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一种基于到达时间差的无线传感器网络中Sybil攻击检测方案

一种基于到达时间差的无线传感器网络中Sybil攻击检测方案
一种基于到达时间差的无线传感器网络中Sybil攻击检测方案

1Project supported by the Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP), under grant No. 20050248043.

TDOA-based Sybil Attack Detection Scheme for

Wireless Sensor Networks 1

Wen Mi, Li hui, Zheng Yanfei, Chen Kefei

Cryptography & Information Security Lab, Shanghai JiaoTong University, Shanghai,

China (200240)

E-mail :superwm@https://www.doczj.com/doc/d45200789.html,

Abstract

As wireless sensor network are deployed in fire monitoring, object tracking applications, security emerges as a central requirement. A case that Sybil node illegitimately reports messages to the master node with multiple non-existent identities (ID), will cause harmful effects on the decision making or resource allocation in these applications. In this paper, we present an efficient and lightweight solution for Sybil attack detection based on the Time Difference of Arrival (TDOA) between the source node and beacon nodes. Our solution can not only detect the existence of Sybil attacks but also locate the Sybil nodes. And we demonstrate the efficiency of our solution through experiments. The experiments show that our solution can detect Sybil attack cases with 100% completeness.

Keywords: Attack Detection; Sybil Attack; Time Difference of Arrival; Wireless Sensor Networks

1. Introductions Wireless sensor network (WSN) has recently emerged as an important application resulting from the fusion of wireless communications and embedded computing technologies. It has been wildly applied in all fields, including monitoring, location, tracking and foresting. However, the nature of wireless sensor network makes them vulnerable to security attacks. Especially, without a trusted centralized authority, the Sybil attack is always possible. The Sybil attack introduced in [1] denotes an attack that the Sybil node tries to forge multiple identifications to broadcast messages in a certain region. Broadcasting messages with multiple identifications can be exceedingly harmful to many important functions of the sensor network, such as voting, fair resource allocation, group based decisions, routing, data aggregation, misbehavior detection etc. A number of protocols for Sybil attack prevention have been proposed in recent years. But most of them are too costly for the resource-poor sensors. Douceur [1] proposes a resource testing method. It assumes that each physical entity is limited in some resource. The verifier tests whether identities

correspond to different physical entities by

verifying that each identity has as much of the tested resource as a physical device. It is unsuitable for wireless sensor networks, because the attacker may be using a physical device with several orders of magnitude more resources than a resource starved sensor node. Karlof [2] commends to use a Needham- Schroeder like protocol to verify each other’s identity and establish a shared key. Consequently, it can limit the number of neighbors a node allowed to have and send an error message when a node exceeds it. But this method just limit the capability of the Sybil attack and it can’t locate the Sybil node and remove it. Newsome [3] adopts key validation for random key pre-distribution and registration; however, they consume precious memory space as every node is required to store pair-wise keys with neighbors. Bazzi [7] prevents Sybil attacks via geometric

distinctness certification, which tests that amongst a group of identities a large enough subset resides on a set of distinct entities. It’s too complex and energy consumptive. Demirbas [8] presents a scheme based on the received signal strength indicator (RSSI)

readings of messages to detect the Sybil attack. This is the one most close to ours.Zhang[9] proposes a suite of location-based compromise-tolerant security mechanisms, which based on a new cryptographic concept called pairing. To the best of our knowledge, pairing is energy-consuming and it is not suitable for the sensor networks.

The major contribution of this paper is that, it proposes a Time Difference of Arrival (TDOA) based solution for Sybil attack detection and demonstrates its efficiency by experiments. This solution can not only detect the existence of Sybil attacks but also locate the Sybil nodes. It requires minimal storage and communication overhead for sensors, as they are listened by three beacon nodes in each cluster, which are assumed to know their own locations (e.g., through GPS receivers or manual configuration).It also does not burden the WSN with shared keys or piggy backing of keys to messages. The essential point of the TDOA based solution is associating the TDOA ratio with the sender’s ID. Once the same TDOA ratio with different ID is received, the receiver knows there is a Sybil attack. To use TDOA ratio instead of TDOA to associate the ID is to avoid the sensors at the circle centered at one of the beacon nodes being misdiagnosed.

This paper is organized as follows. In section 2 we discuss the network model and methodology. In Section 3 we present our Sybil attack detection schemes. In Section 4 we discuss the experiments of our solution, and in section 5 we analyze the performance of our solution. Finally, in Section 6 we give our conclusion and future work.

2. Network model and

methodology

2.1 Network model

We assume a static network, where all

nodes are deployed randomly over a 2-dimensional monitored area (it can be easily

expand to 3- dimensions). If the nodes are deployed too dense, on one hand, the position of more than one node may be located at the same place. This location error may influence the detection of the Sybil attack. On the other hand, in the large scale network, which deployed outdoor, it’s expensive to deploy nodes too densely. So, we assume that the density of the sensor network is beyond 10m (10m is the position accuracy of MTS420C, which is the MICA2 GPS Sensor Board of Crossbow).We assume there are time synchronization between the source nodes and the beacon nodes. Three beacon nodes 123,,S S S with known coordinates (1X ,1Y ), (2X ,2Y ), and (3X ,3Y ) respectively, are placed at the boundary of the monitored area (usually a cluster), as shown in Fig1. Let (x, y) be the Sybil node’s location, which will be determined by time-based positioning schemes [4], [5]. Each node can reach all beacon nodes in the cluster. Note that Sybil node can forge non-existent multiple identities.

2.2 Time Difference of Arrival Principle

The TDOA of a message can be estimated by Hyperbolic Position Location Solution (HPL) [6].Assume that 1S is the master beacon node. The distance between the source and the i

th beacon node is

i R = (1)

Now, the distance difference between

Fig1Hyperbolic Position Location Redraw

[5]

beacon nodes with respect to 1S is given as ,1i R =,1i cd = i R ? 1R (2)

Where c is the signal propagation speed,

,1i R is the range distance difference between 1S and (1)i i S >, ,1i d is the estimated TDOA

between 1S and (1)i i S >. This defines the set of nonlinear hyperbolic equations whose solution

gives the 2-D coordinates of the source. From (2) we know that,

i R = ,1i R +1R (3)

Subtracting (1) at i = 1 from (3) results in 2,1i R +12i R R

=

2

i X +2

i Y ?2,1i X x ?2,1i Y y ?

2

1X ?

2

1Y (4)

Where ,1i X and ,1i Y are equal to 1i X X ? and 1i Y Y ? respectively. And without loss of generality, we assume the beacon node 1S is located at (0, 0).From (2) we obtain

2221R x y =+ (5)

For a three base station system, Chan's method [6] producing two TDOA to render solution for x and y in terms of 1R is in the form (6) as

1

22,12,12,12,121123,13,13,13,1

311*2X Y R R K K x R y R X Y R K K ????????+?????

?????=?+???????????+??????????????Where,

22111K X Y =+,22222K X Y =+ , 22333K X Y =+,

2,12,1,R cd =3,13,1R cd =

On the right side of above equation, all the quantities are known quantities except 1R . Therefore solution of x and y will be determined by 1R . When these values of x and y are substituted into the equation (5), a quadratic equation in terms of 1R is produced. Once the roots of 1R are known, values of x and y can be determined.

3. TDOA-based Sybil node detection Here we first present a basic TDOA-based Sybil attack detection protocol in section 3.1, and in section 3.2, we propose an advanced one by considering the environment errors.

3.1 Basic Algorithm

We are going to use the localization algorithm in section2.2 to detect the Sybil attack as follows. Once hearing a message {,}i x m data D = from source S, the three beacon nodes record its arriving time respectively, for example, 123,,t t t at 123,,S S S . The master beacon node 1S can compute the Time Difference of Arrival when receive 23t and t from 23S and S and deduce the location of the source using equation (2) and (6).Then 1S associates this location with the source-ID included in the https://www.doczj.com/doc/d45200789.html,ter,when another message with a different source-ID is received and the location of the source is computed to be the same as the previous one,the beacon nodes detect a Sybil attack.

But, it is costly and very inconvenient to calculate the location of every node at every communication session using equation (6). In fact, we do not need this computation for Sybil node detection, because it is possible to detect Sybil attack by just recording and comparing the ratio of TDOA for the received messages. Only after the Sybil attack is found can we use equation (6) to locate the Sybil one.

Suppose, a Sybil node forge its ID as D1, D2…, Dx and so on. Considering at session1, a Sybil node broadcasts message 1{,1}m data D = with D1. When beacon nodes hear the message from Source node, they transmit their own ID and the arriving time of message 1m as 1(1){,1,}i i i report S D t >= to 1S .

1S will use

1

,1111*()/D i i i d t t R R c =?=? (7)

to denote the TDOA value between (1)i i S >and itself. Then, 1

S computes

the ratio

11

12,13,1/D D tr d d = (8)

and stores it locally.

Similarly, at session2, the Source node

broadcasts another

message 2{,2}m data D =with D2. When

beacon nodes hear the message, they also

transmit their own ID and the arriving time of

message 2m as 2(1){,2,}i i i report S D t >=

to 1S .1S computes the ratio

22

22,13,1/D D tr d d = (9)

Now,1S can test by comparing the ratio at session1 and session2. Definition 1 (Sybilly) A Sybilly is defined as

a phenomenon in which the TDOA ratio in two

session i and j are same, i.e.0i j tr tr ?=. If the difference between two ratios is

very close to zero, the beacon

node 1S concludes that the Sybil attacking is

happening in the cluster and the Sybil one is at the location of (x, y),which can be calculated

with the method of section 2.2. The received TDOA ratio is same, which means the sources’ locations are same. In other words, the same source node broadcasts messages with multiple IDs. That’s the classical behavior of the Sybil attack. On the contrary, 1S can tell there is no Sybil node. That means, here if

120tr tr ?= (10)

is true, we can detect a Sybil attack.

3.2 Advanced One for Error Tolerance

Theoretically, TDOA should stay the same if the locations of the two transceivers

are fixed, but in practice, there are three major

sources of errors [4] for our time based location detection scheme: the receiver system

delay, the wireless multipath fading channel,

and the nonline-of-sight transmission. These

factors make the measurement

of 2,1R and 3,1R fluctuate a lot and correspondingly they will cause the fluctuation of the TDOA ratio. So we should

consider the variance of the TDOA ratio and

quantify the range of it.

For simplicity and error tolerance, we let

the threshold used to detect a Sybil node be

α, and apply the algorithm described in

section 3.1. We can detect Sybil attacks

robustly using the following formula.

Definition 2 (α-Sybilly) A α-Sybilly is

defined as a phenomenon in which the TDOA

ratio in two session i and j are only different

within the probability of α, i.e.i j tr tr α?<.

If the Source nodes’ identities D1 and D2

are different but their location is adjacent, we can infer the Sybil attack by noticing that the

difference of TDOA ratio for both cases is within the threshold α. That means, if i j tr tr α?< (11)

is true, we can detect Sybil attack. In next section we will quantify the range of the TDOA ratio variance and evaluate the effect of different factors on αby experiments. 4. Experiments

Since the Basic scheme only have theoretic value, in this section, we just base our experiments on the advanced scheme. From the equation (2), (6) and (7), we can see that to determine the TDOA from source S, 2,1R and 3,1R are the main factors. The inaccuracies of 2,1R and

3,1

R will cause the

TDOA errors. We assume the calculation of 2,1R and 3,1R will be repeated enough times to

average their errors. Also certain measures

will be taken to reduce the effect of the three

major sources of errors. For example, as [4] mentioned, beacon nodes can be placed well above the surrounding objects to avoid the

multi-path fading and non-line-of-sight transmission, and the system delay can be predetermined to calibrate the time measurements. Therefore, the errors of 2,1R and 3,1R are approximately normally distributed. So the measuring errors of TDOA

are approximately normally distributed. Without loss of generality we assume the error

of

2,1

R and

3,1

R are distributed according to

21Ν(0,)σand 2

2Ν(0,)σ respectively.

The beacon node we used in our simulations is MTS420CA, which is the MICA2 GPS Sensor Board of Crossbow. The source node is MDA300- CA, which is the MICA2 Data Acquisition Board of Crossbow. In our simulation, we let the three beacon nodes locate at 1S (0, 0), 2S (0, 2000) and 3S (2000, 0). And we write our detection

program according to our method stated in section 2.2 and 3.2, and the TDOA ratio was computed from the equation (7). For each trial, we try our method 10000 times.

4.1 The source is dynamic

In this scenario, we study the distribution of TDOA ratio errors over a 2D planar monitored area. We let the source node move according to the diagonal of the rectangle, which is determined by the coordinates of the three beacon nodes and the point (2000, 2000). We will compare the difference of the TDOA ratio. Now source is placed at the point (i*100, i*100), where i 1, 2,3,…,20.We let the measuring errors distribute according to 2Ν(0,)σ. For easy comparison, we set

21σ=2

2σ=2σ.

Here we let 2σ=0.05, which corresponds

to the TDOA deviation of ±0.22unit [4]. In

our simulation, we observe that with the distance from the source to three beacon nodes augmenting, the variance of TDOA ratio become larger and larger. Also when the source is close to any of the three beacon nodes, the error becomes larger. But specially, when the source arrives at (1000, 1000), the intersection of the perpendicular bisector of isosceles right-angled triangle 123S S S ?,the variance of TDOA ratio become the smallest. That’s because the distance from the source to the three beacon nodes is the same at this point. The measuring error influence is almost the same. This result is similar to that of [4].Fig 2 shows us the results.

From this analysis, we can conclude that the variance of TDOA ratio relate to the position of the beacon nodes and the source sensor. This result will help us to do better deployment of beacon nodes for better performance in other studies.

4.2 The source is static

Next we consider the scenario when

sensors are randomly deployed in a square region with lower-left corner (0, 0) and upper-right corner (2000, 2000). In this scenario, the error model we will study is: normal distribution according to 2Ν(0,)σ.We will consider the influence of 2σon TDOA ratio variance. The source is randomly placed in the cluster, once it is placed, its location is fixed. We let 2σequal to 0.1 and 0.05 respectively.

From the simulation, we gain the following observations. Firstly, Fig3 and Fig4 show that the TDOA ratio variance increases with variance 2σ. This is rational as

2

σcorresponds to the error of 2,1R and 3,1R .In

fact, if 2σincreases, 21σand 22σ will increase. From equation (7) and (8), the TDOA variance will increase and TDOA ratio difference will increase correspondingly.

Fig2 The variance span of TDOA ratio with the changing of the source location Position of the source node

T h e v a r i a n c e s p a n o f T D O A r a t i o s

In Fig3, the values -0.01 and 0.01 occurred only at most five times out of 10000 times (0.05%), and it similar to those of -0.006 and 0.006 in Fig4. Therefore, if we let the threshold used to detect a Sybil node

be 2*,(1)k k ασ=≥, and apply the equation (11) to do the detection, we will succeed. The reason is that the standard deviation covers almost 100% of the values in Fig3 and Fig4. That is, if we set 0.1α= in our simulations, it will detect the Sybil node with almost 100% successful probability. This result is also shown in Fig2. So, our solution is an efficient one.

5. Performance Analysis In this section, we will show that our solution is also a lightweight one by comparison to the Random Key Predistribution in [3]. For simplicity, we let the length of the messages and the keys be L bytes and each transmission cost E energy unit. In section 3, we see except for the normal message transmission in each session, our solution only needs two more transmissions for beacon nodes 23S and S to report the message’s source ID and arriving time to 1S . And only 1S needs to record the TDOA ratio (also let TDOA ratio be L bytes long) in each session. So the total overhead in our solution to detect a Sybil one is K (2E+1L).

By comparison, in [3], in order to detect the Sybil attack it need a key pool in the network, with the size of m keys. And each node will need to store n keys. m and n are related to the size of the network (in [3] m=20000 and n=200). The node identity is associated with the keys assigned to the node. And the network need verify part or all of the keys that an identity claims to have. So, during each verification section, the node must claim what keys it has, this claim may be knE (01k <≤).The total overhead of [3] to detect if a node is the Sybil one is KknE + (m+n) L.

From the above, we can see that our solution needs less storage and lower energy consumption. Thus, it’s a lightweight solution.

6. Conclusions and Future Work

In this paper, we have proposed an efficient and lightweight TDOA based Sybil

attack detection scheme, it includes a basic

scheme an advanced scheme to tolerate the measuring errors. And also we demonstrate the efficiency of our solution through experiments. In future work we will study

another factor that can affect the effectiveness of the advanced scheme. This factor is the accuracy of the TDOA computation model. If

this model cannot accurately model the actual

deployment, there will be extra errors (both on false positive and detection rate) in the Sybilly detection. We will extend our scheme to

Fig4 The measurement errors are normally distributed 21σ=22σ=0.05 x 10-3

Variance of difference of TDOA ratio

F r e q u e r c y

Fig3 The measurement errors are normally

distributed, 21σ=2

2σ=0.1 Variance of difference of TDOA ratio

F r e q u e r c y

tolerate existing nodes in the network. Also we will try the other location algorithms, such as AOA etc.

References

[1] Douceur J R, The Sybil attack. In: Proc. of First International workshop on Peer-to-peer systems (IPTPS' 02).7-8March 2002-Cambridge, MA, USA, V olume 2429 of LNCS 2002. 251~260

[2] Karlof C and Wagner D, Secure routing in wireless sensor networks: Attacks and counter- measures. In Proceedings of 1st IEEE International Workshop on Sensor Network Protocols and Applications, Anchorage, AK, USA, May 2003.

[3] Newsome J, Shi E, Song D, et al. The Sybil Attack in Sensor Networks: Analysis & Defenses. In: Proc. of Third Intl. Symposium on Information Processing in Sensor Networks (IPSN'04), 26-27 April 2004, USA: ACM Press.259-268.

[4] Cheng X, Thaeler A, Guoliang Xue, Dechang Chen; TPS: a time-based positioning scheme for outdoor wireless sensor networks, INFOCOM’04, 7 -

11 March, 2004, 2685 - 2696.

[5] Rahman I, and Reza. Data Fusion for Improved TOA/TDOA Position Determination in Wireless Systems. Master thesis, Virginia Polytechnic Institute and State University, July 2000

[6] Chan YT and Ho KC.A Simple and Efficient Estimator for Hyperbolic Location. IEEE Transactions

on Signal Processing, August 1994, vol. 42, no. 8, pp. 1905-1915.

[7]Rida A, Bazzi, Goran Konjevod, On the establishment of distinct identities in overlay networks, PODC’05, Las Vegas, NV, USA, 17-20 July, 2005,312

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[8] Demirbas M,Song YW,An RSSI-based Scheme for Sybil Attack Detection in Wireless Sensor Networks, International Workshop on Wireless Mobile Multimedia,WOWMOM'06,June 26-29, 2006, Niagara-Falls/Buffalo, New York, USA,564 – 570

[9]Yanchao Z,Wei L,Wenjing L,Yuguang F, Location-based compromise-tolerant security mechanisms for wireless sensor networks, IEEE Journal on Selected Areas in Communications, vol.24,No.2,247-260,Feb. 2006

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目录 前言---------------------------------------------------------------------------3 公司介绍---------------------------------------------------------------------3 项目概况---------------------------------------------------------------------4 需求分析---------------------------------------------------------------------5 无线网络设计思路---------------------------------------------------------8 售后服务承诺--------------------------------------------------------------------17 总结---------------------------------------------------------------------------------18

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