occupying resource riOccur attack, when
Directed edge<aj, ri>The jth of upper generation time attacks weights ΦjWhen >=1, then directed edge；Otherwise,.e2=<riaj>∈ R × A is then contrary, represents that assailant occupies resource node riAfter again carry out attack row
For aj.WhereinFor resource node assets value set, and riBy following four
The key element tolerance being associated：Enterprise's secret (CS) leakage, personal information (PI) leakage, coverage (RI) and the wealth relating to
Produce loss (PD).Assets value computing formula is as follows：
Here, w1, w2, w3, w4It is riThe measurement factor that resource node is associated weights shared in assets value.LCS,
LPI, LRI, LPDFor reciprocity element level degree.P={p | p ∈ C1∪C2, C1For meeting attack ajSelect the bar occurring
Part probability distribution, C2For attack ajSuccessful conditional probability distribution：Work as riOccupied sometimes could meet attack ajSend out
Carded sliver part, now ajCan select to occur or do not attack, therefore p=(aj=ture | riOccupied) ∈ [0,1], p ∈ C1：If
ajSelect attack, go to occupy ri+kResource node, occupying result is success or failure, therefore p=(ajSuccess | ajOccur
Attack) ∈ [0,1], 1≤k≤N, i+k≤N and k is integer, p ∈ C2.AND relation represents all of child nodes Kid(n)Simultaneously
Meet directive property condition and just can reach father node Father(n).The all of child nodes of OR relational representation meets any one refer to
Tropism condition can reach father node.In conjunction with actual present invention definition, when the income of assailant's offensive attack is less than attack cost
When, assailant then attacks substantially without to this target.Thus, network manager can judge to attack on this basis
The attack path that the person of hitting may select, and then take effective safeguard procedures, reduce the successful probability of attack.
Step 132 includes：
Step 1321, according to the node topology order Ψ in described network vulnerability attack graph NVAG, crisp to described network
Each in weak sexual assault figure NVAG is attacked node and is traveled through with resource node.
Step 1322, it is judged that attack the directed edge between node and resource node in described network vulnerability attack graph NVAG
Direction.
Step 1323, if attacking node to resource node<aj, ri>Directed edge, then judge described oriented
Ratio Φ between the path income on limit and costj；Wherein, i is the identification number of described resource node, and j is described attack node
Identification number, ajIt is j-th attack node, riIt is i-th resource node, attack and pass through directed edge between node and resource nodeConnect, and i, j are natural number.
Step 1324, if Φj>=1, thenOtherwise, then.
Step 1325, is identified according to the attack state to described attack node for the judged result.
Step 1326, finds out and does not contains the path that false attacks status indicator, as attack path.
Wherein, the ratio between the income in described path and cost calculates according to below equation：Φj≈Gainj/Costmax；
Wherein, identification number be j attack financial value Gain when node carries out network attackj=valiλjαj；Costj maxFor maximum
Attack cost value；1≤j≤N, N are the total quantitys attacking node in network；valiIt is network attack ajTo riAssets value；λj
It is network attack ajTo riThe control authority of obtained networking component corresponding grade weights after success；αjRefer to this attack
The Land use systems of middle fragility affects coefficient to assailant's income.
Described resource node riCorresponding assets value is：vali=w1Lcs+w2LPI+w3LRI+w4LPD；Wherein, valiResource
Node riThe key element tolerance that corresponding assets value is associated by following four：Enterprise secret CS reveals, personal information PI is revealed,
Coverage RI and property loss PD relating to；w1, w2, w3, w4It is resource node riThe measurement factor being associated is in assets
Weights shared in value；LCS, LPI, LRI, LPDFor reciprocity element level degree.
Step 132 also includes：
Step 1327, to e1The directed edge that there is AND relation in set judges, calculates more than two oriented respectively
Ratio Φ between limit income and costj, it is then added, obtain ratio and ∑ Φj.Wherein, e1=<aj, ri>}∈A×R
Represent and resource node riThe all attack nodes connecting are to resource node riThe attack collection of all attacks composition initiated
Close；A={aj| j=1,2,3 ..., N} is for attacking the set of node；R={ri| i=0,1,2,3 ..., M} is resource node set,
M is the number of resource node, and M is integer.
Step 1328, if ratio and ∑ Φj>=2, then described directed edge is labeled as true；It is otherwise labeled as false.
The directed edge being labeled as false is stored attack node set G abandoned by step 1329xIn.
Step 13210, according to attack node set G abandonedx, remove redundant path.
Step 14 includes：
Step 141, extracts n sample from the attack node and resource node of described attack path；Wherein, resource node
Sample number be pRResource node R that is individual, that obtain with extractionkThe rear-guard connecting attacks node AmSample number have pAIndividual, and pR≥
pA、pR+pA<n；K, m are natural number, and k<i、m<j.
Step 142, when the attack node that sampling obtains or resource node all identify with false, then this sampling obtains
The extraction weight attacking node or resource node be 0；The resource node obtaining when sampling without false mark and is initial
During resource node, then the extraction weight of the resource node that this sampling obtains is 1.
Step 143, in sampling process, if sampling order is followed successively by the rear-guard that resource node is connected with resource node
Attack node, then attacking of the described attack path before sampling searches the resource joint obtaining with sampling node and resource node
Point RkThe all predecessor attack node A connectingm-1, and obtain the resource node R that extraction obtainskExtraction weightThe resource node R obtaining with extractionkThe rear-guard connecting attacks node AmExtraction weight ΔA=P (Am
|Rk)；Wherein, P (Rk|Am-1) at predecessor node Am-1In the presence of by network attack obtain extraction obtain resource joint
Point RkProbability, P (Am|Rk) for extracting the resource node R obtainingkIn the presence of extraction obtain this rear-guard node AmGeneral
Rate；T is natural number, and t is the resource node R obtaining with samplingkThe all predecessor attack node A connectingm-1Number；
In sampling process, if sampling order is followed successively by predecessor attack node, the resource node being connected with resource node,
Then attacking of the described attack path before sampling searches the predecessor attack node A obtaining with sampling node and resource nodem-1
The all resource node R connectingk-1, and obtain the predecessor attack node A that sampling obtainsm-1Extraction weightThe predecessor attack node A obtaining with extractionm-1The resource node R connectingkExtraction weight ΔR'=
P(Rk|Am-1)；Wherein, P (Am-1|Rk-1) at the predecessor attack node A obtaining with samplingm-1The all resource node R connectingk-1
In the presence of extract the predecessor attack node A obtainingm-1Probability, P (Rk|Am-1) save for the predecessor attack obtaining in extraction
Point Am-1In the presence of obtain the resource node R that obtains of extraction by network attackkProbability；V is natural number, and v for
The predecessor attack node A that sampling obtainsm-1The all resource node R connectingk-1Number.
Step 144, if sampling order is followed successively by the rear-guard attack node that resource node is connected with resource node, then obtains
Confidence level P (A | R) the ≈ Δ of contained attack node on the described attack path obtainingA/ΔR, resource node confidence level P (R |
A)=ΔR/ΔA；If sampling order is followed successively by predecessor attack node, the resource node being connected with resource node, then acquire
Described attack path on confidence level P ' (A | R) the ≈ Δ of contained attack nodeA′/ΔR', the confidence level P ' of resource node (R | A)
=ΔR′/ΔA′.
Embodiment
In the present embodiment, NVAG model is as shown in Figure 4.Assailant is with r0As start node, with r5As destination node,
Reaching destination node from start node has 3 paths.In order to clearly show that adjacent each resource node or attack relationships between nodes, this
Invention uses oriented partial ordering relation collection incompatible expression attack path, i.e. NAPi=<r0, a1>,<a1, r1>,<r1, a2>...,<an,
rn>, symbolization ∧ represents the AND relation between node.So, the present embodiment has three attack paths：
NAP1=<r0, a1∧a2>,<a1∧a2, r1>,<r1, a4>,<a4, r3>,<r3, a7>,<a7, r5>}.
NAP2=<r0, a3>,<r0, a1∧a2>,<a3, r2>,<r2∧<a1∧a2, r1>, a6>,<a6, r4>,<r4, a7>,<
a7, r5>}.
NAP3=<r0, a3><a3, r2>,<r2, a5>,<a5, r3>,<r3, a7>,<a7, r5>}.
Attack is once to utilize process to node fragility, fragility utilize rule Rule=(Pre-resource,
Vul, Post-resource).When attacking premise resource Pre-resource and meeting, can be according to fragility Vul of this node
Initiate a step in a network to attack, and after assailant successfully initiates this time to attack, it is possible to obtain corresponding attack result, obtain
Resource Post-resource after attack.
Pre-resource is to be obtained by the Land use systems of node fragility, utilizes the resource obtaining to combine node crisp
Weak property carries out attack next time, and then obtains final goal resource.And common fragility Land use systems includes：Change control parameter
MCPa, change measurement parameter MMPa, the distorting MCPr, intercept critical data information SPa, dereference server and hunt of control program
Take control authority or password GPr etc..
In actual application, can forbid that undelegated people arbitrarily changes net by the rights management of different stage as shown in table 1
Network control assembly configuration, parameter and duty, it is ensured that the safety of equipment and control target.
Table 1 control authority rank and corresponding description
Attack result Post-resource=(Authority, Gain), represents the networking component control that assailant obtains
Permission Levels and assailant successfully utilize the income that fragility obtains.
For the network attack of an attack path jth time, i.e. ajTo ri, its income is：Gainj=valiλjαj；Wherein,
1≤j≤N, valiIt is network attack ajTo riThe corresponding assets value of resource node；λjIt is successfully to carry out after jth time attacks, institute
Obtain the control authority corresponding grade weights of networking component；αjRefer to the Land use systems of fragility in this attack to attack
Person's income affect coefficient.
Gain that single successfully utilize fragility obtained is given belowjComputational methods：
First, all parameters are given with the form of grade, and carry out initial quantization.Wherein, assets value is associated
Factor, grade classification is as shown in table 2.Weights can be arranged quantify to follow following partial ordering relation：PC ＞ MCP ＞ RE ＞ LCC ＞ W ＞
R ＞ N.The quantization that fragility Land use systems affects factor alpha should follow following partial ordering relation：MCPr ＞ MCPa ＞ MMPa ＞ SPa ＞
GPr.
Secondly, judgment matrix method is used to determine that each consequence factor corresponding to resource node is shared in assets value
Weight w, selects domain expert m people, the ratio construction m's individual 4 × 4 according to the importance degree between the factor of consequence two-by-two that expert is given
Judgment matrix s two-by-two(e)：,
Wherein, m=10～30；E=1,2 ..., m；seElement formulaRepresent the consequence attribute w that expert e providespRelatively
In consequence attribute wqSignificance level.According to the individual judgment matrix { s two-by-two of the m obtaining(1), s(2)..., s(m)After }, geometry is used to put down
Equal method carries out comprehensively obtaining synthetical matrix S, and the element of S isP, q=1,2,3,4.
Finally, characteristic value Sw=γ is solvedmaxMain characteristic vector w is normalized, so that it may obtain consequence attribute weight coefficient by w
Vector w '=(w1, w2, w3, w4)T.Thus obtain w1, w2, w3, w4.
Tender spots is attacked cost and is mainly determined by three factors：Attack degree of difficulty D, hidden degree H of fragility and attack
Hit the time T successfully spending.The attack cost of single tender spots can be expressed as Costj=β1D+β2H+β3T, wherein β1,, β2, β3
For the relative weight of corresponding factor, specifically can choose according to actual conditions.The present invention sets maximum attack cost Costj max, with
Select the path of high yield, and bigger probability determines redundant path；Meanwhile, the attack degree of depth can be limited, decrease insignificant
Attack path.
Before network attack is implemented, can the attack against each other income-cost of beat time point of assailant is estimated analyzing, and uses following formula to sentence
The feasibility of this sub-attack path disconnected：
Wherein, feasibility Φ is attacked in pathjIt is the ratio that assailant attacks income and intrusion scene.Only work as Φj>=1, i.e.
When assailant can obtain income more than or equal to the cost oneself paid, assailant just can carry out attack at this paths,
Final acquisition target of attack.
The present invention shows the generation process of NAP described in the inventive method as a example by Fig. 5.Attack graph according to Fig. 5, this
Attack node each in attack graph, resource node are traveled through by invention by node topology order Ψ：First, it is determined that the side of directed edge
To being whether to attack node to resource node<aj, ri>Directed edge, if then carrying out the judgement of weights, as weights Φj>=1, then；Otherwise,, and be identified this resource node for false.If, then this attack a is meanedjAbandon.
For attack graph as shown in Figure 4, traditional Bayesian inference likelihood weighting algorithm is used to calculate the confidence of node
Degree, result of calculation is shown in Table 3；Here, effective sample number 5000 is tested；C represents C1=0.5 and C2=0.8, d represent C1=0.8
And C2=0.5；P (r0)～P (r5) represents starting resource node r0, intermediary resources node r1～r4, target resource node respectively
The confidence level of r5；P ' (a1)～P ' (a7) represents the confidence level attacking node a1～a7 respectively.
Table 3 tradition Bayesian inference result
| P(r0) | P‘(a1) | P’(a2) | P’(a3) | P(r1) | P(r2) | P’(a4) | P’(a5) | P’(a6) | P(r3) | P(r4) | P’(a7) | P(r5) | |
| c | 1 | 0.47 | 0.45 | 0.49 | 0.14 | 0.41 | 0.07 | 0.21 | 0.03 | 0.24 | 0.02 | 0.15 | 0.13 |
| d | 1 | 0.75 | 0.77 | 0.79 | 0.16 | 0.39 | 0.14 | 0.31 | 0.05 | 0.23 | 0.03 | 0.21 | 0.11 |
As a example by weighting parameter given in the attack graph shown in Fig. 6, use each resource that the method for the invention obtains
Node, the confidence level of attack node are shown in Table 4；Here, testing effective sample number is 5000.
Each node belief that table 4 the inventive method obtains
| P(r0) | P‘(a1) | P’(a2) | P’(a3) | P(r1) | P(r2) | P’(a4) | P’(a5) | P’(a6) | P(r3) | P(r4) | P’(a7) | P(r5) | |
| c | 1 | 0.43 | 0.47 | 0.45 | 0.13 | 0.36 | 0.05 | 0.18 | 0 | 0.19 | 0 | 0.11 | 0.09 |
| d | 1 | 0.74 | 0.76 | 0.77 | 0.15 | 0.39 | 0.12 | 0.21 | 0 | 0.17 | 0 | 0.14 | 0.06 |
The contrast of data in table can be obtained each resource node, the confidence level attacking node that two kinds of methods acquire
Situation of change.For the change of observed data more intuitively, with path NAP1And NAP2As a example by, draw out node belief and become
Changing in figure difference Fig. 7, Fig. 8 as shown in Figure 7, Figure 8, abscissa all represents each resource node and attacks node, and ordinate all represents
Each resource node, the attack corresponding confidence level of node, Ta represents node belief change when traditional algorithm condition is c, and Tb is then
Representing the change of node belief when traditional algorithm condition is d, it is different c, d two kinds that Ia with Ib then illustrates the inventive method
Under the conditions of node belief change.
By Fig. 7 and 8 it can be seen that the node belief that algorithms of different obtains is different：The inventive method has confidence
Degree is the node of 0, and in conventional method, confidence level is not the node of 0.Use the target resource node that the inventive method obtains
Confidence level substantially low than the confidence level of target resource node using conventional method to obtain.This is owing to conventional method does not considers
To path redundancy problem and income-Cost Problems, all paths can be selected to carry out attacking for it so that the confidence of target resource node
Spend higher；And the inventive method solves path redundancy problem and income-Cost Problems simultaneously, assailant can select to oneself
Beneficial or the superseded attack path nonsensical to oneself so that the confidence level of target nature node is relatively low.
The present invention also has an improvement, in conjunction with in the table 4 that the weighting parameter given in the attack graph shown in Fig. 6 obtains
Data, it can be observed that, the node belief of a2 is not 0, here it is another of the present invention improves, AND node is designated
The premise of false is no longer only to calculate the weights on a limit, and is by two even more polygon weights and adds up, and then judges
Whether carry out " abandoning attacking ".
The invention have the advantages that：(1) prediction of attack and defence is always network safety filed research
Focus, attack graph combines node fragility as the effective Forecasting Methodology of one, the present invention, by definition NVAG model, enter
And the calculating of node belief is converted into the calculating of attack cost-income, by entering the node that some weights are relatively low
Line identifier, further reduces redundant path.Incorporate the reasoning algorithm of Bayesian network on this basis, utilize prior probability meter
Calculate posterior probability, further increase the accuracy of node belief.
(2), in improved likelihood weighting algorithm, for the node identification problem of AND relation node, the present invention proposes power
The cumulative mode of value solves, and i.e. will not increase redundant path, and will not let slip the active path that assailant may select.Real
Test result show the present invention work can more effective Forecast attack path and calculate node belief, and then reduce insincere
Path redundancy, provide good preventative strategies for network security management.It is to say, not only effectively reduce redundant path,
It also avoid attacking ignoring of leak of AND relation node, and then effectively increase the accuracy of path prediction.
In sum, these are only presently preferred embodiments of the present invention, be not intended to limit protection scope of the present invention.
All within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. made, should be included in the present invention's
Within protection domain.
Claims (8)
1. the Forecasting Methodology based on the network attack path behavior of path income calculation, it is characterised in that include：
Obtain network to be assessed；
Obtain all resource nodes in described network to be assessed and attack node；Intermediary resources node is connected with more than one
Predecessor attack node attacks node with more than one rear-guard, and starting resource node is connected with more than one rear-guard and attacks joint
Point, target resource node is connected with more than one predecessor attack node；Wherein, resource node includes initiateing as network attack
It the starting resource node of point, on the target resource node of network attack impact point, network path, is positioned at start node and mesh
Intermediary resources node between mark node；It according to network attack path, according to sequence of attack, is arranged on the resource node being hacked
The attack node above and with the resource node being hacked being directly connected to, as predecessor attack node, is arranged on the resource being hacked
The attack node being directly connected to after node and with the resource node being hacked is as predecessor attack node；
Calculate the ratio between the path income between each described attack node and resource node and cost；From above-mentioned all of
Attack and the path between node and resource node is removed the path less than 1 for the ratio between income and cost, obtain removing superfluous
Path behind remaining path, as attack path；
Utilize likelihood weighting algorithm, calculate the confidence level of contained attack node and resource node on described attack path；
Export the confidence level of contained attack node and resource node on described attack path and described attack path.
2. method according to claim 1, it is characterised in that described calculating each described attack node and resource node it
Between path income and cost between ratio；Remove from the path between above-mentioned all of attack node and resource node and receive
The path less than 1 for the ratio between benefit and cost, obtains the path after removing redundant path, as the step bag of attack path
Include：
Generate network vulnerability attack graph NVAG according to all attack nodes of described network with resource node；
From described network vulnerability attack graph NVAG, travel through out and do not contain the path attacking status indicator false, as attack
Path.
3. method according to claim 2, it is characterised in that described from described network vulnerability attack graph NVAG, time
Going through out and not containing the path attacking status indicator false, the step as attack path includes：
According to the node topology order Ψ in described network vulnerability attack graph NVAG, to described network vulnerability attack graph NVAG
In each attack node travel through with resource node；
Judge described network vulnerability attack graph NVAG is attacked the direction of each directed edge between node and resource node；
If attacking node to resource node<aj, ri>Directed edgeThen judge the path income of described directed edge
And the ratio Φ between costj；Wherein, i is the identification number of described resource node, and j is the identification number of described attack node, ajFor
Attack node, r j-thiIt is i-th resource node, attack and pass through directed edge between node and resource nodeEven
Connect, and i, j are natural number；
If Φj>=1, thenOtherwise, then
It is identified according to the attack state to described attack node for the judged result；
Find out and do not contain the path that false attacks status indicator, as attack path.
4. method according to claim 3, it is characterised in that the path of described directed edge is benefited and the ratio between cost
For：Φj=Gainj/Costj max；Wherein, GainjCarry out financial value during network attack for the attack node that identification number is j；
Costj maxFor maximum attack cost value.
5. method according to claim 4, it is characterised in that Gainj=valiλjαjWherein, 1≤j≤N, N are in network
Attack the total quantity of node；valiIt is network attack ajTo riAssets value；λjIt is network attack ajTo riObtained after success
The control authority corresponding grade weights of networking component；αjRefer to that assailant is received by the Land use systems of fragility in this attack
Beneficial affects coefficient.
6. method according to claim 5, it is characterised in that described resource node riCorresponding assets value is：vali=
w1Lcs+w2LPI+w3LRI+w4LPD；Wherein, resource node riCorresponding assets value valiThe key element degree being associated by following four
Amount：Enterprise's secret CS leakage, the leakage of personal information PI, coverage RI and property loss PD relating to；w1, w2, w3, w4
It is resource node riThe measurement factor being associated weights shared in assets value；LCS, LPI, LRI, LPDFor reciprocity key element etc.
Stage.
7. method according to claim 2, it is characterised in that from described network vulnerability attack graph NVAG, travel through out
Not containing false and attacking the path of status indicator, the step as screening attack path also includes：
Gather e to attacking1The middle directed edge that there is AND relation judges, calculates directed edge income and the generation of more than two respectively
Ratio Φ between valencyj, it is then added, obtain ratio and ∑ Φj；
If ratio and ∑ Φj>=2, then described directed edge is labeled as true；It is otherwise labeled as false；
The directed edge being labeled as false is stored attack node set G abandonedxIn；
According to attack node set G abandonedx, remove redundant path；
Wherein, e1=<aj, ri>∈ A × R represents and resource node riThe all attack nodes connecting are to resource node riInitiate
All attacks composition attack set；A={aj| j=1,2,3 ..., N} is for attacking the set of node；R={ri| i=
0,1,2,3 ..., M} is resource node set, and M is the number of resource node, and M is integer.
8. method according to claim 7, it is characterised in that described utilize likelihood weighting algorithm, calculates described attack road
On footpath, contained attack node includes with the step of the confidence level of resource node：
N sample is extracted from the attack node and resource node of described attack path；Wherein, the sample number of resource node is pR
Resource node R that is individual, that obtain with extractionkThe rear-guard connecting attacks node AmSample number have pAIndividual, and pR≥pA、pR+pA＜ n；
K, m are natural number, and k ＜ i, m ＜ j；
When the attack node that obtains of sampling or resource node all identify with false, then the attack node that this sampling obtains or
The extraction weight of resource node is 0；When the resource node that sampling obtains identifies without false and is starting resource node,
The extraction weight of the resource node that then this sampling obtains is 1；
In sampling process, if sampling order is followed successively by the rear-guard attack node that resource node is connected with resource node, then from
The resource node R obtaining with sampling is searched in attacking of described attack path before sampling in node and resource nodekThe institute connecting
There is predecessor attack node Am-1, and obtain the resource node R that extraction obtainskExtraction weightWith take out
Obtain the resource node R arrivingkThe rear-guard connecting attacks node AmExtraction weight ΔA=P (Am|Rk)；Wherein, P (Rk|Am-1) be
At predecessor node Am-1In the presence of obtain the resource node R that obtains of extraction by network attackkProbability, P (Am|Rk) be
The resource node R that extraction obtainskIn the presence of extraction obtain this rear-guard node AmProbability；T is natural number, and t is and takes out
The resource node R that sample obtainskThe all predecessor attack node A connectingm-1Number；
In sampling process, if sampling order is followed successively by predecessor attack node, the resource node being connected with resource node, then from
The predecessor attack node A obtaining with sampling is searched in attacking of described attack path before sampling in node and resource nodem-1Connect
All resource node Rk-1, and obtain the predecessor attack node A that sampling obtainsm-1Extraction weightThe predecessor attack node A obtaining with extractionm-1The resource node R connectingkExtraction weight ΔR’
=P (Rk|Am-1)；Wherein, P (Am-1|Rk-1) at the predecessor attack node A obtaining with samplingm-1The all resource nodes connecting
Rk-1In the presence of extract the predecessor attack node A obtainingm-1Probability, P (Rk|Am-1) attack for the forerunner obtaining in extraction
Beat time point Am-1In the presence of obtain the resource node R that obtains of extraction by network attackkProbability；V is natural number, and v
For the predecessor attack node A obtaining with samplingm-1The all resource node R connectingk-1Number；
If sampling order is followed successively by the rear-guard attack node that resource node is connected with resource node, then acquire is described
Confidence level P (A | R) the ≈ Δ of contained attack node on attack pathA/ΔR, confidence level P (R | the A)=Δ of resource nodeR/ΔA；
If sampling order is followed successively by predecessor attack node, the resource node being connected with resource node, the then described attack acquiring
Confidence level P ' (A | R) the ≈ Δ of contained attack node on pathA′/ΔR', the confidence level P ' of resource node (R | A)=ΔR′/
ΔA′.
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