Secure localization against malicious attacks on wireless sensor network
ISSN 2354-0575
SECURE LOCALIZATION AGAINST MALICIOUS ATTACKS
ON WIRELESS SENSOR NETWORK
Vinh V. Le1,*, Dang Van Anh2, Nguyen Thi Thanh Hue2, Tran Do Thu Ha2
1 National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
2 Hung Yen University of Technology and Education
Received: 05/05/2020
Revised: 20/08/2020
Accepted for publication: 15/09/2020
Abstract:
Wireless sensor networks (WSN) are very susceptible to location errors due to malicious attacks on
sensor nodes that distort the position of the sensor nodes, which will lead to an error during unknown
node localization. In this paper, we propose a localization algorithm to defend against independent
attacks and collusion attacks. In the algorithm, we first select three random reference nodes, then use the
trilateral detection method and the confidence interval to get rid of the malicious nodes. We then use the
PSO optimization algorithm to locate the unknown node, is called (Secure localization algorithm against
advanced attacks-SL4A). Through the simulation results, we prove that our proposed algorithm outperforms
the existing algorithms, in terms of the variability of malicious nodes and noise, average localization error
and degree computational complexity.
Keywords: Wireless sensor network (WSNs), Secure Location, Independent Attack, Collusion Attack.
1. Introduction
In recent years, a number of algorithms have
method filters out malicious beacons signals on the
basis of the consistency among beacons signals, the
second method accepts malicious beacon signals by
using the voting method to remove malicious nodes.
Propose combining clustering technique in attack-
resistant localization method [7]. The localization
method is to measure the received signal strength
(RSS) of any three points to make location
estimates and combine with the K-means clustering
method to eliminate malicious location. Based on
the clustering effect of localization results from not
attacked access points and based on the localization
results of attacked access points, it is divided into
two separate groups and achieve effective filtering
of toxic point locations.
been proposed for the secure localization of sensor
nodes, determining safe locations that are widely
used in health care, environmental monitoring,
and smart homes, IoT, and other commercial
applications [1], [2]. All of these WSN localization
algorithms have the same feature that they estimate
the actual location of nodes (anchors or beacons)
with location node (unknown or target).
The proposed PSO localization algorithms
do not take into account malicious attacks [3], [4].
When the beacon nodes are affected by the attacks
have a bad purpose, malicious nodes deliberately
enhance or weaken signal strength and affect the
accuracy of localization. Current localization
algorithms take into account anti-attack [5], [6]. In
[6], the localization algorithm based on minimum
mean square estimation and voting based is
proposed. In WSNs, the common attack methods
in localization are independent attack and collusion
attack [7], [8]. In [8], two methods are used based
on the exploration of beacon node locations that are
interfered with by malicious attack nodes: The first
Inthispaper, wedesignalocalizationalgorithm
against malicious attacks in two independent
attack and collusion attack environments: First, we
randomly select a set of three reference nodes and
use it with PSO position estimation algorithm. Next,
we use the confidence interval method to get rid of the
malicious nodes. Finally, we use the PSO optimization
algorithm to estimate the location of the unknown
node with the nodes within confidence intervals.
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ISSN 2354-0575
2. Network Model and Problem
misleading location information without restriction.
A) Collusion attack method: In Figure 2, the
beacon nodes B, C, and D signals send location
information to node A, because the node B, C, and
In this section, we first introduce the WSNs
node model. Next, we then introduced two
independent and collusion attack. Finally, we
investigate the effects of attacks on beacon nodes
in localization.
D
are affected by the attack leading to the RSSI signal
wrong, so the beacon nodeAwill be positioned to theA’
beacon node position for incorrect positioning results.
2.1. WSN Network Node Model
Figure 2. CollusionAttack model
B) Independent attack method: In Figure
3, the node B, C, and D signals send location
information to node A, since node B, C, and D can
be affected in part by independent attacks, credits
RSSI signal is stronger or weaker than the original
signal, leading to position A can be placed in
different positions A’, A’’ and A’’’ form big position
errors, independent attack model [10], using α and
β parameters to control and enhance RSSI signals.
Figure 1. WSN network node model
Figure 1 shows our WSN nodes model
consisting of three types of nodes: 1) M is an
unknown node, 2) A, B and C are beacon or anchor
nodes, and 3) A’ is a malicious node. Where B and C
are benign nodes that have not been attacked, node
A is attacked by malicious node A’. Therefore, the
check node will assume that the position of node
A is at the position of node A’. The distance from
nodes A, B and C to the unknown node is called the
measurement distance.
Normally, when the beacon nodes sends
location information to an unknown node, the
unknown node receives a signal from the beacon
nodes and based on the signal strength, it converts
the measurement distance between the beacon
nodes and unknown node. Therefore, when the
beacon nodes are attacked by malicious nodes, will
lead to unknown node localization error.
Figure 3. Independent Attack model
2.3. Position Estimation Unknown Node with
Presence of Attacks
In the WSN network, the correct localization
of the network node will help routing, save energy,
and then maintain the life of the node and the
entire network. But it poses challenges because
the appearance of an exception attacker changes
the position of benign nodes, or increases and
decreases the signal strength. Therefore, methods
of location estimation and location measurements
from beacon nodes to unknown nodes position will
lead to localization errors. The issue of estimating
the location in the presence of malicious attackers
is as follows:
2.2. Model Attack Location and Noise
In the presence of malicious attacks, location
deviation measurements from compromised beacon
nodes can fool sensors with unknown coordinates.
Malicious measurements that are injected into the
system by the attackers are the exception. The
solution of benign position estimates is generally
largely affected by malicious attacks. We allow
many independent and collusive attacks to transmit
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Consider a WSN with N anchor nodes and describe a particle i: (1) current location Xi = [xi1,
an unknown node. The location coordinates of the xi2, … xiN]; (2) the previous location Pbesti = [pi1, pi2,
anchor nodes i can be expressed as Zi = [xi yi]T, 1, …, piN] of the best fitness; and (3) current velocity
2, ..., N, and the position of the unknown node is Z0 V = [V , V , …, V ] . Besides, Gbest = [G1, G2, …,
i
i1
i2
iN
= [x0 y0]T .
GN] denotes the position of the best particle so far
(ie, Gbestd is the smallest of all Pbestid). At each
iteration k, the velocity vid and position Xid of each
particle are updated according to the following
equations.
di = Zi − Z0 = (xi − x0 )2 + (yi − y0 )2 ,
i =1, 2,, N (1)
Where di is the estimated distance between Zi and
V (k 1) wV (k) c1r1(Pbestid Xid ) c2r (Gbestd Xid )
id
id
2
Z0.
is the measured distance between Zi and Z0
di
according to RSSI.
( 3 )
(4)
Since the actual distance
is not known, the
di
Xid (k 1) Xid (k) V (k 1)
id
objective of localization is to minimize the difference
where, d =1,2, …, N; i=1, 2, …, K; and K is the
size of the swarm population, it is denotes iteration
number, r1 and r2 are random numbers between
[0,1], and c1=c2=2 are respectively the cognitive and
social learning parameters, ω is the inertia weight.
(x , y )
of unknown
between d and i .Therefore, the
d
0
0
i
node is estimated by the following equation:
N
arg min
2
(d − d ) (2)
(x0 , y0 ) =
∑
i
i
x0 , y0
i=1
where N is the number of anchor nodes used to
estimate the position of an unknown node. Equation
(2) uses the (Least Square-LS) method, which is a
mathematical optimization method [9], [10]. The
applied LS is to minimize the total square of the
location error.
The inertial weight is added to element w of
the original PSO algorithm as in Eq. (5). The bigger
value of ω is beneficial for particles to jump out
of local minimum points, the smaller value of ω
is favor the algorithm convergence. As originally
developed, ω often decreases linearly with the
number of iterations. Therefore, ω can be set
Due to location estimation error, the position
(x0 , y0 )
in Equation (2) is often a multimodal
according to the following equation.
wini wend (Tmax t)
function. Therefore, we use the PSO optimization
algorithm to estimate the error location, then use
the CI confidence interval method or the PSO
group method with the smaller value group. Then
calculate the average position from the benign
position estimates (not attacked by malicious nodes)
obtained from the confidence interval method and
the PSO group. Average position is the estimated
position of unknown node.
w
wend
end
(5)
Tmax
wini
where
is the initial weigh.
is the final
T
weight value,
is the total number of iterations,
max
and t is the current number of iterations. In previous
experimental studies, ω was often set from 0.9
reducing linearly to 0.4.
4. Secure Localization Algorithm Against
Advanced Attacks (SL4A)
3. Particle swarm optimization algorithm (PSO)
Our idea throughout the paper is to develop
the localization estimation algorithm based on the
PSO optimization paradigm. In this section, we
first present details of how to implement the PSO
paradigm to estimate the unknown node’s location.
In PSO, all individuals in a population are seen as
particles in a N-dimensional solution space, the
fitness value is determined by its location in the
search space.
Existing algorithms for estimating attack
resistance positions often use as many beacon nodes
as possible to estimate unknown node coordinates
[12], [8]. The idea behind the algorithm LS4Ais that
we select three random points for an initial subset,
using the trilateral detection paradigm and the PSO
optimization algorithm to identify measurements in
confidence intervals.
To find the coordinates of a node requires
at least 3 distance measurements. Therefore, to
estimate the position of an unknown node from a
randomly chosen subset of 3 nodes, Ωi (Step 4). In
Assume, in an N-dimensional objective search
space. Three N-dimensional vectors are used to
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4.1. The Trilateral Detection Method: [13] is
derived from a beacon node detection method
developed from the theorem of a triangle. The
total 2 sides of the triangle are larger than the third
side, and the subtraction of 2 sides of the triangle is
smaller than the third side. Given a triangle ∆ABC
and the corresponding edges are a, b, c, the three
sides must satisfy a + b > c, a + c > b, b + c > a,
a − b < c, a − c < b and b − c< a.
an attack network model, any node can be attacked,
so that to detect an attack beacon node, (Step 5) uses
the trilateral detection method to detect whether a
set Ωi has been maliciously attacked. If the set Ωi
is not attacked (Step 6) use the PSO algorithm to
find the location estimate. Next (Step 7), calculate
the K value by the Equations (7), of all cases falling
into the confidence interval e. When K = N, the
estimated location ofc
is within the confidence
Z0
interval of all the beacons, and its coordinates are
completely removed by malicious attacks.
N
if (−1.96 ≤ e ≤1.96),1
i
K = u = where u =
(7)
∑
i
i
otherwise,0
i=1
If the value of K is greater than the threshold value
of t given (Step 8). Once a consistency set has
been identified, the algorithm uses all points in the
confidence interval set to form the final estimate ofc
Z0
and it terminates (Steps 9). In our experiments,
we use the PSO optimization algorithm to calculate
the initial estimate from the Zi subset and for the
final estimate obtained from the set within the
confidence interval.
Figure 4. The trilateral detection method
For example, in Figure 4 M is called an
unknown node or a measured node, A and B are
anchor nodes that provide positioning information.
M node is measured uses RSSI signal strength to
convert the distance between A and B anchor nodes
into dAM distance and dBM, and then use the known
distance dAB to perform trilateral detection, then
assess whether or not to meet the principles of a
triangle.
If the algorithm performs all iterations
and does not find the confidence interval have a
minimum size t. Then it will either declare a failure
Z
or give an
position estimate acceptable from the
0
largest confidence interval (Steps 13).
Algorithm Secure Localization Algorithm Against
4.2. Confidence Interval (CI): We assume that not
all beacon nodes are attacked by malicious nodes,
meaning that non-toxic distance measurement
errors are Gaussian random variables distributed
according to N(0,σ2), error variance is the RSSI
distance measurement technique in this paper. The
distance measurement error e computes the distance
between the actual position of with the reference
position (xi , yi) according to the Equations (8).
Advanced Attacks (SL4A)
1: Input: unknown node Z0, maximum number of
interations imax, Set L, confidence interval CI,
threshold t;
2: Ouput: estimated positionc
3: for iter=0 to imax do
(x , y )
;
Z0
0
0
4:
5:
6:
7:
Randomly select a subset Ωi of size 3 from L;
if TriEdgeCheck(Ωi) then
Call PSO-Based Localization Algorithm;
Calculate K, the number of points in the
confidence interval with to the estimatedc
Z0 in e;
e = di − (xi − x0 )2 + (yi − y0 )2 (8)
In statistics, the confidence interval of the
probability sample means estimating the interval of
some of the overall parameters of the sample [17].
The confidence interval is a distance estimation
method that uses a range to estimate a parameter.
This shows that the true value of this parameter
is likely to fall into the measurement result, the
8:
9:
10:
11:
if K > t then
return (x0 , y0 )
end if
end if
;
12: end for
13: return (x0 , y0 )
;
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probability is called the level of Confidence 1 - α, is simulated for 10,000 times.
α is the probability of not falling into the range.
We compare the performance of the new SL4A
For example, as shown in Figure 5 we apply the algorithm with current algorithms: LMS [16],
confidence interval method to measure errors in the RANLD [15], IDM [11] and Cluster-NLS [14].
estimated position coordinates, 95% CI are a given First, compare the secure location in the collusion
level taken from the Normal distribution N(0,σ2), attack environment, in which the number of attack
be within the standard deviation [-1.96e, 1.96e], of nodes in the environment ranges from 3 to 7 nodes.
which 1 - α = 95%; α = 5%. In other words, e is Next, compare the safe position in the collusion
in the CI confidence interval with a probability of attack environment, where the noise variation is
more than 95%.
1, the attack intensity Alpha varies from 0.6 to 1.4
and β = 0. With α have strength from 0.7 to 1 is a
weakening attack, and α from 1 to 1.4 is a signal
booster attack.
5.1. Collusion Attack: Figure 6(a), under the
scenario of collusion attack by changing malicious
nodes. The average localization error of the
algorithm increases as the number of malicious
nodes increases. Because the number of malicious
nodes directly affects the power of collusion attacks.
When the number of malicious nodes is 3 and 4, the
Figure 5. Example of 95% confidence interval
5. Simulation Evaluations
Two types of the simulation scenario with Cluster-NLS algorithms have the smallest average
network environment are 50x50. Total of 15 beacon localization error. When the number of malicious
nodes including 3 to 7 malicious nodes; and 1 nodes is 5, 6 and 7, the SL4A and RANLD
unknown node. Let α = 1.1 and β = - 10. Scenario algorithms are best against collusion attacks. Based
1, evaluates the influence of the collusion attacks on the empirical results, it can be concluded that
by changing malicious nodes, and Scenario 2, when the number of malicious nodes is small in
evaluates the influence of the independent attacks collusion attack, the secure location algorithm using
by changing Gaussian noise intensity. The program group combination shows better localization ability.
Fig 6. Comparing secure localization algorithms against attacks:
Fig. 6(a) is collusion attack and Fig. 6(b-f) are independent attacks
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5.2. Independent attack: Figure 6(b) to Figure caused by malicious attacks on anchor nodes. Most
6(f) with the scenario of independent attacks by localization algorithms are suffer from malicious
changing Gaussian noise intensity. In the cases of attacks, resulting in a large difference between
number of attack nodes changer from 3 to 7 nodes. the estimated location and the actual location
We can see that when the attack intensity α = 0.9 of an unknown node. However, there are a few
and α = 1.1, all algorithms are affected by the worst localization algorithms that considers independent
attacks power. With different attack intensities, the attack mechanisms, due to inappropriate algorithmic
SL4A and RANLD algorithms have the smallest architectural design, the ability to resist malicious
average localization error. IDM algorithm randomly attacksis limitedandthe complexityofthe algorithm
chooses 5 nodes to estimate position. Therefore, is too high compared to hardware devices. In this
the average localization error in the middle. As paper, we have proposed a anti-attack security
the signal strength increased, the Cluster-NLS and algorithms SL4A with stability and robustness,
LMS algorithms had average localization errors algorithm focused on combating independent
increased accordingly.
attacks and collusion attacks. Compared to existing
secure localization algorithms, our two algorithms
resist malicious attacks and calculate the location
6. Conclusions
In WSNs there is practically a lot of noise coordinates of the unknown node more accurately.
References
[1]. Y.S.I.F.Akyildiz,W.SuandE.Cayirci,“Asurveyonsensornetworks,” IEEE Communications Magazine,
2002, vol. 40, no. 8, pp. 102–114.
[2]. L.Doherty and K. Pister, “Convex position estimation in wireless sensor networks,” in Proceeding
of IEEE International Conference on Computer and Communications Societies (INFOCOM), 2001,
vol. 3, pp. 1655–1663.
[3]. T. He and C. Huang, “Range-free localization schemes for large scale sensor networks,” ACM
MobiCom, San Diego, 2003, pp. 81–95.
[4]. D. Niculescu and B. Nath, “Dv based positioning in ad hoc networks,” Telecommunication
Systems, 2003, vol. 22, no. 1, pp. 267–280.
[5]. C. H. Chang and W. Liao, “A probabilistic model for relative location estimation in wireless
sensor networks,” IEEE Communications Letters, 2009, vol. 13, no. 12, pp. 893–895.
[6]. O.M.R.Weber,E.MademannandS.Zeisberg,“Localizationtechniques for traffic applications based
on robust wecols positioning in wireless sensor networks,” Proceedings of Workshop on Positioning
Navigation and Communication (WPNC), 2012, pp. 215–219.
[7]. J. Cao, “A localization algorithm based on particle swarm optimization and quasi-newton
algorithm for wireless sensor networks,” Journal of Communication and Computer 12, 2015, pp.
85–90.
[8]. J. M.-S. . E. R.-I. . A. E.-R. . L. Rizo-Dominguez, “A comparative of range free localization
algorithms and dv-hop using the particle swarm optimization algorithm,” in 2017 IEEE 8th Annual
Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), 2017, no.
17489361, pp. 150–157.
[9]. W. T. Y. Chen and R. Martin, “Attack detection in wireless localization,” in Proceeding of IEEE
International Conference on Computer Communications (INFOCOM), 2007, pp. 1964–1972.
[10]. S. U. M. Jadliwala, S. Zhong and C. Qiao, “Secure distance-based localization in the presence of
cheating beacon nodes,” IEEE Transactions on Mobile Computing, 2010, vol. 9, no. 6, pp. 810–823.
[11]. C. C. C. S. H. Fang and C. Wang, “Attack-resistant wireless localization using an inclusive
Khoa học & Công nghệ - Số 27/Tháng 9 - 2020
Journal of Science and Technology
59
ISSN 2354-0575
disjunction model,” IEEE Transactions on Communications, 2012, vol. 60, no. 5, pp. 1209–1214.
[12]. L. S. J. Jiang, G. Han and H. C. Chao, “A novel secure localization scheme against collaborative
collusion in wireless sensor networks,” Proceedings of Wireless Communications and Mobile
Computing Conference (IWCMC), 2011, pp. 308–303.
[13]. J. H. N. Bulusu and D. Estrin, “Gps-less low cost outdoor localization for very small devices,”
GPS-less low cost outdoor localization for very small devices, 2000, vol. 7, no. 5, pp. 28–34.
[14]. V. B. L. J. Yang, Y. Chen and V. Swaminathan, “Robust wireless localization to attacks on access
points,” Proceeding of IEEE Sarnoff Symposium, 2009, pp. 208–212.
[15]. N. Kiyavash and F. Koushanfar, “Anti-collusion position estimation in wireless sensor networks,”
in Proceedings of IEEE International Conference on Mobile Adhoc and Sensor Systems, 2007, pp.
1–9.
[16]. Y. Z. Z. Li, W. Trappe, “Robust statistical methods for securing wireless localization in sensor
networks,” in IPSN 2005. Fourth International Symposium on Information Processing in Sensor.
BẢN ĐỊA HÓA AN TOÀN CHỐNG LẠI CÁC CUỘC TẤN CÔNG ĐỘC HẠI
VÀO MẠNG CẢM BIẾN KHÔNG DÂY
Tóm tắt:
Mạng cảm biến không dây (WSN) rất dễ bị lỗi vị trí do các cuộc tấn công độc hại vào các nút cảm biến
làm sai lệch vị trí của các nút cảm biến, điều này sẽ dẫn đến lỗi trong quá trình nội địa hóa nút không xác
định. Trong bài báo này, chúng tôi đề xuất thuật toán bản địa hóa để chống lại các cuộc tấn công độc lập
và các cuộc tấn công thông đồng. Trong thuật toán, đầu tiên chúng tôi chọn ba nút tham chiếu ngẫu nhiên,
sau đó sử dụng phương pháp phát hiện ba bên và khoảng tin cậy để loại bỏ các nút độc hại. Sau đó chúng
tôi sử dụng thuật toán tối ưu hóa PSO để xác định vị trí nút không xác định, được gọi là (Secure localization
algorithm against advanced attacks-SL4A). Thông qua kết quả mô phỏng, chúng tôi chứng minh rằng thuật
toán đề xuất của chúng tôi có hiệu suất tốt hơn các thuật toán hiện có, về sự thay đổi của các nút độc hại
và nhiễu, lỗi cục bộ hóa trung bình và độ phức tạp tính toán.
Từ khóa: Wireless sensor network (WSNs), Secure Location, Independent Attack, Collusion Attack.
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