ResearchArticle …downloads.hindawi.com/journals/mpe/2020/3618284.pdf · 2020. 8. 27. · ResearchArticle AutonomousCognitiveModelandAnalysisforSurvivableSystem YiweiLiao,1 GuoshengZhao

Research ArticleAutonomous Cognitive Model and Analysis for Survivable System

Yiwei Liao1 Guosheng Zhao 1 and Jian Wang2

1College of Computer Science and Information Engineering Harbin Normal University Harbin 150025 China2School of Computer Science and Technology Harbin University of Science and Technology Harbin 150001 China

Correspondence should be addressed to Guosheng Zhao zgswj163com

Received 12 June 2020 Revised 31 July 2020 Accepted 7 August 2020 Published 27 August 2020

Academic Editor Frederic Kratz

Copyright copy 2020 Yiwei Liao et al -is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

-e research on autonomous recognition mechanism for survivability has vigorously been growing up A method of autonomouscognitive model and quantitative analysis for survivable system was proposed based on cognitive computing technology Firstly acognitive model for survivable system with cross-layer perception ability was established a self-feedback evolution mode ofcognitive unit based onmonitor-decide-execute loop structure was improved and a self-configuration of cognitive unit is realized-en combined with the cognitive state transition graph the analysis of cognitive performance for survivable systems based ondynamic cognitive behavioral changes was constructed Finally the cognitive processes of survivable system were described byusing formal modeling Simulation validated the influence degree of test parameters on system survivability from two perspectivesof the probability of intrusion detection systems vulnerability and attacks detected Results show that enhancing the rate ofmonitoring actions change and the rate of performing actions change obviously improved the cognitive performance ofsurvivable system

1 Introduction

Survivability is a hot topic in the research on the next-generation Internet security According to Westmark [1]and Ellison [2] definition survivability can be illustratedfrom three properties resistance recognition and recoveryAmong them recognition reflects the systemrsquos autonomouscognition of its own survival situations and securities of thescene and environment Current research focuses more onthe definition of survivability [1 2] quantitative and qual-itative evaluation [3ndash5] formal description [6ndash8] trustedprotection [9] recovery [10] and other topics in resistanceand recovery But the research on recognition has just begunand is growing

At present consensuses on the research of survivabilitymechanism have been achieved at home and abroad asfollows Recognition refers to the ability that the systempossesses to ldquoknowrdquo and ldquofeelrdquo the current systemrsquos survivalsituation [11] Survivability-oriented recognition gives pri-ority to the perception and cognition of the security status ofthe whole system environment which can be regarded as theidentification of basic key servicesrsquo decline in survivability

and of the attack and intrusion event sets [12] Recognitionmeans the systemrsquos response and adaptability when systemsface malicious intrusion [13] which can reflect systemsrsquoability to assess its own security status and surroundingworking environment which can be analyzed from itsrecognition rate of security incidents and the recognitiontime of nonsecurity incidents Recognition can be achievedby constraining reference thresholds of cognition parame-ters while autonomy can be achieved by the central controlprocess of the autonomous recognition unit [14] Recog-nition can be obtained by establishing a hierarchical per-ception model and making the policy library drive the self-management mode of the monitor-decide-execute (MDE)loop structure [15] Cognitive Computing is a summary ofthe characteristics of the next-generation intelligent Inter-netrsquos core concepts [16] Cognitive computing in the era ofbig data is approaching cognitive science with the abilities ofself-learning self-adaptation and self-perception to realizethe human-brain-like recognition and judgment In thispaper based on previous survivability researches an au-tonomous cognitive model of survivable system is raisedand the model is formalized by using semi-Markov

HindawiMathematical Problems in EngineeringVolume 2020 Article ID 3618284 11 pageshttpsdoiorg10115520203618284

stochastic process algebra [17 18] which provides theo-retical guidance for the study of survivable systemrsquos cog-nitive ability

2 Autonomous Cognitive Model

-e systemrsquos cognitive needs are mapped to the dynamicselection of multiobjective cognitive results at multiplecognitive levels Meanwhile the cross-layer perception isused to obtain the autonomous reasoning dynamic deci-sion-making and resource reallocation of survivable sys-tems and to realize the self-adaptation to dynamic changesof the cognitive needs and environment security In addi-tion cognitive model should reach a balance between formaldescription and cognitive abstraction so it can not onlyaccurately describe and reflect the systemrsquos recognition butalso facilitate reasoning thus providing theoretical supportfor the study of cognitive ability of survivable system

21 Cross-Layer Recognition According to different em-phasis on cognitive process cognitive needs and cognitiveelements the survivable system can be divided into threecognitive layers namely access cognitive layer networkcognitive layer and service cognitive layer as shown inFigure 1

Access cognition layer reflects the recognition of thecommunication capability of available transmission chan-nels which supports protocol conversion and adaptation ofvarious available channels and achieves high reliable in-formation transmission through the recognition of channelsrsquocommunication capability

-e network cognitive layer shows the unified cognitionof cognitive specifications in the cognitive process and goalsof cognitive needs It can realize dynamic reconfigurationand planning constraints of cognitive network resources

-e service cognitive layer reflects the recognition of thematching ability of providing Internet resources required forapplications and users It can serve high QoS service incomplex environment where massive incomplete or evenmalicious service scenarios exist

22 Self-Feedback Mode of Cognitive Units -e cognitiveunit structure is similar to Agent in the traditional sense thebasic unit of the realization of cognitive model [19ndash21]which is also the symbol of the autonomous cognitive abilityof survivable system With the self-feedback ability added onthe basis of the existing cognitive unit structure an im-proved cognitive loop structure is achieved as shown inFigure 2 -is structure is a self-feedback evolving structuredriven by the self-configuring strategy library of cognitiveelements (M-D-E Monitor-Decide-Execute) which canadjust behaviors topology and service parameters with thechanges of working environment and task objectives insideand outside the survivable system Apart from the functionof perceiving contexts of normal network events thestructure can also deal with internal and external securitythreats to enable survivable systems to independently adaptto environment and demand changes

-e self-feedback mechanism of cognitive unit is shown inFigure 3 which includes local domain-level and global feed-backs Each layer is composed of several cognitive units toachieve global domain and local cognition of the systemrsquoscognitive behaviors Results of local feedback can obtain the localoptimal solution to the goal of cognitive needs global feedbackcan coordinate the feedback results at domain and local levelsand obtain the global optimal or suboptimal solution

Cognitive units can obtain self-configuration of cognitiveelements with a self-feedback mechanism -ere are two cases

221 Preset Self-Configuration When matchable strategiesare found in existing strategy libraries the configurationstrategy in the preset cognitive rule set will analyze andreason the system as shown in Figure 4

222 Acquired Self-Configuration When matchable strat-egies cannot be found in strategy library effective rulesachieved after acquisition will be stored as acquired rules inthe configuration strategy library as shown in Figure 5

3 Cognitive Process of Formal Modeling

In order to formally describe the transition between differentstates of the system under attacks faults or accidentalfailures and to better understand the dynamic evolutionprocess of the survivable systemrsquos survival situations acognitive survival state transition diagram [14] is intro-duced as shown in Figure 6

-e tool Version v25 of the PEPA Eclipse Plugin [22] ofthe Computer Science Foundation Laboratory of the Uni-versity of Edinburgh is used to simplify the calculationprocess -e formal description of the cognitive survivalstate for the survivable system in Figure 6 is as follows

(i) Intruder (searching h) Attack(ii) Attack (starck_attack p) (attack k) Attack +

(starck_attack g) Intruder(iii) General (attack z1) Compromised + (failing

z2) Compromised + (error z3) Compromised(iv) Compromised (probe w1) Detection + (mask

w2) General(v) Detection (start_probe L1) (emergency1 p1)

SelfDestruction + (start_probe L2) (healing L3)SelfHealing

(vi) SelfHealing (start_healing L3) (sealheaking s1)General + (start_healing L4) (emergency2 p2)SealDectruction + (strat_healing L5) (selfhealings2) SelfHealing

(vii) SelfDestruction (start_destroy L6) (destroydelta L (s)) (backup s3) General

-e parameters and their meanings are shown in Table 1-e cognitive model of survivable systems can be for-

malized as a quintuple form (Mde Objects Domain) whereMdeMde1 Mde2 Mdem represents the resource con-straint sets of m cognitive units CC (Mdei) represents the

2 Mathematical Problems in Engineering

cognitive sublayer at layer j including i cognitive unit re-sources Objects Object1 Object2 Objects is a set ofcognitive needsrsquo objectives -e single objective Objectk isassociated with the ith cognitive sublayer Cj C (layeri) andsatisfies the mapping function ρ Objectk⟶Cj If there aremultiple cognitive needs objectives in a cognitive sublayer itcan be expressed by a union setObjects1cupObjects2cup cupObjectsq Domain represents the

set of cognitive domains and each subnet i is regarded asDomain Domaini Σ ς1 ς2 L ςn i is the set of actiondecision result functions

Survivable systems provide key services to the outsideworld and users request services -erefore from theperspective of service supply the survivable system ismodeled as two ends User and Server -e User end can berepresented as Userk

j process -e formal description of userend is

(i) Monitor (monitor m) Decide(ii) Decide (decide r1) Execute + (uncertain r2)

Learn(iii) Learn (learning r3) Decide(iv) Execute (execute r4) Monitor + (service1 s1)

Monitor + (servce2 s2) Monitor ++ (servcet st)Monitor

And amodel for Server end is made objective kisinObjectsand the process of Server end is represented as Serverk

jiwhere iisinDomain jisinC (layeri) which satisfy ρk⟶j Fordifferent Domaini processes the rate and number of actionchanges are different-erefore the cognitive process can beshown as

Userkj ||service

k Serverkji

M E

Cognitive normative language

Resources

Survivable system cognitive elements

Cognitive processes

Self-configuration

Host InterchangerRouter Other elements

Application

DEM

Users

154

362Access

channel 1 Accesschannel 2 hellip

D

M E

D

M E

D

M E

D

E

D

M E

D

M E

D

M

Demand analysis

Need goals

Survivablesystem

Networkcognition layer

Accesscognitive layer

Figure 1 -e cross-layer sensing cognitive model

Outside

Sensory input Action output

EM

D

Strategy librarySelf-configuring

M MonitorD DecideE Execute

Figure 2 -e improved cognitive loop structure

Mathematical Problems in Engineering 3

Strategy library

Cognitive elements

Local decision

Effector

Monitoring modulePerform module

Sensor

Strategy library

Cognitive elements

Local decision

Effector


Sensor

Monitoring module Perform module

Domain decision

Strategy library Strategy library

Monitoring module

Global decision

Domain decision

Strategy library

SensorEffector

Monitoring module

Sensor

Perform module

Effector

Sensor

Globaldecision

Domainfeedback

Localfeedback

Figure 3 -e self-feedback model based on the cognitive loop

Outside Self-configuring

Monitoring

Performation

Decision

Preinstall

Acquisition

Strategy libraryCognitive ring

Sensor

Effector

Figure 4 Self-configuration in preset cognitive rule sets


Monitoring

Performation

Decision

Preinstall

Acquisition


Sensor

Effector

Figure 5 Self-configuration in acquisition cognitive rule sets


And service is the collection of all service interactionsFor all the cognitive needs of the system the formal de-scription of server end is

Modelcognitive | (k)User || monitork executekSever kobjects jC (layer1) C (layer2) C (layerp) iDomain

4 Quantitative Analysis

-e PEPA Workbench and Eclipse Plugin cognitive tool isused to quantitatively analyze the model of survivable

system and the quantitative analysis results are obtainedfrom the perspective of steady-state probability

41 Solution of Steady-State Probability For SM-PEPA if

any component P satisfies the formula P⟶(a1 r1)

⟶(anrn)

Pprime Pprimewill be called as the derivation of P and the collection ds(P)

will be the collection of all derivations of P-e state spaceXsis the collection of all nodes of the derivative graph of SM-PEPA and SMP corresponding to SM-PEPA is built XT Xn Tn n 0 1 2 where XnisinXS and whenm n l 1 we get

G C D SD

SH

Under threat

Self-repair self-configuration

Vulnerabilities areexploited

reat blocked

Critical situation

Restore success Restore failureself-destruct condition

Recovery renewal

Restore failurenot self-destruct

G General C CompromisedD Detection SH SelfHealing

SD SelfDestruction

Figure 6 -e transition diagram of cognitive survival state

Table 1 -e explanation of parameters in SM-PEPA

Parameters MeaningsH Probability of finding system flawsP Probability of persistent attackK Duration of attacksG Rate of invaders abandoning attacksZ1 Probability of detecting intrusionsZ2 Probability of stochastic failures entering compromise stateZ3 Probability of entering compromise state because of faulty operationsW1 Probability of compromise state entering detection stateW2 Probability of blocking flawsL1 Probability of detecting state entering self-destructive stateP1 -e duration of the detected state to the self-destructive stateL2 Probability of calling recovering updates from detection stateh1 Duration of calling recovery updatesL3 Probability from self-recovery state to normal stateS1 Duration of self-recovery state to normal stateL4 Probability of self-recovery state to self-destruction stateP2 Duration of self-recovery state to self-destruction stateL5 Probability of self-restoring state to self-restoring stateS2 Duration of self-recovery state to self-recovery stateL6 Repair probability from self-destructive state to normal stateS3 Repair duration from self-destructive state to normal state


Attackrsquo Failurersquo Accidentrsquo Generalrsquo are derivations ofcomponent Attack Failure Accident General respectivelyQ (t) of SMP satisfies

Qij(t) P Xn + 1 j Tn+1 minus Tn le t Xn i11138681113868111386811138681113966 1113967 PijHij

(1)

pij PXn + 1 j Xn i represents the state transition ratebetween i j Hij PTn + 1minusTnle t | jXn + 1 iXn representsthe distribution probability obeyed by action change ratesbetween i and j

-e stable-state probability of Markov can be obtainedafter the following calculations [23]

πi viE Ti1113858 1113859

1113936 jvjE Tj1113960 1113961 i j isin XS (2)

Let XS be any state space and let the correspondingMarkov Chain P (pij) be a state transition matrix

pij limt⟶+infin

Qij(t) (3)

After doing reduction of the model when the delay ofaction obeys the exponential distribution the probability oftransition from state α to state l is pal ral1113936jraj where rαj isthe delay parameter of actions And when the delay timeparameter obeys the general distribution of action d becauseits priority is higher than other actions the probability oftransition to the determined state q is 1 and the probabilityof transition to the rest is 0

-erefore the steady-state rate of embedded semi-Markov Chain satisfies

vrarr

vrarr

P

1113944i

vi 1

⎧⎪⎨

⎪⎩(4)

vrarr

(VG VC VD VSH VSD) is a stationary probability vectorembedded in semi-Markov Chain

When the duration of behaviors in SM-PEPA modelobeys exponential distribution the solution of the model canbe transformed into solving the duration Markov Chaincorresponding to PEPA Assuming that the steady-stateprobability distribution of durationMarkov chains is π(middot) so

πQ 0

1113944n

i1πi 1

⎧⎪⎪⎨

⎪⎪⎩(5)

π π1 π2 is the steady-state probability vector

411 State Transition Matrix Because a survivable systemapplication scenario for the corresponding goal is differentits internal and external environment are also different atthe same time it is limited by many constraints etc soaccording to different application scenario for the conditionsfor survival systems can be divided into five states normalsurvival state (general) compromise survival state (com-promised) cognitive detection state (detection) the re-covery state (selfhealing) and self-destructive state

(selfdestruction) From the state set the state space X GV D SH SD can be obtained and then the DTMC chainjust an example can be obtained as shown in Figure 7

-e above-mentioned parametersrsquo probability values areshown in Table 2

000000000

00000010

p5p4p3VGp2p2

p1p1

SDSHDCG

SDSHDCG

P = (6)

412 Quantification of Evaluation Indicators Based on thestate transition matrix P the corresponding relationshipbetween the evaluation index and the state transitionprobability is established [15]

Recognition p1 TC⟶GResistance p1 + (1minusp1) p2 TC⟶G TC⟶DRecovery 1minusp3minusp4 TSH⟶GReliability 1minusπSD

Among them TC⟶G means the time interval be-tween threat detection and threat processing TC⟶Dmeans the time interval of resisting invasion or attackTSH⟶G is the time interval of system self-recover πSD isthe steady-state probability of system in self-destructivestate TC⟶G TC⟶D TSH⟶G can be obtained from theactual operation of survivable systems through bypassnetwork monitoring tools

413 Solution of Approximate Steady-State ProbabilityAccording to the steady-state distribution value of thesteady-state rate vi embedded in semi-Markov Chain thefive calculating formulas of steady states are as followsVG + VC + VD + VSH + VSD 1

VG 1 minus p3

2 1 minus p3( 1113857 2 minus p1( 1113857 + p2 1 minus p1( 1113857 p3 + p4( 1113857

VC 1 minus p3


VD 1 minus p1( 1113857 1 minus p3( 1113857


VSH p2 1 minus p1( 1113857


VSD 1 minus p1( 1113857 1 minus p2 minus p3 + p2p3 + p2p4( 1113857


(7)

Here we make the average staying time of self-de-structive SD obey subexponential distribution distributionparameters λ1和λ2 while the average staying time of otherstates obeys exponential distribution which is also consis-tent with the actual network situation then the averagestaying time of five states is shown as formula (7)


tG 1λG

tC 1λC

tD 1λD

tSH 1λSH

tSD 1λ1

+1λ2

(8)

-e formula to get the steady-state probability based onthe semi-Markov process is

πi viti

1113944jvjtj

i j isin G C D SH SD (9)

-e steady-state probability of semi-Markov process canbe solved finally To simplify the analysis process a globalcognitive unit is assumed to consist of two domain cognitiveunits Domain_1 and Domain_2 -e approximate steady-state probabilities derived from each cognitive unit areshown in Table 3

42 Quantitative Analysis and Simulation In this paperPEPA Workbench is used to process data files and the toolVersion v25 of the PEPA Eclipse Plugin of the ComputerScience Foundation Laboratory of Edinburgh University isadopted to quantitatively analyze the performance of theproposed cognitive model in terms of resistance recogni-tion and recovery

Due to the addition of cognitive computing features inthe model state space XS can be further divided into col-lection X1 and X2 to represent cognitive and noncognitivesurvivable state collections Each local derivation in X2contains noncognitive survivable state and indefinite state inthe following form X1 x|xDeGradation|| Similarlythe steady-state probability collection π π1 π2 πn can also be divided into two parts corresponding to thesubcollection CD in X1 and the subcollection CUD in X2respectively

-e test parameters are listed in Table 4In order to better measure the impact of the selected

index parameters on the cognitive performance of survivablesystems the resistance parameter h and the cognitive pa-rameter z1 are first examined And then the values of h andz1 are adjusted to maintain the rest of the parameters un-changed -e experimental results are shown in Figures 8and 9

In Figure 8 parameter h means the probability of at-tackers finding system flaws and correspondingly meansthe systemrsquos resistance to attacks -e smaller the value of his the stronger the anti-attack ability of the system becomesWith h decreasing the survivability index of the systemincreases gradually But the resistance of the system is notendless When the value of h reaches 1e-09 the survivabilityindex of the system approaches 10 and gradually becomesstable No matter how strong the attack defense is it ispossible to be invaded -e curve shows the defense trendthat it will return to the origin and start a new round ofsurvivability evolution process As long as new flaws areadded to the system and the flaws recognition rate of at-tackers are increased in unit time the survival index curvewill always show a trend similar to Figure 8

Figure 9 shows the curve of system survivability index z1represents the probability of attacks being recognized by thesystem When the initial recognition rate is close to zero thesurvivability index of the system is about 008 and the localcognitive units begin to update the acquisition rules inde-pendently With the recognition rate increasing the systemkeeps adjusting its state and updates the results of self-feedback behavior transitions to the global cognitive leveland the survivability index gradually increases which im-proves the fact that the self-configuration mechanism in thecross-layer cognitive network further strengthens the sys-temrsquos survivability When z1 increases to 07 the

G C D SD

SH

p1

1 ndash p11

p2

p3

p4

p5

1 ndash p3 ndash p4

1 ndash p2

Figure 7 DTMC corresponding to survival situations

Table 2 Parameters and probability valuesp1W2 1minusp1W1p2 L2 1minusp2 L1p3 L5 p4 L41minusp3minusp4 L3 p5 L6-e state transition matrix P

Table 3 -e approximate steady-state probabilities

Module type Module name Approximatesteady-state probabilities

Global cognition

Monitor 035971Decide 003452Execute 046896

Monitor_1 031506Decide_1 003447

Domain cognition

Execute_1 003569Monitor_2 031958Decide_2 003452Execute_2 003689


survivability index begins to climb rapidly which shows thatimproving the systemrsquos attack recognition rate deliversbetter effects on enhancing the systemrsquos survivability ratherthan strengthening its resistance

From the DTMC corresponding to the cognitive survivalstate collections we can see that there are three possiblestates of self-recovery actions L3 L4 and L3 as assumed Andthe self-recovery rate V L3L3 + L4 + L5 in Figure 10 showsthe changes of system survivability indexes when the self-recovery rates are 0532 0758 0914 and 0997 respectivelyIt also unveils the fact that with the increase of the intervaltime of self-recoveries the survivability index curve declinessteadily When the intervals are the same the larger the valueof self-recovery rateV is the higher the survivability index ofthe system becomes When the value of V is 0997 thesurvivability index is close to the highest 10 the systemperforms the best self-recovery ability It can be seen thatimproving the system recovery is one of the most feasibleways to improve the system survivability

For survivable systems different indicators affectingcognitive performance are tested -e main parameters andtheir implications are shown in Table 5 In view of thecognitive model in this paper the relationship between theabove parameters and the cognitive ability of survivablesystem is analyzed and tested accordingly

Reliability is one of the important indicators affecting thecognitive ability of survivable systems Failure of cognitiveunits has great impacts on the cognitive performance ofsystems -e relationship between ESD and reliability isshown in Figure 11 Parameters of ESD decline along thetransverse axis and the height of the histogram decreases aswell which proves that the reliability of the system getsweakened as the interval of failure time decreases that is thehigher the failure frequency is the weaker the reliabilitybecomes When ESD is 150times ESD the reliability is still above09 while when ESD is reduced to 1100times ESD the reliabilitydrops sharply to less than 01 -at is because the number ofcognitive units that provide normal service decreases withthe increase of failure frequency the reliability of the systemis weakened dramatically thus causing significant impactson the systemrsquos cognitive ability

Recovery is an important indicator to measure the systemrsquoscognitive ability Figure 12 demonstrates the relationship be-tween ESH and recovery -e systemrsquos recovery falls with ESHgrowing which shows that the longer the recovery time is themore poor the recovery performance will be In particularwhen the ESH value is 100times ESH the systemrsquos recovery de-creases to about 02 -e survivable system cannot avoid at-tacks faults or other accidents under such complex workingenvironment If the self-recovery time is too long the durationof staying in unsafe states will be longer thus affecting thecognitive survivable systemrsquos cognitive ability

-e relationship between the rate of monitor behaviorsrsquotransitions (m represents different rates) and recognition isshown in Figure 13 From the figure we can see that everycurve climbs upwards demonstrating that the systemrsquosrecognition gets stronger as t increases At first the fourcurves rise significantly and then tend to grow steadily andslowly -at is because the time t starts to advance from 0

Table 4 Test parameters

Parameters ValuesP 06000k 1000g 04000Z2 02460Z3 00500W1 09100W2 00900L1 02700P1 00015L2 07300H1 00140S1 00100P2 01500S2 02600L6 09500S3 00152

01 001 1E ndash 3 1E ndash 4 1E ndash 5 1E ndash 6 1E ndash 7 1E ndash 8 1E ndash 9 1E ndash 1006

07

08

09

10

11

Surv

ivab

le p

aram

eter

s

h

Figure 8 -e impact of (h) on survivable parameters

00 01 02 03 04 05 06 07 08 09 1000

01

02

03

04

05

06

07

08

09

10

Surv

ivab

ility

inde

x

z1

Figure 9 z1rsquos effects on survivability indexes


meaning that the system begins to work from nonworkingstates -en the systemrsquos recognition increases rapidly from0 And when t advances to a certain value the recognitionability will also remain at a stable state When m is 10 thecurve of recognition stays at the lowest level while whenm is50 the curve is at the highest level which shows that thebigger the m value is or the faster the execution rate oftransition behaviors is the stronger the recognition of thesystem will be Because the time delay of executing

monitoring behavior decreases the number of monitoringunits in working states increases which improves the effi-ciency of perception and detection of the internal and ex-ternal environment of the system so the systemrsquos cognitiveability gets stronger

-e transition rate of monitoring behaviors namely therelationship between e and recognition is shown in Fig-ure 14We can see that every curve climbs upwards along thetransverse axis demonstrating that the systemrsquos recognitiongets stronger as t increases When the value of t is relativelysmall the four curves rise rapidly and then tend to growsteadily and slowly that is because the time t starts to ad-vance from 0 meaning that the system begins to work fromnonworking states -en the systemrsquos recognition increasesrapidly from 0 And when t advances to a certain value therecognition ability will also remain at a stable state -e fourcurves are obtained when e is 02 04 15 and 20 re-spectively When e 02 the corresponding curve is at the

00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997

Self-recovery interval

Surv

ivab

ility

inde

x

Figure 10 Recovery ratersquos effects on survivability indexes

Table 5 Parameters and implications of cognitive performanceindicators

Parameter Value

ESDExpected value of interval time between system

failuresESH Expected time for system self-recoveryM Rate of monitoring behaviorsrsquo transitionsE Rate of executing behaviorsrsquo transitions

E 12lowastE 15lowastE 110lowastE 150lowastE 1100lowastE00

02

04

06

08

10

Relia

bilit

y

E = ESD

Figure 11 ESDrsquos impacts on reliability


02

04

06

08

10

Reco

vera

bilit

y

E = ESH

Figure 12 ESHrsquos effects on recovery

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01

Figure 13 mrsquos effects on the systemrsquos recognition


lowest level and when e 20 the corresponding curve is atthe highest level which means that the bigger the value of eis the stronger the systemrsquos recognition ability becomesBecause the time delay of executing monitoring behaviordecreases the number of monitoring units in working statesincreases which improves the efficiency of perception anddetection of the internal and external environment of thesystem so the systemrsquos cognitive ability gets stronger

5 Conclusion

Cognitive model of survivable system is the abstraction ofcognitive ability of survivable system and the key to enhancethe systemrsquos cognitive ability

-is paper studies the autonomous cognitive model andanalysis method of survivable systems -e self-feedbackstructure of cognitive unit is improved and the formalmodeling of cognitive process is carried out by describing thetransitionmap of cognitive survival state In addition the paperhas obtained standardized results with the application of PEPAWorkbench model tool Next we will further improve thecognitive structure and formal model of survivable systems andconduct research on the enhanced design of survivable systemwith autonomous cognitive model

Data Availability

-e data set can be obtained free of charge from httpkddicsuciedudatabaseskddcup99kddcup99html

Conflicts of Interest

-e authors declare that they have no conflicts of interest

Acknowledgments

-is present research work was supported by the NationalNatural Science Foundation of China (Nos 61202458 and

61403109) the Natural Science Foundation of HeilongjiangProvince of China (No F2017021) the Harbin Science andTechnology Innovation Research Funds (No2016RAQXJ036)

References

[1] V R Westmark ldquoA definition for information system sur-vivabilityrdquo in Proceedings of the 37th Annual Hawaii Inter-national Conference on System Sciences pp 2086ndash2096 IEEEComputer Society Press Big Island HI USA January 2004

[2] R J Ellison R C Linger T Longstaff and N R MeadldquoSurvivable network system analysis a case studyrdquo IEEESoftware vol 16 no 4 pp 70ndash77 1999

[3] G S Zhao H Q Wang and J Wang ldquoStudy on situationevaluation for network survivability based on grey relationanalysisrdquo Mini-Micro Systems vol 27 no 10 pp 1861ndash18642006

[4] J Wang H Q Wang and G S Zhao ldquoSituation trackingassessment for network survivability based on sequentialMonte Carlordquo Journal of Harbin Institute of Technologyvol 40 no 5 pp 802ndash806 2008

[5] J Wang H Q Wang and G S Zhao ldquoA situation assessmentmethod for network survivabilityrdquoWuhan University Journalof Natural Sciences vol 11 no 6 pp 1785ndash1788 2006

[6] J Wang H-Q Wang and G-S Zhao ldquoFormal modeling andquantitative evaluation for information system survivabilitybased on PEPArdquo Ee Journal of China Universities of Postsand Telecommunications vol 15 no 2 pp 88ndash113 2008

[7] J Wang H Q Wang and G S Zhao ldquoAutomated analysisand validation for survivability of distributed mission-criticalsystemsrdquo Chinese High Technology Letters vol 19 no 6pp 572ndash579 2009

[8] G S Zhao H QWang and JWang ldquoA novel formal analysismethod of network survivability based on stochastic processalgebrardquo Tsinghua Science amp Technology vol 12 no 1pp 175ndash179 2007

[9] J A Zinky D E Bakken and R E Schantz ldquoArchitecturalsupport for quality of service for CORBA objectsrdquoEeory andPractice of Object Systems vol 3 no 1 pp 55ndash73 1997

[10] G S Zhao J Wang and Z X Li ldquoA method of autonomousemergency rejuvenation for survivable systemrdquo Mini-MicroSystems vol 35 no 10 pp 2284ndash2289 2014

[11] Z H Yu S M Lin and H Q Chen Network Security-Re-search on Survivability and Network Modeling Beijing China2012

[12] Report on Software Survivability[EBOL] httpwwwdoc88comp-147660727345html Beijing 2012

[13] G S Zhao W D Wang and W Zhang ldquoResearch of cloudsurvivabilityrdquo Telecommunications Science vol 9 pp 52ndash592011

[14] G S Zhao H L Liu and J Wang ldquoStudy on the autonomousrecognition mechanism for survivable systemsrdquo Chinese HighTechnology Letters vol 24 no 10 pp 999ndash1006 2014

[15] J Wang and G S Zhao ldquoCognitive model and quantitativeanalysis for survivable system based on SM-PEPArdquo Journal ofHuazhong University of Science and Technology (Nature Sci-ence Edition) vol 43 no 5 pp 99ndash103 2015

[16] D S Modha R Ananthanarayanan S K Esser A NdirangoA J Sherbondy and R Singh ldquoCognitive computingrdquoCommunications of the ACM vol 54 no 8 pp 62ndash71 2011

[17] J T Bradley ldquoSemi-Markov PEPA modelling with generallydistributed actionsrdquo International Journal of Simulationvol 6 no 3-4 pp 43ndash51 2005

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02

Figure 14 ersquos effects on the systemrsquos recognition


[18] H-Q Wang H-W Lu Q Zhao X-K Dong and G-S FengldquoModel and quantification of autonomic dependability ofmission-critical systemsrdquo Journal of Software vol 21 no 2pp 344ndash358 2010

[19] R -omas D Friend L Dasilva and A Mackenzie ldquoCog-nitive networks adaptation and learning to achieve end-to-end performance objectivesrdquo IEEE Communications Maga-zine vol 44 no 12 pp 51ndash57 2006

[20] P Balamuralidhar and R Prasad ldquoA context driven archi-tecture for cognitive radio nodesrdquo Wireless Personal Com-munications vol 45 no 3 pp 423ndash434 2008

[21] C Fortuna and M Mohorcic ldquoTrends in the development ofcommunication networks cognitive networksrdquo ComputerNetworks vol 53 no 9 pp 1354ndash1376 2009

[22] Laboratory For Foundations of Computer Science Versionv25 of the PEPA Eclipse Plugin[EBOL]httpwwwdcsedacukpepadownloads

[23] J D Nicholas Parallel Computation of Response Time Den-sities and Quantiles in Large Markov and Semi-markovmodels p 10 Imperial College London UK 2004


stochastic process algebra [17 18] which provides theo-retical guidance for the study of survivable systemrsquos cog-nitive ability

2 Autonomous Cognitive Model

-e systemrsquos cognitive needs are mapped to the dynamicselection of multiobjective cognitive results at multiplecognitive levels Meanwhile the cross-layer perception isused to obtain the autonomous reasoning dynamic deci-sion-making and resource reallocation of survivable sys-tems and to realize the self-adaptation to dynamic changesof the cognitive needs and environment security In addi-tion cognitive model should reach a balance between formaldescription and cognitive abstraction so it can not onlyaccurately describe and reflect the systemrsquos recognition butalso facilitate reasoning thus providing theoretical supportfor the study of cognitive ability of survivable system

21 Cross-Layer Recognition According to different em-phasis on cognitive process cognitive needs and cognitiveelements the survivable system can be divided into threecognitive layers namely access cognitive layer networkcognitive layer and service cognitive layer as shown inFigure 1

Access cognition layer reflects the recognition of thecommunication capability of available transmission chan-nels which supports protocol conversion and adaptation ofvarious available channels and achieves high reliable in-formation transmission through the recognition of channelsrsquocommunication capability

-e network cognitive layer shows the unified cognitionof cognitive specifications in the cognitive process and goalsof cognitive needs It can realize dynamic reconfigurationand planning constraints of cognitive network resources

-e service cognitive layer reflects the recognition of thematching ability of providing Internet resources required forapplications and users It can serve high QoS service incomplex environment where massive incomplete or evenmalicious service scenarios exist

22 Self-Feedback Mode of Cognitive Units -e cognitiveunit structure is similar to Agent in the traditional sense thebasic unit of the realization of cognitive model [19ndash21]which is also the symbol of the autonomous cognitive abilityof survivable system With the self-feedback ability added onthe basis of the existing cognitive unit structure an im-proved cognitive loop structure is achieved as shown inFigure 2 -is structure is a self-feedback evolving structuredriven by the self-configuring strategy library of cognitiveelements (M-D-E Monitor-Decide-Execute) which canadjust behaviors topology and service parameters with thechanges of working environment and task objectives insideand outside the survivable system Apart from the functionof perceiving contexts of normal network events thestructure can also deal with internal and external securitythreats to enable survivable systems to independently adaptto environment and demand changes

-e self-feedback mechanism of cognitive unit is shown inFigure 3 which includes local domain-level and global feed-backs Each layer is composed of several cognitive units toachieve global domain and local cognition of the systemrsquoscognitive behaviors Results of local feedback can obtain the localoptimal solution to the goal of cognitive needs global feedbackcan coordinate the feedback results at domain and local levelsand obtain the global optimal or suboptimal solution

Cognitive units can obtain self-configuration of cognitiveelements with a self-feedback mechanism -ere are two cases

221 Preset Self-Configuration When matchable strategiesare found in existing strategy libraries the configurationstrategy in the preset cognitive rule set will analyze andreason the system as shown in Figure 4

222 Acquired Self-Configuration When matchable strat-egies cannot be found in strategy library effective rulesachieved after acquisition will be stored as acquired rules inthe configuration strategy library as shown in Figure 5

3 Cognitive Process of Formal Modeling

In order to formally describe the transition between differentstates of the system under attacks faults or accidentalfailures and to better understand the dynamic evolutionprocess of the survivable systemrsquos survival situations acognitive survival state transition diagram [14] is intro-duced as shown in Figure 6

-e tool Version v25 of the PEPA Eclipse Plugin [22] ofthe Computer Science Foundation Laboratory of the Uni-versity of Edinburgh is used to simplify the calculationprocess -e formal description of the cognitive survivalstate for the survivable system in Figure 6 is as follows

(i) Intruder (searching h) Attack(ii) Attack (starck_attack p) (attack k) Attack +

(starck_attack g) Intruder(iii) General (attack z1) Compromised + (failing

z2) Compromised + (error z3) Compromised(iv) Compromised (probe w1) Detection + (mask

w2) General(v) Detection (start_probe L1) (emergency1 p1)

SelfDestruction + (start_probe L2) (healing L3)SelfHealing

(vi) SelfHealing (start_healing L3) (sealheaking s1)General + (start_healing L4) (emergency2 p2)SealDectruction + (strat_healing L5) (selfhealings2) SelfHealing

(vii) SelfDestruction (start_destroy L6) (destroydelta L (s)) (backup s3) General

-e parameters and their meanings are shown in Table 1-e cognitive model of survivable systems can be for-

malized as a quintuple form (Mde Objects Domain) whereMdeMde1 Mde2 Mdem represents the resource con-straint sets of m cognitive units CC (Mdei) represents the











Userkj ||service

k Serverkji

M E


Resources


Cognitive processes

Self-configuration


Application

DEM

Users

154

362Access


D

M E

D

M E

D

M E

D

E

D

M E

D

M E

D

M

Demand analysis

Need goals

Survivablesystem




Outside


EM

D





Strategy library

Cognitive elements

Local decision

Effector


Sensor

Strategy library

Cognitive elements

Local decision

Effector


Sensor


Domain decision


Monitoring module

Global decision

Domain decision

Strategy library

SensorEffector

Monitoring module

Sensor

Perform module

Effector

Sensor

Globaldecision

Domainfeedback

Localfeedback



Monitoring

Performation

Decision

Preinstall

Acquisition


Sensor

Effector



Monitoring

Performation

Decision

Preinstall

Acquisition


Sensor

Effector










⟶(anrn)



G C D SD

SH

Under threat



reat blocked

Critical situation


Recovery renewal



SD SelfDestruction







(1)



πi viE Ti1113858 1113859

1113936 jvjE Tj1113960 1113961 i j isin XS (2)


pij limt⟶+infin

Qij(t) (3)



vrarr

vrarr

P

1113944i

vi 1

⎧⎪⎨

⎪⎩(4)

vrarr



πQ 0

1113944n

i1πi 1

⎧⎪⎪⎨

⎪⎪⎩(5)





000000000

00000010

p5p4p3VGp2p2

p1p1

SDSHDCG

SDSHDCG

P = (6)





VG 1 minus p3


VC 1 minus p3


VD 1 minus p1( 1113857 1 minus p3( 1113857


VSH p2 1 minus p1( 1113857




(7)



tG 1λG

tC 1λC

tD 1λD

tSH 1λSH

tSD 1λ1

+1λ2

(8)


πi viti

1113944jvjtj









G C D SD

SH

p1

1 ndash p11

p2

p3

p4

p5

1 ndash p3 ndash p4

1 ndash p2





Global cognition



Domain cognition












07

08

09

10

11

Surv

ivab

le p

aram

eter

s

h


00 01 02 03 04 05 06 07 08 09 1000

01

02

03

04

05

06

07

08

09

10

Surv

ivab

ility

inde

x

z1






00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997


Surv

ivab

ility

inde

x



Parameter Value




02

04

06

08

10

Relia

bilit

y

E = ESD



02

04

06

08

10

Reco

vera

bilit

y

E = ESH


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01




5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02



















Userkj ||service

k Serverkji

M E


Resources


Cognitive processes

Self-configuration


Application

DEM

Users

154

362Access


D

M E

D

M E

D

M E

D

E

D

M E

D

M E

D

M

Demand analysis

Need goals

Survivablesystem




Outside


EM

D





Strategy library

Cognitive elements

Local decision

Effector


Sensor

Strategy library

Cognitive elements

Local decision

Effector


Sensor


Domain decision


Monitoring module

Global decision

Domain decision

Strategy library

SensorEffector

Monitoring module

Sensor

Perform module

Effector

Sensor

Globaldecision

Domainfeedback

Localfeedback



Monitoring

Performation

Decision

Preinstall

Acquisition


Sensor

Effector



Monitoring

Performation

Decision

Preinstall

Acquisition


Sensor

Effector










⟶(anrn)



G C D SD

SH

Under threat



reat blocked

Critical situation


Recovery renewal



SD SelfDestruction







(1)



πi viE Ti1113858 1113859

1113936 jvjE Tj1113960 1113961 i j isin XS (2)


pij limt⟶+infin

Qij(t) (3)



vrarr

vrarr

P

1113944i

vi 1

⎧⎪⎨

⎪⎩(4)

vrarr



πQ 0

1113944n

i1πi 1

⎧⎪⎪⎨

⎪⎪⎩(5)





000000000

00000010

p5p4p3VGp2p2

p1p1

SDSHDCG

SDSHDCG

P = (6)





VG 1 minus p3


VC 1 minus p3


VD 1 minus p1( 1113857 1 minus p3( 1113857


VSH p2 1 minus p1( 1113857




(7)



tG 1λG

tC 1λC

tD 1λD

tSH 1λSH

tSD 1λ1

+1λ2

(8)


πi viti

1113944jvjtj









G C D SD

SH

p1

1 ndash p11

p2

p3

p4

p5

1 ndash p3 ndash p4

1 ndash p2





Global cognition



Domain cognition












07

08

09

10

11

Surv

ivab

le p

aram

eter

s

h


00 01 02 03 04 05 06 07 08 09 1000

01

02

03

04

05

06

07

08

09

10

Surv

ivab

ility

inde

x

z1






00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997


Surv

ivab

ility

inde

x



Parameter Value




02

04

06

08

10

Relia

bilit

y

E = ESD



02

04

06

08

10

Reco

vera

bilit

y

E = ESH


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01




5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02










Strategy library

Cognitive elements

Local decision

Effector


Sensor

Strategy library

Cognitive elements

Local decision

Effector


Sensor


Domain decision


Monitoring module

Global decision

Domain decision

Strategy library

SensorEffector

Monitoring module

Sensor

Perform module

Effector

Sensor

Globaldecision

Domainfeedback

Localfeedback



Monitoring

Performation

Decision

Preinstall

Acquisition


Sensor

Effector



Monitoring

Performation

Decision

Preinstall

Acquisition


Sensor

Effector










⟶(anrn)



G C D SD

SH

Under threat



reat blocked

Critical situation


Recovery renewal



SD SelfDestruction







(1)



πi viE Ti1113858 1113859

1113936 jvjE Tj1113960 1113961 i j isin XS (2)


pij limt⟶+infin

Qij(t) (3)



vrarr

vrarr

P

1113944i

vi 1

⎧⎪⎨

⎪⎩(4)

vrarr



πQ 0

1113944n

i1πi 1

⎧⎪⎪⎨

⎪⎪⎩(5)





000000000

00000010

p5p4p3VGp2p2

p1p1

SDSHDCG

SDSHDCG

P = (6)





VG 1 minus p3


VC 1 minus p3


VD 1 minus p1( 1113857 1 minus p3( 1113857


VSH p2 1 minus p1( 1113857




(7)



tG 1λG

tC 1λC

tD 1λD

tSH 1λSH

tSD 1λ1

+1λ2

(8)


πi viti

1113944jvjtj









G C D SD

SH

p1

1 ndash p11

p2

p3

p4

p5

1 ndash p3 ndash p4

1 ndash p2





Global cognition



Domain cognition












07

08

09

10

11

Surv

ivab

le p

aram

eter

s

h


00 01 02 03 04 05 06 07 08 09 1000

01

02

03

04

05

06

07

08

09

10

Surv

ivab

ility

inde

x

z1






00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997


Surv

ivab

ility

inde

x



Parameter Value




02

04

06

08

10

Relia

bilit

y

E = ESD



02

04

06

08

10

Reco

vera

bilit

y

E = ESH


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01




5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02

















⟶(anrn)



G C D SD

SH

Under threat



reat blocked

Critical situation


Recovery renewal



SD SelfDestruction







(1)



πi viE Ti1113858 1113859

1113936 jvjE Tj1113960 1113961 i j isin XS (2)


pij limt⟶+infin

Qij(t) (3)



vrarr

vrarr

P

1113944i

vi 1

⎧⎪⎨

⎪⎩(4)

vrarr



πQ 0

1113944n

i1πi 1

⎧⎪⎪⎨

⎪⎪⎩(5)





000000000

00000010

p5p4p3VGp2p2

p1p1

SDSHDCG

SDSHDCG

P = (6)





VG 1 minus p3


VC 1 minus p3


VD 1 minus p1( 1113857 1 minus p3( 1113857


VSH p2 1 minus p1( 1113857




(7)



tG 1λG

tC 1λC

tD 1λD

tSH 1λSH

tSD 1λ1

+1λ2

(8)


πi viti

1113944jvjtj









G C D SD

SH

p1

1 ndash p11

p2

p3

p4

p5

1 ndash p3 ndash p4

1 ndash p2





Global cognition



Domain cognition












07

08

09

10

11

Surv

ivab

le p

aram

eter

s

h


00 01 02 03 04 05 06 07 08 09 1000

01

02

03

04

05

06

07

08

09

10

Surv

ivab

ility

inde

x

z1






00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997


Surv

ivab

ility

inde

x



Parameter Value




02

04

06

08

10

Relia

bilit

y

E = ESD



02

04

06

08

10

Reco

vera

bilit

y

E = ESH


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01




5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02












(1)



πi viE Ti1113858 1113859

1113936 jvjE Tj1113960 1113961 i j isin XS (2)


pij limt⟶+infin

Qij(t) (3)



vrarr

vrarr

P

1113944i

vi 1

⎧⎪⎨

⎪⎩(4)

vrarr



πQ 0

1113944n

i1πi 1

⎧⎪⎪⎨

⎪⎪⎩(5)





000000000

00000010

p5p4p3VGp2p2

p1p1

SDSHDCG

SDSHDCG

P = (6)





VG 1 minus p3


VC 1 minus p3


VD 1 minus p1( 1113857 1 minus p3( 1113857


VSH p2 1 minus p1( 1113857




(7)



tG 1λG

tC 1λC

tD 1λD

tSH 1λSH

tSD 1λ1

+1λ2

(8)


πi viti

1113944jvjtj









G C D SD

SH

p1

1 ndash p11

p2

p3

p4

p5

1 ndash p3 ndash p4

1 ndash p2





Global cognition



Domain cognition












07

08

09

10

11

Surv

ivab

le p

aram

eter

s

h


00 01 02 03 04 05 06 07 08 09 1000

01

02

03

04

05

06

07

08

09

10

Surv

ivab

ility

inde

x

z1






00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997


Surv

ivab

ility

inde

x



Parameter Value




02

04

06

08

10

Relia

bilit

y

E = ESD



02

04

06

08

10

Reco

vera

bilit

y

E = ESH


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01




5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02










tG 1λG

tC 1λC

tD 1λD

tSH 1λSH

tSD 1λ1

+1λ2

(8)


πi viti

1113944jvjtj









G C D SD

SH

p1

1 ndash p11

p2

p3

p4

p5

1 ndash p3 ndash p4

1 ndash p2





Global cognition



Domain cognition












07

08

09

10

11

Surv

ivab

le p

aram

eter

s

h


00 01 02 03 04 05 06 07 08 09 1000

01

02

03

04

05

06

07

08

09

10

Surv

ivab

ility

inde

x

z1






00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997


Surv

ivab

ility

inde

x



Parameter Value




02

04

06

08

10

Relia

bilit

y

E = ESD



02

04

06

08

10

Reco

vera

bilit

y

E = ESH


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01




5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02



















07

08

09

10

11

Surv

ivab

le p

aram

eter

s

h


00 01 02 03 04 05 06 07 08 09 1000

01

02

03

04

05

06

07

08

09

10

Surv

ivab

ility

inde

x

z1






00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997


Surv

ivab

ility

inde

x



Parameter Value




02

04

06

08

10

Relia

bilit

y

E = ESD



02

04

06

08

10

Reco

vera

bilit

y

E = ESH


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01




5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02













00 02 04 06 08 10 12 14 16 18 200970

0975

0980

0985

0990

0995

1000

v3 = 0532v3 = 0758

v3 = 0914v3 = 0997


Surv

ivab

ility

inde

x



Parameter Value




02

04

06

08

10

Relia

bilit

y

E = ESD



02

04

06

08

10

Reco

vera

bilit

y

E = ESH


0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10

Reco

gniz

abili

ty

t

m = 50m = 40

m = 20m = 01




5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02











5 Conclusion



Data Availability




Acknowledgments



References


















0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 3000

02

04

06

08

10Re

cogn

izab

ility

t

e = 20e = 15

e = 04e = 02

















Documents

ResearchArticle …downloads.hindawi.com/journals/mpe/2020/3618284.pdf · 2020. 8. 27. · ResearchArticle AutonomousCognitiveModelandAnalysisforSurvivableSystem YiweiLiao,1 GuoshengZhao