Cooperative transmission takes advantage of the broadcast nature of wireless networks, and exploits spatial diversity and multiuser diversity. However, due to its ...
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Securing Cooperative Transmission in Wireless Communications Zhu Han and Yan Lindsay Sun∗ Electrical and Computer Engineering Department, Boise State University ∗ Electrical and Computer Engineering Department, University of Rhode Island Abstract— Cooperative transmission is an emerging communication technique that takes advantages of spatial diversity and broadcast natures of wireless channels to improve wireless channel capacity. However, cooperative transmission can be vulnerable to malicious attacks, especially in its current design. In this paper, we examine whether the cooperative transmission still have performance advantages when we consider the security issues. In particular, we identify various attacks against cooperative transmission, analyze the vulnerabilities of current schemes, design a trust-assisted cooperative transmission scheme, and evaluate the proposed scheme through analysis and simulations. The proposed scheme can fix the security problems and maintain the performance advantage. It performs much better than the traditional scheme when there are malicious/greedy relays or severe channel estimation errors. In addition, we investigate the advantage of cooperative transmission in terms of defending bad mouth attacks. A recovery of link bit error rate is observed.
I. I NTRODUCTION Multiple antennas systems, such as MIMO, can create spatial diversity and therefore significantly increase wireless channel capacity. However, installation of multiple antennas on one wireless device faces many practical obstacles, such as increase in cost and size of hardware. Recently, cooperative transmission has gained considerable research attention as a transmit strategy for future wireless networks [1] [2]. In cooperative transmission, when the source node transmit a message to the destination node, some nearby nodes, which overhead this transmission, can serve as virtual antennas by transmitting replicas of the source’s message. These nodes are referred to as the relay nodes. At the destination node, multiple waveforms, received from the source nodes and relay nodes, are combined to optimize the overall link quality. Cooperative transmission takes advantage of the broadcast nature of wireless networks, and exploits spatial diversity and multiuser diversity. However, due to its broadcast and cooperative nature, cooperative transmission can suffer from various attacks. One of the most severe security problems is node compromising, in which some wireless nodes are compromised and therefore under the control of malicious parties. In cooperative transmission, the malicious nodes have chances to serve as relays. Instead of forwarding correct information, malicious relays can send arbitrary information to the destination and therefore significantly damage the system performance. Besides malicious nodes, cooperative transmission can also suffer from greedy behaviors. When the wireless nodes do not belong to the same authority, some nodes can refuse to cooperate with other nodes, i.e. not working as relay nodes, for the
purpose of saving their own resources. The greedy behavior not only degrades system performance, but also hurts regular nodes’ incentive to collaborate. To make things even more difficult to handle, the relays’ misbehavior, i.e. forwarding wrong information or not forwarding, can also result from noise and channel estimation errors. Does cooperative transmission still have performance advantage when it is under attack? In this paper, we would like to investigate this problem from various angles. What are the advantages and disadvantages of collaboration in physical layer from the security and performance points of view? Under what conditions the physical layer collaboration can improve network performance? can protocols be developed, which take advantage of cooperative transmission and fix the potential security problems? In this paper, we propose a distributed trust-assisted cooperative transmission scheme. In cooperative transmission, it is important for the destination to know the channel information of relay nodes. This channel information is usually represented by signal-to-noise ratio(SNR) or biterror-rate (BER). In this paper, however, trust values are constructed to describe the link quality. This trust not only represents channel estimation and noise factors, but also relays’ malicious/greedy behaviors. Trust values are calculated and maintained by the distributed nodes. We also develop distributed trust establishment and propagation models. With a very low overhead, the parameters of the models can propagate through complicated cooperative relaying topology from the source to the destination. In the destination, the information from both the direct transmission and relayed transmissions is combined according to the trust values. No additional channel information is needed. From the analysis and simulations, we will show that the proposed scheme can automatically recover from various attacks and perform better than maximal ratio combining used in traditional schemes. The paper is organized as follows. Section II states the related work. In Section III, cooperation transmission system model and attack model are given. In Section IV, the proposed algorithms are developed. Finally, simulation results and conclusions are given in Section V and Section VI, respectively. II. R ELATED W ORK Currently, the research on cooperative transmission focuses on improving communication efficiency. Examples of representative work are in [3]–[9]. In those studies, all
Relay 1
participating nodes are assumed to be trustworthy. However, none of above designs consider security issues in cooperative transmission. Trust establishment has been recognized as a useful tool to enhance security in applications that need cooperation among multiple parties. The research on trust establishment has been performed for various applications, including authorization and access control, electronics commerce, peerto-peer networks, routing in MANET, and data aggregation in sensor networks [10]–[15]. No existing work on trust is for cooperative transmission. In fact, no much study on this topic has been conducted for physical layer security. In this paper, we will generalize the beta-function based trust models [16] to assist secure cooperative transmission in the physical layer.
Destination 1 Source
Relay 2
x
Phase 2
yri
A. Cooperative Transmission System
p
Ps Gs,ri x + nri ,
Phase 2
Relay i
yir
xri
correlation
As shown in Figure 1, the system investigated in this paper contains a source node s, relay nodes ri and a destination nodes d. The cooperative transmission is conducted in two phases. In Phase 1, source s broadcasts a message to destination d and relay nodes ri . The received signal yd at the destination d and the received signal yri at relay ri can be expressed as p yd = Ps Gs,d x + nd , (1) yri =
y Destination combining
Phase 1
x
III. S YSTEM M ODEL AND ATTACK M ODEL
and
yd
Source
Fig. 1: System Model
Due to noise, channel degradation, and estimation errors, the relay’s output may not be the same as the source’s output. Correlation can be used to quantify the difference between x and xri . In the ideal case, the correlation should be 1. However, the correlation can be less than 1 due to various reasons. For example, the decoding from yri to xri is not accurate due to noise and channel estimation error, or the relay node i does not honestly decode yri . When only considering the decoding errors, we can derive the correlation between x and xri from (1) and (4), as "Ã !Ã !# yri − n0d yd − nd p p E(xxri ) = E Pri Gri ,d Ps Gs,d
(2)
where Ps represents the transmit power at the source, x is the transmitted information symbol with unit energy, Gs,d is the channel gain between s and d, Gs,ri is the channel gain between s and ri , nd and nri are the additive white Gaussian noises (AWGN). Without loss of generality, we assume that the noise power is the same for all the links, denoted by σ 2 . We also assume the channels are stable over each transmission frame. When there is no relays are used, the transmission only contains phase 1 and is referred to as the direct transmission. In direct transmission, without the help from relay nodes, the signal-to-noise ratio (SNR) at destination is
=
p
E(yri yd ) . Pri Gri ,d Ps Gs,d
(5)
Here n0d and nd are independent random variable since they are noises at different time slots. Notice that Ps Gs,d and Pri Gri ,d are the received power at stage 1 and stage 2, respectively. Therefore, from (1) and (2), we get
Ps Gs,d . (3) σ2 In Phase 2, relay nodes send information to the destination, and destination combine messages from the source and relays. In this paper, we examine the decode-and-forward (DF) cooperation transmission protocol [1] [2], in which the relays decode the source information received in Phase 1 and send this information to the destination in Phase 2. The received signal at the destination from relay i is p yri = Pri Gri ,d xri + n0d , (4) ΓDT s,d =
E(xxri ) = 1 − Pes,ri
(6)
where Pes,ri is the BER from the source to the relay i. Next, the destination combines the direct transmission information and relayed information together. In [1], the authors propose a combining scheme named λ-MRC, in which the combining vector [1, λ] is to minimize the resulting BER as Peλ = E(xxri )Q([1, λ]) + (1 − E(xxri ))Q([1, −λ]), (7) where Q([1, λ]) is the BER of combining assuming that the decoding at the relay is correct, and Q([1, −λ]) is the BER if the relay decodes incorrectly. Since λ need to be optimized
where xri is the decoded signal and n0d is the thermal noise with variance σ 2 . 2
One-hop cooperative transmission
The Beta function model is often used in the scenarios where the subject has collected binary opinions/observation about the node. For example, node B has transmitted (α+β− 2) packets to node A. Among them, node A received (α − 1) packets with correct CRC or (α − 1) bits with SNR greater than a certain threshold. These transmissions are considered to be successful. That is, there are (α−1) successful trails and (β − 1) failed trails. It is often assumed that the transmission of all (α + β − 2) packets are independent and a Bernoulli distribution with parameter p governs whether transmissions succeed or fail. Under these assumptions, given α and β, the parameter p follows a Beta distribution as
r1 r2 Direct transmission
s
r3
r4
d
rN
Multi-hop cooperative transmission
B(α, β) =
Fig. 2: Concatenation and Combination Propagation
Γ(α + β) α−1 p (1 − p)β−1 . Γ(α)Γ(β)
(8)
It is well known that B(α, β) has mean (m) and variance (v) as
through numerical methods, λ-MRC is difficult to implement in practice. In (6), only decoding error is counted. When the channel estimation error, mobility, and relays’ misbehavior are all considered, the correlation becomes extremely hard to analyze. This motivates us to design the self-learning scheme to determine the correlation. This scheme will be discussed in Section IV.
m=
α αβ ; v= . 2 α+β (α + β) (α + β + 1)
(9)
In the context of trust establishment, given α and β values, the trust value is often chosen as the mean of B(α, β), i.e. α α+β . This trust value represents how much a wireless link can be trusted to deliver packets correctly. In addition, some trust models introduce confidence values associated with trust values [18]. The confidence value is often calculated from the variance of B(α, β). The confidence value represents how likely the estimated trust value is accurate. Due to the physical meaning of the trust values and the close tie between trust and the Beta function, we use Beta function to represent the link quality in this paper. This is equivalent to using trust value and confidence value to describe the link quality. In the rest of paper, the terms “link quality” and “trust values” are sometimes used interchangeably.
B. Attack Models We will study the performance of cooperative transmission under several attack models. • Model 1: greedy behaviors, non-cooperative There are some greedy nodes that do not relay messages for others in order to reserve their own energy. • Model 2: inside attack, malicious relays There are malicious nodes that send garbage information to the destination when they serve as relays. Different from the jamming attack, those information can be decoded by the destination. • Model 3: attack against the proposed scheme When a malicious node needs to report link quality or trust values, the malicious node can lie and report false information. This attack is referred to as bad mouthing attack [17].
B. Link Quality Concatenation Propagation As an example of concatenation link quality (i.e. trust) propagation, we study the transmission from source s to relay r1 then to destination d. Relay r1 estimates the sourcerelay link quality as B(α1 , β1 ), and destination d estimates the relay-destination link quality as B(α2 , β2 ). Then, the question becomes how the destination estimates source-relaydestination link quality based on concatenation propagation. Let x ˆ denote the probability that transmission will success through path s − r1 − d. The cumulative distribution function of x ˆ can be written as Z Z xˆ=pq Γ(α1 + β1 )Γ(α2 + β2 ) CDF (ˆ x) = Γ(α1 )Γ(β1 )Γ(α2 )Γ(β2 ) 0 pα1 −1 q α2 −1 (1 − p)β1 −1 (1 − q)β2 −1 dpdq. (10)
IV. T RUST- BASED C OLLABORATIVE T RANSMISSION In this section, we will show how trust information can be obtained and used in cooperative transmission. A. Trust-based Representation of Link Quality Figure 2 shows two types of cooperative transmission that are currently studied separately in the literature: one-hop and multi-hop. In the one-hop case, one or multiple relays retransmit the source signal, and the destination combines information from all relays and the source. In the multi-hope case, the information is retransmitted over a chain of relays. In this paper, we analyze a more sophisticated topology that contains both cases.
Since it is very difficult to obtain the analytical solution to (10), we find a heuristic solution to approximate the distribution of x ˆ. Two assumptions are made. First, it is assumed that the distribution of x ˆ follows a Beta distribution B(α12 , β12 ). p (α1 + β1 )(α2 + β2 ). Second, we assume α12 + β12 = 3
pdf of β distributions
Cooperative Transmission
18 16
β(α ,β ) 1
1
β(α ,β ) 2
14 12
Estimate ( , ) values based on past transmissions with neighbors α
12
12
β(α1+2,β1+2)
Trust Record ( , ) based on observation ( , ) reported by other nodes β
α
β
α
record update according to time
Combine Numerical
q
10
β
2
Concatination Numerical β(α ,β )
Trust / Link Quality Manager
8 6
If destination, perform signal combination
Misbehavior Detection
Report observed BER
