IMPROVEMENT OF FALSE REPORT DETECTION PERFORMANCE BASED ON INVALID DATA DETECTION USING NEURAL NETWORK IN WSN

International Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018
DOI: 10.5121/ijcnc.2018.10602 21
IMPROVEMENT OF FALSE REPORT DETECTION
PERFORMANCE BASED ON INVALID DATA
DETECTION USING NEURAL NETWORK IN WSN
Sanghyeok Lim and Taeho Cho
Department of Electrical and Computer Engineering, Sungkyunkwan University,
Republic of Korea
ABSTRACT
WSN consists of a number of nodes and base stations and is used for event monitoring in various fields
such as war situations, forest fires, and home networks. WSN sensor nodes are placed in fields that are
difficult for users to manage. It is therefore vulnerable to attackers, and attackers can use false nodes or
MAC injection attacks through the hijacked nodes to reduce the lifetime of the network or trigger false
alarms. In order to prevent such attacks, several security protocols have been proposed, and all of them
have been subjected to MAC-dependent validation, making it impossible to defend against false report
attacks in extreme attack circumstances. As attacks have recently become more diverse and more
intelligent, WSNs require intelligent methods of security. Based on the report information gathered from
the base station, the proposed method provides a technique to prevent attacks that may occur where all
MAC information is damaged by carrying out verification of a false report attack through the machine
learning based prediction model and the evaluation function.
KEYWORDS
Network Protocols, Wireless Sensor Network, simulation, machine learning, neural network
1. INTRODUCTION
The wireless sensor network (WSN) consists of sensor nodes and a base station (BS) [1], [2]. It is
installed and used in various situations, including war situations, home networks, and forest fire
monitoring. Sensor nodes are often placed in hard-to-manage locations and are equipped with
low-performance CPUs and low-capacity batteries to save network cost. A network attacker who
exploits such vulnerability could launch various kinds of attacks such as false report injection
attacks or false Message Authentication Code (MAC) injection attacks by compromising nodes.
These attacks cause unnecessary energy consumption of the node or cause false alarms at the BS
by sending a report about events that did not actually occur [3],[4],[5].Currently, several security
protocols are proposed to prevent false report injection attacks. These protocols determine
whether the report is authentic or not depending on whether the MAC included in the report is
normal or abnormal [6], [7],[8],[9].Even if filtering is not properly performed, the BS may
perform the final MAC check. However, in the case of a report that is generated in a cluster
where a large number of nodes are compromised, all MACs from the nodes participating in the
report generation will have normal values and the report is securely transmitted to the BS. The
final MAC check will also consider if it is a legitimate report. The BS sounds a false alarm and
responds abnormally, which leads to a high risk. Attacks are becoming increasingly diverse and
difficult to detect, and there are security vulnerabilities that rely solely on MACs. As a result, a
more intelligent method of security is needed. The proposed scheme is a security model for a
false report attack situation in a WSN environment used in forest fire detection with an event

22
prediction model and an evaluation function using a neural network model learned based on
report contents. A lot of data is needed to determine the authenticity of the report through the
contents received from the nodes. As it is not easy to receive data from the actual field, we use the
forest fire state variation model to extract the learning data through the simulation and use it to
teach the neural network. Section 2 introduces related research, Section 3 introduces the proposed
scheme with an attack scenario, and Section 4 explains the results of the experiment and
evaluation of the function’s validity. Finally, Section 5 outlines the conclusions.
2. RELATED WORK
2.1. PVFS
The probabilistic voting-based filtering scheme (PVFS) is a cluster-based application layer
security protocol [6]. All member nodes constituting the cluster are assigned a predetermined
number in the node placement step, and each node is assigned one key.
Figure 1. PVFS en-route filtering
When an event occurs, the cluster head node (CH) generates a report, and the member node
verifies the contents of the report. If the report is validated and found to be valid, the member
nodes attach the MAC generated by their own key to the report. This MAC is used for verifying
the authenticity of the report in the next verification node. The verification node is
probabilistically selected based on the number of hops between the event detection cluster and the
BS, as well as the number of hops between the node and BS. When the verification node receives
the report, it compares its own key index with the key index attached to the report. If the key
index overlaps, the MAC check is performed. If the index does not overlap, the report is not
verified and it is sent to the node on the next path. If a normal MAC is detected as a result of the
check, a normal vote is cast. If a false MAC is detected, a false vote is cast. If the number of votes
reaches a preset threshold value, the report is considered a false report and dropped immediately.
On the contrary, if a true vote reaches the true threshold value, report is then transmitted to the BS
without any further verification steps. Since the BS has all the key information, if the validation
nodes of the PVFS are not properly verified, the BS finally verifies the report. If at least one
MAC is abnormal in the report arriving at the BS, it is regarded as a false report and discarded. If
all MACs are normal, the report is determined a normal report and the BS responds as described
in the report.
2.2. Artificial Neural Network
A neural network in machine learning is a nonlinear model used to solve prediction problems in
data with complex structures. This data mining classification technique is for finding a certain

23
rule or pattern in a large amount of data [10-14].This method has the disadvantage that it is
difficult to explain the relationship between the target variables of the input variables, but it has
the advantage that the prediction power is very high. The neural network model contains a
component called a hidden node, which is a model of human neurons. Each hidden node receives
a combination of input variables and delivers it to the target variable. The neural network model
is shown in Figure 2.
Figure 2. Neural network model
In this case, the coefficients used for combining are called connection strengths. The activation
function converts each input value and outputs it to another node to be used as a new input.
Among various models, the multi-layer model (MLP) neural network is widely used for data
analysis. MLP is an unidirectional neural network composed of hidden layers and output layers
composed of input layer hidden nodes. The input layer is composed of nodes corresponding to
each input variable. The hidden layer is composed of several hidden nodes. Each hidden node is
processed by a nonlinear function of the linear combination of the variable values transmitted
from the input layer to the output layer or another hidden layer. It has the role of delivery. The
output layer consists of nodes corresponding to the target variable, and the size of the output layer
varies depending on the characteristics of the model. Currently, machine learning and neural
network engineering are used for such actions as a social prediction, situation recognition, and
image processing, and their performance is demonstrated by several studies [15], [16],[17],[18].
2.3 Cellular Automata Model
Several techniques and models have been proposed for modeling the spread of forest fires and
fires [19],[20],[21],[22].Since our goal is to confirm the spread of fire per unit time, we
constructed a simulated environment in a forest fire diffusion model based on cellular automata
(CA) for collecting forest fire pattern data. The current CA model is often used as the base
simulation model [23],[24], [25].Additionally, a CA-based simulation model for firefighting
similar to the simulation environment of the proposed system has been studied [26],
[27],[28],[29].CA also called a cell autologue, is a discrete dynamic system devised by American
mathematicians Stanislaw Ulam and John von Neumann in the 1940s that has been studied in
computational theory, mathematics, physics, complex systems, mathematical biology, and micro

structural models. This model is important because it can
phenomena reflecting diverse dynamic phenomena
automaton, which is the number of cellular automata, is divided into discrete
cellular automaton belonging to each cell consists of a uniform, regular grid with discontinuous
variables. Their state at a particular time is determined solely by the variables of each cell, and the
variables are influenced by the values of the neighborhood variables of the previous time step.
Neighborhood means a cell adjacent to a cell. All cells change simultaneously bas
neighboring variables of previous time according to
interaction and neighboring rules,
and a von Neumann method. As shown in
the state transition for each of
Alternatively, there is the von Neumann method that considers only four directions of up, down,
left, and right.
We use Moore's CA and the cells where the state transition occurs in the Moore method are the
same as those shown in Figure 3’s
cells. Based on the situation that the learning data
the actual field, the proposed method constructs the CA
the learning data.
3. PROPOSED METHOD
3.1. Problem statement
The WSN is installed and used in vast site
the application layer is an attack that occurs in order to send a report to the BS containing
information about an event that has not occurred, to cause a false
unnecessary energy from the intermediate verification
proposed to cover the authenticity of the current report. The existing application layer security
protocols depend on verification by the
or false based on whether the MAC value included in the report is normal or abnormal. This
means that if all the MACs included in the report
the reports are regarded as normal events and an inappropriate countermeasure is taken. This is
shown in Figure 4.
national Journal of Computer Networks & Communications (IJCNC) Vol.10, No.6, November 2018
structural models. This model is important because it can effectively model real natural
diverse dynamic phenomena, such as discrete time. The space of cellular
automaton, which is the number of cellular automata, is divided into discrete ‘
a particular time is determined solely by the variables of each cell, and the
Neighborhood means a cell adjacent to a cell. All cells change simultaneously bas
neighboring variables of previous time according to the local rule. Therefore, according to local
interaction and neighboring rules, the CA state transition is identified. CA has a Moore method
and a von Neumann method. As shown in Figure 3, the Moore method is a method of performing
the state transition for each of the eight adjacent cells of the reference cell every step.
, there is the von Neumann method that considers only four directions of up, down,
Figure 3.Moore style CA Model
same as those shown in Figure 3’s(i, j), (i, j + 1), (I, j + 1) (I + 1, j + 1), (i + 1, j), and (i + 1, j + 1)
. Based on the situation that the learning data cannot be retrieved from the node installed in
the actual field, the proposed method constructs the CA-based fire spreading model and obtains
WSN is installed and used in vast sites for event detection. A false report attack occurring in
information about an event that has not occurred, to cause a false alarm or to consume
y energy from the intermediate verification node. Several security protocols
verification by the MAC. The security determines whether the report is true
means that if all the MACs included in the report which contains invalid data are normal, all of
egarded as normal events and an inappropriate countermeasure is taken. This is
24
model real natural
as discrete time. The space of cellular
‘cells.’ Every
a particular time is determined solely by the variables of each cell, and the
Neighborhood means a cell adjacent to a cell. All cells change simultaneously based on
local rule. Therefore, according to local
. CA has a Moore method
e method is a method of performing
eight adjacent cells of the reference cell every step.
, there is the von Neumann method that considers only four directions of up, down,
(i, j), (i, j + 1), (I, j + 1) (I + 1, j + 1), (i + 1, j), and (i + 1, j + 1)
from the node installed in
based fire spreading model and obtains
for event detection. A false report attack occurring in
alarm or to consume
protocols are
The security determines whether the report is true
are normal, all of
egarded as normal events and an inappropriate countermeasure is taken. This is

Figure
The report generated in the compromised CH
verification process by the security protocol.
lower performance when the attack rate is higher.
major numbers of compromised
compromised, the attacker can inject a false report
These reports are not verifiable at the validation node and are regarded as normal reports and
arrive at the BS securely. The BS finally performs the MAC check of the corresponding report,
but all of them are judged as the normal MAC, and a false alarm is sounded.
that rely solely on MAC can’t function in this extreme attack situation.
security scheme that does not depend on
discrimination technique of the report contents by recognizing the situation occurring in the field.
Although research on tracking the location of
been performed, it can’t provide security ag
3.2. Simulation model
Figure 5
Figure 4. Various attacks in the WSN field
compromised CH is shown in Figure 4 is dropped during the
verification process by the security protocol. However, these types of security protocols have
lower performance when the attack rate is higher. Figures 4-(a) and (b) are clusters
compromised member nodes. If all the nodes participating in the report are
promised, the attacker can inject a false report attack that includes only the normal MAC.
The BS finally performs the MAC check of the corresponding report,
but all of them are judged as the normal MAC, and a false alarm is sounded. Security protoc
function in this extreme attack situation. Therefore, a
security scheme that does not depend on the MAC is needed. We propose an authenticity
Although research on tracking the location of corrupted nodes based on context awareness has
provide security against extreme attack situations[30].
5. Change of state of the simulation models
25
igure 4 is dropped during the
of security protocols have
(a) and (b) are clusters that have
If all the nodes participating in the report are
attack that includes only the normal MAC.
The BS finally performs the MAC check of the corresponding report,
Security protocols
Therefore, a secondary
We propose an authenticity
corrupted nodes based on context awareness has

26
The proposed method’s simulation model for extracting learning data is based on the Moore
method CA. There are three kinds of fire transition states normal, fire in progress, and burnt.
Considerations in the forest fire transfer model include the probability of the forest fire transition,
wind direction, and wind speed. The probability of fire between two different points is denoted as
Pt. The reason for considering the transition rate is that the probability of fire may vary depending
on the type of tree and how thick the dry branches or leaves of the tree are on the bottom of the
forest. In other words, if there is a place burning in a neighboring grid where the probability of
fire is low, it can be a space where the forest fire does not spread. There is no empty space in the
simulation space and it depends only on the transition probability. The wind direction changes in
8 directions, the wind speed is 1 - 10, and the fire probability is 50 - 100%. P shows the
probability that a tree with a value of Pr is affected by the wind and has a higher probability of
fire. Pw is a random variable representing the wind strength [31].The fire transition probability
function is as follows:
= + (1 − ) .
Figure 6 shows a brief overview of the field’s fire state transition process when CH = 2500, wind
direction = south, wind speed =7, and the maximum unit time (UT) is 15. As shown in Figure 5,
fire spreads in the south direction as UT progresses. The cell that is green means a normal state,
red is fire in progress, and the burned cell is colored black.
3.3. Overview
The proposed method is described briefly in this section. Each CH periodically sends a report to
the BS about the fire situation. If no report is received from the node, the area can be considered a
place where no fire occurred. Since the report contains the location information of each node, the
BS can collect the reports and draws a real-time situation map for the fire. The relationship
between a normal fire transition and the UT is learned through the artificial neural network based
on the Map shown in figure.6. We used simulation data using fire models instead of real fire data
for neural network engineering experiments because the actual fire data is costly and time-
consuming because it must be actually obtained in the field where the node is installed. The CA-
based fire prediction model considers wind direction, wind speed, and field conditions. Based on
the CA model based data and the evaluation function, the false report second detection algorithm
is completed. Figure.6 is a false report second defense scenario of the proposed system.
Figure 6. Flow chart of the proposed scheme

27
The BS starts counting UT when the fire is started. In the BS, the Map generated from reports
received from nodes is added to the neural network model for each actual UT to obtain the
predicted UT corresponding to the image of the Map. If both UTs are equal, then the BS
considers there is no attack occurring at the corresponding UT. If the two values are different, the
function argument values are inserted into the evaluation function to determine whether an attack
has occurred in the corresponding UT depending on the output of itself.
3.4. Artificial neural network model
Figure 7. Proposed neural network model.
The input value of the proposed neural network model is the forest fire progress value transmitted
from each node. A state can have three values in total: normal, in progress, and burnt. Therefore,
the volume of the input layer is the number of CHs installed in the whole field. In the output
layer, the UT of the MAP drawn by the corresponding input value is output with a label format.
The UT refers to the elapsed time until the state changes at each node. All states go to normal-fire
in progress-burnt. The learning rate is 0.01, Relu is considered the activation function, and the
dropout is considered to prevent a sticking phenomenon [32-34].

28
3.5. Evaluation function
The evaluation function of the proposed scheme can be expressed as follows:
= ∗α ∗β ∗ γ ,
where represents the gap between actual UT and predicted UT. represents reliability
of the model depending on the wind power. represents the reliability of the model depending
on UT. And represents the reliability of the model depending on the ignition starting
position. The minimum gap, which is the evaluation function’s threshold value, is 1 because the
evaluation function is executed at least when the difference between those values is 1.α, βand γis
the weight of each reliability value; the sum of these values is therefore 3. When the result of the
function F exceeds 1, the node data coming into the actual UT is regarded as false data that an
attack occurred. The reliability of the model, which is the criterion of the evaluation function,
depends on how well the model is built. The reliability of the model will be explained in Section
4. The reliability and weight of the model should be able to adjust the scope according to the
tendency of the network user.
4. EXPERIMENTAL RESULT
4.1. Experimental environment
Table 1. Experiment parameters
Item Value
Sensor field size(m*m) 1000 × 1000
Number of sensor nodes 1000 - 36000
Cluster size 40 - 60
Transmission range(m) 80
4.2. Assumptions
It is assumed that all of the attacked reports are false reports of MACs involved in the entire
report. Therefore, the BS judges that the report is a normal report. Since the tree firing probability
of a simulation is a field’s specific characteristics, it does not change after being set initially,
which is done at random. The wind direction and wind speed value were also not changed. For
the experiment, the only changes made to any values were those that needed to be changed for
each graph. All calculations and learning proceed on the BS. We used a tensor flow for machine
learning experiments, and the other environmental variables are shown in Table 1. The proposed
system is basically used for additional verification on the WSN where the security protocol
operates. It is assumed that PVFS is used in the experiment. This protocol is characterized in that
the initially set routing path and node position are unchanged. The WSN that the node moves in is
a mobile-WSN, which goes beyond the scope of the proposed scheme [35].

29
4.3. Experimental result
Figure 8. Error rate of neural network model by unit time.
Figure 8 shows the accuracy of the neural network model’s prediction performance in each UT
when the number of nodes and wind speed are constant. The prediction model has low accuracy
at the start of the fire phase and at the end of the fire spread. The reason that UT has a low
accuracy between 0 and 1 is that the range of state transitions that one cell can have when it
moves one unit of time from the start point is limited. In addition, it is somewhat difficult to
accurately discriminate the UT because the fire progresses to the edge of the field and there is no
further progression in the field with a small number of nodes. In this particular off-site UT, we
can see that most models have fairly high accuracy.
Figure 9. Accuracy of the neural network model by location of the ignition starting point.
The graph shows the prediction accuracy of the neural network model according to the distance
from the field center of the fire start point. Let D be the distance from the center of the ignition
starting point, i.e., the distance from the center to the end on the same line, as shown in Figure 10.
As shown in the graph, a larger D value is more difficult to predict.
0
20
40
60
80
100
120
0 1 2 3 4 5 6 7 8 9
E
rro
r
ra
te
Unit Time
OVERALL 100_5 100_8
400_5 400_8 900_5
900_8
60
65
70
75
80
85
90
95
100
0 10 20 30 40 50 60 70 80 90
A
ccu
ra
cy
Distance (%)

Figure 10. Relative distance of the ignition starting point.
The reason for this is that the neural n
of the MAP per UT. In the case of a fire that starts at the edge of the field or moves to the edge of
the field, no further fire state transition occurs or the probability of state transition
becomes low.
Figure 11. Prediction accuracy of the neural network model by wind velocity.
Figure 9 represents the relative prediction accuracy of the model by the wind speed when the
number of nodes is 400 and the unit
with high wind speed and the difference
maximum wind speed and low wind
0
20
40
60
80
100
0
R
elative
accuracy
Figure 10. Relative distance of the ignition starting point.
The reason for this is that the neural network model predicts based on the change in
the field, no further fire state transition occurs or the probability of state transition
Figure 11. Prediction accuracy of the neural network model by wind velocity.
number of nodes is 400 and the unit of time is 10. The neural network model has higher accuracy
with high wind speed and the difference in the error rate is approximately twice
low wind area.
1 2 3 4 5 6 7 8 9 10
Wind Speed
30
in the fire state
the field, no further fire state transition occurs or the probability of state transition occurrence
higher accuracy
twice that of the

Figure 12. Simulation results according to wind speed.
The reason for the large error failure is shown in Fig
when the wind velocity is 0, 3, 5,
10 units of time, and the picture shows some of
the spread of the fire does not proceed and it automatically evolves at the initial stage of ignition.
Therefore, the state transition does not occur even if the unit time progresses and it seems to be a
false detection in the model. Co
proposal evaluation function.
5. CONCLUSIONS
Attacks in WSNs become increasingly diverse and
reports of false reports due to cluster node deportation. The existing MAC
process did not provide security in this situation.
based prediction model and the
WSN security. The secondary security scheme of the proposed scheme has the advantage of high
scalability. The proposed method can be applied to the whole range of the report without
limitation of the model or situation as long as the format of the report shows the status value and
unit time. Therefore, we will introduce a model that will apply the proposed method to other
models (tank movement, precipitation warning, etc.) in the future. We will
additional tests to determine security accuracy
only possible to distinguish whether or not an attack has occurred in a
research, we plan to conduct a detection study o
occurred at the time of an attack.
Figure 12. Simulation results according to wind speed.
error failure is shown in Figs. 10 (a) - (d) with the fire state variation
when the wind velocity is 0, 3, 5, and 10, respectively. The state transition occurs up to a total of
time, and the picture shows some of the processes. In the case of the low wind speed,
fire does not proceed and it automatically evolves at the initial stage of ignition.
Considering this special situation, the wind speed is used
asingly diverse and more intelligent and can result in unfiltered
reports of false reports due to cluster node deportation. The existing MAC-based authentication
process did not provide security in this situation. Context awareness using the machine learning
the evaluation function of the proposed method was used to achieve
. The secondary security scheme of the proposed scheme has the advantage of high
situation as long as the format of the report shows the status value and
models (tank movement, precipitation warning, etc.) in the future. We will
determine security accuracy through the evaluation function. Currently, it is
only possible to distinguish whether or not an attack has occurred in a unit of time. In future
research, we plan to conduct a detection study on a node in which a false report injection attack
occurred at the time of an attack.
31
fire state variation
10, respectively. The state transition occurs up to a total of
low wind speed,
fire does not proceed and it automatically evolves at the initial stage of ignition.
is used in the
and can result in unfiltered
based authentication
machine learning-
used to achieve
. The secondary security scheme of the proposed scheme has the advantage of high
situation as long as the format of the report shows the status value and
also conduct
through the evaluation function. Currently, it is
unit of time. In future
n a node in which a false report injection attack

32
ACKNOWLEDGEMENTS
This research was supported by Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-
2015R1D1A1A01059484)
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AUTHORS
Sanghyeok Lim Received a B.S. degree in Digital Information Engineering from Hanguk
University of Foreign Studies in 2017, and is now working toward an M.S. degree in the
Department of Electrical and Computer Engineering at Sungkyunkwan University.
Taeho Cho Received a Ph.D. degree in Electrical and Computer Engineering from the
University of Arizona, USA, in 1993, and B.S. and M.S. degrees in Electrical and
Computer Engineering from Sungkyunkwan University, Republic of Korea, and the
University of Alabama, USA, respectively. He is currently a Professor in the College of
Information and Communication Engineering, Sungkyunkwan University, Korea.

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