Evaluation of Attack Effect in Ad Hoc Networks Based on Variable
Weight TOPSIS Method
Linxi Guo
1
, Bin Wu
1
1
School of Cyberspace security, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Beijing,
China
Keywords: Evaluation of attack effect, Variable weight theory, TOPSIS, Ad Hoc network security.
Abstract: Evaluation of Attack Effect in Ad Hoc networks is one of the key technologies for Ad Hoc network security
applications. In order to solve the traditional attack effect evaluation with the constant weighted summation
can’t adjust the relevant weights in time to the change of the attack situation, which causes the limitation and
one-sidedness of the judgment. This paper proposes an attack effect evaluation model based on variable
weight theory. First, comprehensively considering the relevance of the attack's own complexity and the attack
effect, establish an attack effect evaluation indicator system. Then, construct a state variable weight vector
expression, so that the weights are adjusted accordingly with the change of the situation. Finally, combined
with TOPSIS method, the attack effect evaluation model based on variable weight TOPSIS is constructed.
The experimental simulations show that the evaluation results obtained by the model are scientific and
reasonable, which makes up for the deviation caused by the decision of the constant weight, and provides a
theory and method for the evaluation of the attack effect in Ad Hoc network.
1 INTRODUCTION
Compared with traditional wireless networks, Ad Hoc
networks do not need infrastructure construction, and
have a high coverage and high dynamic self-
organizing network mechanism, which supports
various devices to access and exit the network at any
time, thus more in line with the actual mobile device
networking. In addition, because of their robustness
and low cost, Ad Hoc networks have broad
application prospects in many fields such as
intelligent transportation, disaster relief and military
communications. However, the Ad Hoc network is a
typical dynamic network with a flexible topology,
and there is no unified security control center.
Therefore, Ad Hoc networks are more vulnerable to
various types of attacks such as eavesdropping,
impersonation, tampering, etc., which may lead to
greater security threats (Aarti, D.S. 2013).
A lot of researches have been done on Ad Hoc
network attacks, but the number of studies on the
evaluation of Ad Hoc network attacks is very limited.
The evaluation of the effect of network attacks is an
important part of network security. The evaluation of
the network attack effect is an important part of
network security. The evaluation results can not only
test the effect and assessability of the specified attack
(operation plan) scheme, but also measure the
security of the network through simulated attacks,
thereby improving the security protection capability
of the network.
The research on the evaluation of network attack
effect firstly determines indicator weights according
to performance indicators of the target network. Then,
according to the membership function of the attack
effect, the comprehensive evaluation value is
obtained by the linear weighted comprehensive
method based on constant weights. Zeng, C. X.et
al.(2016) applied fuzzy mathematics theory to
analytic hierarchy process and established an
evaluation model based on FAHP, thus avoiding the
calculation of complex problems; Yuan, Z. and
Jianguo, H. (2014) proposed an attack effect
evaluation method based on network entropy, which
relieved the subjectivity of the evaluation to some
extent; Jajodia, S. et al.(2005) used gray theory into
evaluate calculations so that the evaluation results
implied the correlation between the evaluation
indicators to some extent. In the study of the DoS
attack effect evaluation of mobile Ad Hoc networks,
the idea of variable power was introduced for this
problem, but it was targeted for each specific attack
Guo, L. and Wu, B.
Evaluation of Attack Effect in Ad Hoc Networks Based on Variable Weight TOPSIS Method.
DOI: 10.5220/0008098602130221
In Proceedings of the International Conference on Advances in Computer Technology, Information Science and Communications (CTISC 2019), pages 213-221
ISBN: 978-989-758-357-5
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
213
(Chen, J. and Ma, T. 2012). The weight determination
method, which undoubtedly increases the complexity
of the evaluation, and the qualitative evaluation
results obtained by the gray fuzzy evaluation model
cannot measure the advantages and disadvantages of
different attacks in the same category. In order to
make the evaluation results more accurate and
reasonable, the attack effect evaluation models are
improved, and new evaluation models are
continuously proposed, but there are still some
problems, and the number of evaluation models that
can be applied to Ad Hoc networks is extremely
limited.
The commonly used constant weights vectors
reflect the overall goodness of the attack effect
evaluation to a certain extent, and the weight
coefficient corresponding to each evaluation
indicator reflects the importance of this indicator.
Therefore, the constant weights vector will play a
good role in most cases. However, regardless of the
value of the evaluation indicator attribute, the weights
vector remains unchanged, so the constant weights
vector cannot objectively reflect the change of the
state value of each attribute and the influence of the
attribute relevance on the weights. There are many
unreasonable phenomena in using the same weights
vector in different attack scenarios, mainly in the
following two types:
1) If the value of the indicator reaches a critical
value, it will have a greater impact on the evaluation
of the attack effect. For example, when the node
corruption reaches a critical value, it will have a great
impact on the reliability indicator of the node. The
network reliability will be poor, and the
corresponding security performance will be worse,
especially when the destroyed node is a critical node.
At the same time, when obtaining the attribute values
of the attack effect evaluation, there may be cases
where the individual indicator values are too low or
zero. Assume that there are two evaluation indicators
in the evaluation of Ad Hoc network attacks effect,
namely network performance and security
performance, and these two indicators are equally
important, that is, the weight
. Then the comprehensive evaluation result
is 
 
. From the evaluation results,
the results of the attack effect obtained by the state
vector  and the state vector
 are the same. However, the actual situation
is that the network performance of the target network
with the state vector  is already in a
state of paralysis, and the network availability is
significantly reduced. And the network and security
performance of the target network with state vector
 are still within acceptable limits. The
reason why the evaluation result is inconsistent with
the actual situation is that the constant weight vector
is independent of the value of each indicator, and it
does not affect the influence of the indicator values
on the comprehensive evaluation result.
2) When evaluating specific types of attacks, each
type of attacks focuses on different network security
performance metrics. For example, DoS attacks more
affect the network performance of the target network,
thereby destroying its reliability and availability.
While obtaining information-based attacks more
affect the security performance of the target network,
thereby undermining its confidentiality. Therefore,
different types of attack effects are not comparable.
In addition to the irrational problems caused by
constant weighted summation, the current attack
effect evaluation models are more subjective and
focus on the attack results more than the process,
ignoring the correlation between the complexity of
the attack behavior and the effect of the attacks.
In order to solve the above problems, this paper
innovatively proposes an attack effect evaluation
model based on variable weight TOPSIS. The
innovations of this paper mainly include the
following points:
This paper comprehensively considers the
impact of attack complexity and proposes an
attack effect evaluation indicator system
suitable for Ad Hoc networks
This paper combines the variable weight theory
based on punishment and incentive mechanism
with the TOPSIS evaluation method, and
proposes a state variable weight vector
expression suitable for Ad Hoc network attack
evaluation. The calculation formula
appropriately adjusts the weight according to
the attribute value of the attack effect indicator.
Specifically, a penalty is imposed on the
indicator weight of the attribute value that is
low. While incentives are given to indicator
weights with high attribute values. Therefore,
this model solves the unreasonable problems
brought about by the evaluation of constant
weights.
Finally, we use the specific attack test in the
simulation experiment platform and obtain the real
and objective indicator data to verify the rationality
and effect of the proposed model.
The rest of the paper is organized as follows.
Section 2 proposes a standardized quantization
method for indicators and establishes an evaluation
system for attack effect. Section 3 describes in detail
the method of determining the variable weight vector.
CTISC 2019 - International Conference on Advances in Computer Technology, Information Science and Communications
214
On the basis of using the analytic hierarchy process
to determine the weight of the indicator constant, the
construction and application of the state variable
weight vector applicable to the evaluation are mainly
studied. Then, in Section 4, the variable weight theory
is combined with the TOPSIS method to describe the
specific evaluation process of the variable weight
TOPSIS model. Afterwards in Section 5, the
rationality and effect of the proposed model are
proved by experimental simulation. Finally, Section
6 contains our conclusions.
2 ESTABLISH AN EVALUATION
INDICATOR SYSTEM
The establishment of the evaluation model for Ad
Hoc network attack effects can be divided into the
following three steps: establishing an evaluation
indicator system for attack effects, determining the
weight value of the evaluation indicators, and using
the comprehensive evaluation algorithm to calculate
the evaluation results.
The evaluation indicator system is the
infrastructure of the entire assessment process.
Therefore, it is a basis for Mobile Ad Hoc Network
Attack Effect of effective evaluation to establish a
reasonable evaluation indicator system, which is an
important basis to reflect the effect of the attack.
This paper proposes an evaluation indicator
system of attack effect for Ad Hoc network, based on
the correlation between attack complexity and attack
effect, and gives a standardized quantification method
of the indicators.
2.1 Ad Hoc Network Attack Effect
Evaluation Indicator System
The basic idea of establishing the evaluation indicator
system of attack effect for Ad Hoc networks is as
follows: Firstly, according to the security
vulnerabilities of Ad Hoc networks and the impact of
common attacks, the basic evaluation indicators are
selected, and the three-level indicator system of
target-criteria-indicators” is established (Lai C et al.
2015).
Considering the correlation between the complexity
of the attack and the effect of the attack, this paper
establishes an attack effect evaluation indicator
system for Ad Hoc networks based on attributes of
attack process and performance factors of attack
results, as shown in Figure 1.
Figure 1: Ad Hoc network attack effect evaluation
indicators system.
2.2 Standardized Quantification of
Evaluation Indicators
2.2.1 Standardized Quantification of
Qualitative Indicators
In order to quantitatively describe the effects of
different types of attacks, qualitative indicators such
as cost of attack, attack target level and get
permission level need to be assigned from high to low,
and the data sources can be obtained by experts. The
specific scoring criteria are as follows:
Cost of attack: It mainly refers to the technical
requirements and the amount of equipment
resources consumed to implement the attack.
For this qualitative indicator, the possible states
of the indicator can be listed and the reference
segment value is assigned according to the
degree of importance, as shown in Table 1.
Table 1: Quantitative reference value of Cost of
attack.
Indicator state
Reference score
Number of malicious nodes
0~3
Resource and equipment
consumption
0~3
Human resources
0~2
Financial consumption
0~4
Other
0~4
Then normalize, that is, the ratio of the initial
attribute value to the reference total score value.
Attack target level: It mainly refers to the
importance of the network. It is a qualitative
Evaluation of Attack Effect in Ad Hoc Networks Based on Variable Weight TOPSIS Method
215
indicator with order, which can be quantified
according to the information, as shown in Table
2.
Table 2: Quantitative value of Attack target level.
Indicator state
Quantitative value
Single network
0.3
Partial network
0.5
Entire network
0.8
Get permission level: It refers to the level of
permission obtained through an exploit method
during the attack process and quantifying it
according to the degree of importance, as
shown in Table 3.
Table 3: Quantitative value of Get permission level.
Indicator state
Quantitative value
Single network
0.3
Partial network
0.5
Entire network
0.8
2.2.2 Standardized Quantification of
Quantitative Indicators
Different indicators have different dimensions,
ranges of variation and confrontational problems.
Therefore, they cannot be directly used for attack
effect evaluation. It is necessary to dimensionless and
normalize the original data of the indicator. In this
paper, extreme value processing method will be used
to standardize the results of dimensionless processing
as [0,1]. Considering the problem of different
confrontation among indicators, the indicators are
divided into two types: benefit-oriented indicators
and cost-oriented indicators, which are standardized
and quantified separately:
Benefit-oriented indicators: The greater the
attribute value, the better the attack effect. For
this type of indicator attribute value

, the
pre-treatment formula is:



 



(1)
Cost-oriented indicators: The smaller the
attribute value, the better the attack effect. For
this type of indicator attribute value

, the
pre-treatment formula is:


 


   

(2)
Benefit-oriented indicators include attack target
level, getting permission level, getting data volume,
average end-to-end delay, network packet drop rate,
normalized routing overhead, data loss rate, the
amount of data tampering, degree of node destruction
and unit time power consumption. Cost-oriented
indicators include cost of attack, attack time,
bandwidth utilization and throughput scaling.
3 VARIABLE WEIGHT THEORY
TO DETERMINE INDICATORS
WEIGHT
This paper focuses on the application of variable
weight theory in the evaluation of attack effects,
proposes a state variable weight vector expression
suitable for the model and determines the value of the
parameter, which is on the basis of determining the
constant weight of indicators by AHP method. The
state variable weight vector is the key of variable
weight theory in practical application. And It is one
of the most important innovation of this paper.
3.1 Determination of Constant Weight
of Indicators Based on AHP
Analytic Hierarchy Process (AHP) is a method for
determining the weight of indicators combined with
qualitative analysis and quantitative analysis. It can
quantify multiple uncertainties and fuzziness in the
decision process. AHP requires that the problem to be
solved be decomposed into several parts, each Parts
are divided into different hierarchical structures.
Compare each indicator at the same level and
determine the weight of the indicator based on the
importance of the indicator (Sun Z et al. 2012).
3.1.1 Constructing Judgment Matrix
According to the expert opinion, the pairwise
comparison factors are quantified using the 19 ratio
scale comparison table with reference to expert
opinions, as shown in Table 4.
Table 4: 19 ratio scale comparison table.
Meaning
ai is as important as a j
ai is a little bit important than a j
ai is obvious important than a j
ai is consuming important than a j
ai is extreme important than a j
Remarks: Take 2, 4, 6, 8 between adjacent judgment
values
According to Table 4 and the comparison of the
advantages and disadvantages of each evaluation
indicator, the following judgment matrix can be
constructed:
CTISC 2019 - International Conference on Advances in Computer Technology, Information Science and Communications
216










(3)
Where:

 
is the ratio
of the i-th factor to the importance of the j-
th factor.

and


.
3.1.2 Calculate Indicator Weights and
Consistency Check
For all pairs of comparison matrices, consistency
check is required. The purpose of consistency check
is to avoid the self-contradictory phenomenon of
subjective judgment.
Define 


. If CR<0.1, the judgment matrix
is considered to satisfy the consistency, where 



;

is the maximum eigenvalue of the
judgment matrix A; is the order; RI is the average
random consistency indicator. Table 5 gives the
corresponding RI values of matrix 1-14.
Table 5: The value of the random consistency indicator
RI(n).
n
1
2
3
4
5
6
7
RI
0
0
0.52
0.89
1.12
1.26
1.36
n
8
9
10
11
12
13
14
RI
1.41
1.46
1.49
1.52
1.54
1.56
1.58
When the judgment matrix satisfies the complete
consistency, the eigenvector corresponding to the
eigenvalue

is the constant coefficient of the
indicator 
 
.
Since the judgment matrix is constructed by the
subjective judgment of the expert, if it does not meet
the consistency, the data need to be adjusted.
3.2 Determination Indicators Weight
by Variable Weight Theory
The introduction of variable weight theory can solve
the problem that the weights of indicators in constant
weight assessment cannot be changed according to
the change of the attack situation, leading to the
decision bias. Therefore, how to apply variable
weight theory to the field of attack effect evaluation,
which can make the change of weight better reflect
the attack situation, is the key of this paper.
3.2.1 Variable Weight Theory
Variable weights are relative to constant weights. The
concept of variable weight vector and state variable
weight vector were first proposed by Wang, P.X.
(1985). It was emphasized that the weight of indicator
should change with the change of attribute value of
indicator in order to overcome the deviation caused
by constant weight decision-making. Li, H. X. (1995)
further gave the axiomatic definition of variable
weight and state variable weight vector.
Let 
 
be the state variable and
  be the variable
weight of the relative constant weight
. According
to the variable weight theory, the variable weight
vector satisfies the following axiom (Deqing, L.
2002):
1) Normalized condition:


2) Continuity: The variable weight vector
is continuous with respect to each
independent variable
;
3) Monotonicity: The variable weight vector
is monotonically decreasing
(punitive variable weight) or increasing (incentive
variable weight) with respect to the independent
variable
.
The variable weight vector x mainly relies on the
construction of state variable weight vector s.
According to the configuration level of attributes of
attack effect evaluation indicator, the weight values
of each indicator are adjusted. In addition to
satisfying continuity and monotonicity, the state-
varying weight vector also satisfies the Hadamard
product:
 


(4)
According to the above definition, it can be seen
that the purpose of the punitive state variable weight
vector is to punish the low-level indicator attribute to
ensure the balance of the evaluation indicator by
increasing the weight of the indicator with the
decrease of the state value; The purpose of the
incentive state variable weight vector is to stimulate
the high-level indicator attribute by increasing the
indicator weight with the increase of the state value.
3.2.2 Constructing State Variable Weight
Vector
The key to the application of variable weight theory
to the actual variable weight problem lies in the
construction and selection of the state variable weight
vector
. Therefore, the characteristics of the
existing various types of state variable weight vectors
and the requirements of equilibrium for the decision-
making problem should be fully considered in
practical applications. Next, after analysing the
Evaluation of Attack Effect in Ad Hoc Networks Based on Variable Weight TOPSIS Method
217
evaluation process of attack effect for Ad Hoc
networks, it is proposed that in order to meet the
variable weight requirements proposed in this paper,
the constructed state variable vector should satisfy the
following characteristics:
1) The indicator value is too large or too small, and
the weight is increased. If a certain indicator is too
high in the evaluation process, which means that a
certain performance indicator of the target network is
too low or the attack cost is very small, it will affect
the rise of the overall attack effect level regardless of
the size of the constant weight. Therefore, the weight
of the indicator needs to be increased. Similarly, if a
certain attribute value is very low, which means that
a certain performance of the target network is not
affected by the attack or the attack cost is too high, it
will also affect the overall evaluation level of attack
effect to a certain extent even if the constant weight
of this indicator is very small. So, the weight of the
indicator also needs to be increased.
2) Incentive range is greater than punishment range.
Due to the complexity of Ad Hoc networks, the
relationship between the proposed attack effect
evaluation indicators is relatively large and inevitably
there are redundant indicators. By analyzing the value
of single indicator separately, it is found that when the
value of single indicator is high, such as the average
end-to-end delay is too high, the overall attack effect
is significantly improved. However, when the value
of single indicator is low, the overall attack effect is
not significantly reduced. Therefore, considering the
balance of the evaluation indicator system, the state
variable weight vector in the evaluation model of the
attack effect of Ad Hoc network should satisfy the
requirement that the incentive range is greater than
the penalty range.
3) The punishment and incentive of the indicator
with relatively large constant weight are also
relatively large. The constant weight reflects the
relative importance of each indicator attribute to a
certain extent. The evaluation result of the attack
effect is more dependent on the indicator with
relatively large weight. Therefore, the state variable
weight vector should be able to punish and motivate
the indicators with relatively large constant weight.
In view of the advantages of exponential state
variable weight vectors, such as obvious decision-
making requirements, flexible parameter setting and
strong model expansion ability, this paper constructs
the expression of state variable weight vectors

 
as Equation 5 by
drawing on the relevant research results of variable
weight theory and satisfying the above three points of
analysis.













(5)
Where: n is the number of indicators;;
is the average attack effect indicator value;
are the penalty amplitude coefficient and the
excitation amplitude coefficient respectively and
;  is the penalty threshold coefficient;
when the value of the j-th indicator status value is not
higher than the penalty threshold or not lower than the
incentive threshold the weight is increased by
changing the weight to achieve the purpose of
punishment or incentive. In practical applications, the
evaluator should set and k according to the
specific requirements of the attack effect evaluation.
4 CONSTRUCTION OF
VARIABLE WEIGHT TOPSIS
EVALUATION MODEL
The basic principle of Technique for Order
Preference by Similarity to Ideal Solution (TOPSIS)
is to rank the evaluation objects by means of the
positive ideal solution and the negative ideal solution
in the multi-objective decision problem.
Theoretically, the positive ideal solution and the
negative ideal solution are the optimal solution and
the worst solution respectively, which are often not
achieved in reality. When evaluating the attack effect
of an attack scheme, the scheme should be judged
from the distance between the two ideal solutions to
determine the situation between the different schemes.
On this basis, the paper proposes a variable
weight TOPSIS model.
4.1 Construction of Normalized Multi-
Attribute Evaluation Matrix
There are m attack schemes to form a scheme set


 
, and n evaluation indicators constitute
the indicator set

, then the
evaluation sample value 

of
to

constitutes
the multi-attribute evaluation matrix X.










(6)
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218
4.2 Normalization of Evaluation
Matrix
The indicators are divided into qualitative indicators
and quantitative indicators. Qualitative indicators can
be normalized according to the quantitative principles
in Table 1, Table 2 and Table 3. The quantitative
indicators can be further divided into benefit
indicators and cost indicators, which are normalized
according to formula (1) and formula (2) respectively
to obtain the normalized matrix U as follows.










(7)
4.3 Determining Indicator Weights
Based on Variable Weight Theory
Firstly, the AHP method is used to calculate the
indicator constant weights, and the indicator constant
weight coefficient vector
 
is
obtained. Then according to the state variable weight
vector, that is, formula (5), the indicator weights

are
calculated.
4.4 Establishing the Weighted
Normalization Evaluation Matrix
Based on Variable Weight Vectors
The weighted normalization evaluation matrix Y is
obtained by multiplying the corresponding items of
the matrix U and the matrix W(U), and is expressed
as follows:







(8)
4.5 Calculating the Closeness of Attack
Schemes
1) Positive and negative ideal solutions are as follows:


 







(9)
2) The distance values between each attack
scheme and the ideal solutions are as follows:

 



 



(10)
3) The closeness of each attack plan and positive
ideal solution is calculated according to the following
formula:

 

 
(11)
Where
represents the closeness degree of
each evaluation scheme and the positive ideal
solution, and also indicates the degree of distance
from the negative ideal solution. Therefore, each
scheme can be evaluated and ranked by sorting
in
descending order.
5 CASE ANALYSIS
5.1 Simulation Environment Setting
The simulation environment is established under NS2.
The settings of network parameters and environment
parameters in the scenario are shown in Table 6.
Table 6: Simulation parameters setting.
Network
parameter
Set value
Scene
parameter
Set value
Simulation
area size
1000m*10
00m
Channel
attenuatio
n model
TwoRayG
round
Number of
network nodes
60
Antenna
type
Omni
Antenna
Channel type
Channel/
Wireless
PHY
protocol
Phy/Wirel
essPhy
Channel
bandwidth
2Mbps
MAC
Protocol
MAC/802
_11
Maximum
movement
rate
30m/s
Routing
Protocol
AODV
Transmission
distance
250m
Interface
Queue/dro
ptail
Data packet
size
512 Bytes
Wireless
network
interface
LL
At the beginning of the simulation, the initial
energy of all nodes is consistent, the packet loss rate
is maintained between 0% and 15%, and the attack
duration is 300 seconds.
Evaluation of Attack Effect in Ad Hoc Networks Based on Variable Weight TOPSIS Method
219
5.2 Quantitative Measurement of
Indicators
1) In the simulation experiment analysis of the Hello
flood attack scenario, the attack nodes are randomly
selected. as shown in Figure 2. The attack payload is
20 kb/s, and the number of attack nodes is 4, 8, and
12. The normalized values of the indicators are as
shown in Case 1, Case 2 and Case 3 in Table 7.
Figure 2: Simulation of the Hello Flood attack scenario.
2) In the simulation experiment analysis of the
wormhole attack scenario, the attack nodes are
randomly selected. as shown in Figure 3. The number
of malicious nodes is 4 and 8. The normalized values
of the indicators are as shown in Case 4 and Case 5 in
Table 7.
Figure 3: Simulation of the wormhole attack scenario.
Table 7: Indicator normalization values.
Indicator
Case1
Case2
Case3
Case4
Case5
Number
of
malicious
nodes
4
8
12
4
8
C1
0.68
0.43
0.31
0.58
0.39
C2
0.30
0.30
0.30
0.30
0.30
C3
0.40
0.40
0.40
0.40
0.40
C4
0
0
0
0
0
C5
0
0
0
0.42
0.67
C6
0.33
0.43
0.62
0.38
0.44
C7
0.30
0.42
0.51
0.33
0.42
C8
0.17
0.26
0.33
0.46
0.46
C9
0.28
0.43
0.62
0.52
0.68
C10
0.15
0.20
0.42
0.16
0.22
C11
0.32
0.46
0.45
0.46
0.58
C12
0
0
0
0
0
C13
0.083
0.10
0.13
0.067
0.067
C14
0.11
0.17
0.24
0.087
0.10
5.3 Obtaining Indicator Weights Based
on Variable Weight Theory
1) The basis weight of each indicator is determined
based on the AHP method. The judgment matrix of
the criterion level indicator 

based on the expert opinion is established as follows:
1
1/5
1/6
1/5
1/4
5
1
1/2
1
2
6
2
1
2
3
5
1
1/2
1
2
4
1/2
1/3
1/2
1
CR=0.0145<0.1, which satisfies the consistency
requirement, and the weights of the criterion layer can
be calculated as W= (0.0901,0.224,0.301,0.224,
0.160). Similarly, the weights of each indicator layer
can be calculated as shown in Table 8.
Table 8: Indicator weight values of each layer.
Aggressive
indicator
(0.485,0.340,0.175)
Confidentiality
indicator
(0.667,0.333)
Usability
indicator
(0.222,0.097,0.169,0.384,0.128)
Integrity
indicator
(0.667,0.333)
Reliability
indicator
(0.5,0.5)
Finally, the resulting constant weight vector is W =
(0.0441,0.0309,0.0159,0.149,0.0746,0.0668,
0.0292,0.0509,0.116,0.039,0.149,0.0746,0.08,0.08).
2) Determine the variable weight vector matrix.
According to the variable weight state vector, set
=0.5,, k=0.7. Combining the formulas (5),
(4) and the constant weight vector W, we can obtain
the variable weight vector matrix as:
W(U)=
    
    
    
    
    
    
    
    
    
    
    
    
    
    
CTISC 2019 - International Conference on Advances in Computer Technology, Information Science and Communications
220
5.4 Comprehensive Evaluation Results
and Analysis Based on Variable
Weight TOPSIS Method
Based on the weighted vector matrix W(U), the
weighted normalized evaluation matrix is further
calculated to obtain the closeness of the attack
scheme, as shown in Table 9. The rationality and
effect of the variable weight TOPSIS evaluation
model are verified by comparison with the calculation
results of the constant-weight TOPSIS evaluation
model.
Table 9: Closeness of each attack scheme.
Case1
Case2
Case3
Case4
Case5
variable-
weight
TOPSIS
0.1892
0.3341
0.4938
0.5079
0.7562
constant
weight
TOPSIS
0.1673
0.3304
0.4919
0.4831
0.7835
Using variable weight TOPSIS evaluation
method, the evaluation results rank of the attack effect
is Case 1 < Case 2 < Case 3 < Case 4 < Case 5. If
constant-weight TOPSIS method is used, the attack
effect evaluation rank is Case 1 < Case 2 < Case 4 <
Case 3 < Case 5. Comparative analyses of the results
of different methods are as follows:
1) Case 1 < Case 2 < Case3 and Case 4 < Case
5 are satisfied simultaneously. It means that for the
same attack scheme, the more malicious nodes, the
better the attack effect. According to this, the
rationality of the variable weight TOPSIS model has
been confirmed.
2) Case 4 is a wormhole attack initiated by four
malicious nodes. By establishing a fake malicious
channel to steal data packets, the network
performance and security performance of the target
network are simultaneously reduced. In comparison,
although Case 3 has more malicious nodes, the flood
attack only affects the network performance of the
target system. Therefore, the attack effect of Case 4 is
stronger than Case 3. The evaluation results of
variable weight TOPSIS model have shown that the
proposed model solves the limitation of the problems
in constant-weight TOPSIS model to some extent,
and is a more effective evaluation method.
6 CONCLUSIONS
Aiming at the problem of attack effect evaluation of
Ad Hoc networks, based on the comprehensive
consideration of the correlation between attack
complexity and attack effect, this paper constructs a
comprehensive evaluation indicator system of attack
effect. And by introducing the variable weight theory,
an attack effect evaluation model based on variable
weight TOPSIS is proposed. The model can
reasonably adjust the weights based on the change of
the attribute values of each indicator, and can obtain
a more reasonable evaluation result. The proposed
evaluation method overcomes the limitations of
traditional attack effect evaluation methods and
provides an effective reference processing method for
Ad Hoc network attack effect evaluation.
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