nodes in cognitive radio networks (CRNs) is a promising .... station from the PU base station (assuming there is such a .... The format of the testimony is related.
Dynamic Witness Selection for Trustworthy Distributed Cooperative Sensing in Cognitive Radio Networks Han Yu1, Siyuan Liu2, Alex C. Kot2, Chunyan Miao1, Cyril Leung3 1
School of Computer Engineering, Nanyang Technological University, Singapore. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. 3 Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada. 2
Abstract-Cooperative spectrum sensing by secondary user (SU) nodes in cognitive radio networks (CRNs) is a promising approach to increase the spectrum access efficiency and overall network performance. However, unreliable sensing results or malicious behaviors from cooperator SU nodes can be very disruptive and reduce the network performance. Trust and reputation modeling has been identified as one of the potential solutions to address this problem, but the current centralized trust evaluation approach in CRN lacks scalability. Although some decentralized trust models have been proposed in CRN, without proper protection mechanisms, they are vulnerable to collusive behaviors by the witness SU nodes when they share testimonies about the trustworthiness of neighboring SU nodes. In this paper, we propose a clustering based witness selection method to address this problem. By dividing the witness SU nodes testimonies about the trustworthiness of neighboring SU nodes into clusters, the proposed method helps SU nodes to select which witness’s opinion to trust mode in the future. The proposed method has been studied using extensive computer simulation and has demonstrated good robustness against common collusive attacks.
I.
not necessarily know each other, they need to be able to adapt to the changes in behavior of other SUs when sharing their spectrum sensing results. In recent years, computational trust modeling methods have been proposed to study how a CRN can be protected from possible malicious behaviors by some SUs during cooperative spectrum sensing [4]. Radio Environment
Cooperative Spectrum Sensing
Spectrum Decision
Spectrum Analysis (Direct Trust)
Sensing Results
INTRODUCTION
Cognitive radio network (CRN) [1] is a promising technology to solve the global challenge of spectrum scarcity and inefficient spectrum usage caused by the classical fixed spectrum assignment scheme [2]. In order to achieve the envisioned opportunistic access of spectrum, the cognitive radio nodes must possess the capability of making sense of the actions of the primary users (PUs) in the network environment and adapt to it, so as to satisfy the constraints of transmitting their own packets while minimizing interference to the PUs. Spectrum sensing and spectrum state decision is the foundation that makes CRNs work. Secondary users (SUs) need to continuously gather and analyze information related to the spectrum bands of interest to identify the opportune moment to make use of these spectrum bands. It is generally agreed by researchers that cooperation among multiple SUs when sensing the spectrum conditions can improve the sensing efficiency in terms of time and energy consumed by individual nodes [3]. As a SU needs to obtain information from other SUs during the process of cooperatively sensing the spectrum conditions, the reliability and good will of these cooperating SUs heavily affect the decisions made by the initiator SU. Since the SUs do ___________________________________
Figure 1. Trust-aware Cooperative Sensing using only trust observations gathered locally (direct trust).
Computational trust modeling is essentially a way for a SU to analyze its past observations on the behaviors of other cooperating SUs in terms of the accuracy of their reported sensing results to help it make future decisions on whose sensing results to trust more. The term trustworthiness refers to the subjective expectation of a node A receiving positive outcomes (in this case, an accurate report of the current condition of a licensed spectrum band) from its interaction with a node B in a specific context [4]. Reputation is the global perception of a node’s trustworthiness in a network [4]. The generic process of trust-aware cooperative sensing in CRNs, as illustrated in Figure 1, consists of the following steps: 1) Spectrum Sensing result sharing: during cooperative sensing, SUs nodes may share with each other the sensing results in one of two forms – either the measured value concerning the characteristics of the spectrum band (e.g., signal power, wave form, etc.), or a binary local decision on whether the spectrum is currently being used by a PU. Sending binary decisions reduces
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With the advantages provided by the second-hand testimonies come new challenges. In an open network environment, SU nodes may be compromised to give out false testimonies. Collusion among SU nodes may also occur whereby a group of SU nodes share testimonies with others in such a way as to promote the group members’ reputations while tarnishing those of nodes outside the collusion ring. With an artificially elevated reputation, the collusion group can distort future cooperative sensing results to their own advantage.
communication overhead, but makes the final decisionmaking more challenging. 2) Spectrum Analysis and Decision: from available literature in trust-aware CRNs, sharing binary local decisions appears to be the prevailing method of choice. Therefore, the usual data fusion method used is the “K out of M” rule [5]. If at least K out of M received cooperative sensing results (d) support the hypothesis that the spectrum in question is currently unused, the final decision (D) is to use the spectrum for SU transmission (D = 1); otherwise, the final decision is not to attempt to transmit using the spectrum (D = 0). For a trust-aware CRN, the data fusion part is modified to take into account of the estimated trustworthiness (τ) of each cooperator SU such that: 1 1, ∑ ∑ =1 ≥ =1 = (1) 1 0, ∑ ∑ =1 <
Radio Environment
Cooperative Spectrum Sensing
Spectrum Decision
=1
Spectrum Analysis (Direct & Indirect Trust)
where τ is usually a decimal value in the range of 0 to 1 (0 means completely untrustworthy while 1 means completely trustworthy). 3) Trustworthiness Evaluation Update: the effect of a final spectrum usage decision is only partially observable. When D = 0, the SUs will not use the spectrum to transmit their packets. This action will not cause interference to the PUs. In this case, if the SUBS is involved in the decision making process, it can listen to the licensed channel, and decide if it is being used by the PU. If the SUBS detects no transmission over the licensed channel, it does not guarantee that the channel is available; if the SUBS decodes a PU transmission, it can conclude that D = 0 was the correct decision. When D = 1, if the spectrum is being used by the PU, the interference caused by the SU transmission could result in a feedback message being sent to the SU base station from the PU base station (assuming there is such a service agreement [6], [7]). In this way, the initiator SU can reward or penalize the cooperator SUs by adjusting their trustworthiness estimation (i.e., the weight of their opinions in future data fusion processes).
Sensing Results & Testimonies
The proposed Witness Selection Method Figure 2. Trust-aware Cooperative Sensing using both trust observations gathered locally (direct trust) and testimonies from other SU nodes (indirect trust), and our contribution to the process.
To deal with such misbehaviors, we propose a clustering based witness selection method to preprocess the received testimonies with the aim to identify which witness SU nodes are credible enough. The proposed method contributes primarily to the spectrum analysis process as shown in Figure 2. Experimental results have shown that the proposed method can accurately estimate the trustworthiness of a cooperator SU based on second hand (possibly biased) observations from other SUs. The rest of the paper is organized as follows: Section II discusses recent work in CRN research that are most related to our study; Section III presents the details of our proposed method; Section IV analyzes the results obtained from our experiments; Section V concludes the paper and offers possible future research directions.
Trust modeling has been demonstrated to improve the overall performance of a CRN. However, with the introduction of this mechanism into a CRN, new problems arise. Since trustworthiness estimation often involves analyzing a cooperator SU’s past spectrum sensing reports, it implies that in order to produce a reasonable trustworthiness estimation using locally gathered direct trust evidence alone, a SU has to expose itself to potentially malicious cooperator SUs for a considerable number of interactions. It is desirable to explore the use of both direct trust evidence as well as indirect trust evidence in the form of testimonies provided by witness SU nodes which have had previous cooperation experience with a particular cooperator SU node.
II. RELATED WORK In recent years, as the discussion on cognitive radio networks among researchers deepens, social computational approaches start to cross the disciplinary boundaries to be studied under the CRN environment. The autonomous nature of the SU nodes in CRNs has prompted some researchers to investigate the effect of incorporating trust modeling functions into these nodes.
III. THE PROPOSED METHOD
Qin et al. [6] appears to be the first to carry out a concrete study of making cooperative sensing of licensed spectrum bands in CRNs trust-aware. In their paper, a centralized trust management scheme for Secondary User Base Stations (SUBSs) was proposed. They incorporated the trustworthiness evaluation from a modified Beta reputation model into the “K out of M” data fusion rule to give greater weight to the opinions of more trustworthy SU nodes. A set of time-based punitive measures to deter misbehavior was also proposed to make the trust-aware cooperative spectrum sensing protocol more complete. Since it is a centralized trust model, it assumes that the SUBS is trustworthy and all the observed behavior data for each SU under its management are stored at the SUBS, providing it a global perspective. Thus, it does not have to be concerned with the problems associated with indirect trust information. Chen et al. [7] have done a comprehensive analysis of both the CRN architecture and state of the art in computational trust modeling. They have identified another area where trust modeling can help CRNs – setting up associations between SUs and Primary User Base Stations (PUBSs) based on the trustworthiness of the SUs. A modified Bayesian learning approach was proposed to enable the PUBSs to learn the trustworthiness of the SUs over multiple interactions. In their application scenario, indirect trust evidence was not considered, either. Parvin et al. [11] was one of the first to explore the use of both direct and indirect evidence to evaluate the reputation of a SU. In their paper, the SUs cooperatively sense the state of a spectrum band and send their sensing results to a central entity - the SUBS. In addition to the sensing results, each SU node needs to provide what the authors called “indirect trust” about other SUs to the SUBS, too. From the paper, it is not entirely clear how the SUs can assess the trustworthiness of other SUs. Also, in the most extreme case, each SU would provide indirect trust evidence of all other SUs in the network to the SUBS in every cooperative sensing operation. This can potentially place undue overhead on the transmission channel and limit the scalability of the proposed approach. All the testimonies received by the SUBS are used without filtering to derive the final reputation estimation for each SU, which makes the whole model vulnerable to malicious witness nodes sharing false testimonies. In our opinion, the use of indirect trustworthiness evidence has its advantages. However, their use should be constrained to decentralized trust models for distributed cooperative sensing operations. In addition, such trust models should always try to address the problem that the testimonies shared by the witness nodes may not be reliable. To the best of our knowledge, there is no published work at the moment that takes this problem into consideration.
A. System Architecture The proposed method is expected to operate in a CRN that employs distributed cooperative spectrum sensing. This paper assumes the presence of a short-range (within one hop wireless transmission range of the node) and low-bandwidth control channel over which the SU nodes exchange messages about cooperation. Cooperator SUs are invited by the cooperative sensing initiator SU to provide the following information: 1) A local decision about the state of a spectrum band: the decision made by the cooperator SU based on its own sensing result. It is a binary value representing the channel state hypothesis: H0 (denoted by the value 0, for spectrum access not possible) and H1 (denoted by the value 1, for spectrum access possible). 2) The testimony on the trustworthiness of nearby SU nodes: the SU node’s local observations of the aggregate accuracy of other SU nodes within its one hop wireless transmission range during past cooperative sensing operations. The SU node acts as a witness who testifies about the trustworthiness of other SUs it has cooperated with previously. The format of the testimony is related to the trust evaluation method we choose as the foundation for the proposed model, which will be discussed in more details in the next sub-section. With the additional testimony information, the proposed clustering based witness selection method can help the cooperative sensing initiator SU node to decide whose testimonies to trust more when unfamiliar cooperator nodes are encountered in the future. B. Trustworthiness Evaluation Method In this study, we have selected the Beta reputation system [8] as the basis of our proposed clustering based trust model. Originally proposed in the field of Multi-agent Systems research, the Beta reputation system regards the behavior of each agent as a binary event (i.e., either trustworthy or untrustworthy) modeled by the Beta distribution. Beta distribution is commonly used to represent the posterior probability of a binary event. This provides a sound mathematical ground for evaluating the trustworthiness of an agent. The Beta family of probability distribution functions (PDFs) is a set of continuous functions indexed by two parameters: α and β. In our case, α denotes the number of observed trustworthy sensing result reports, and β denotes the number of observed untrustworthy sensing result reports by a SU node. In this context, the terms trustworthy and untrustworthy represent the following situations: 1)
A SU node ni’s sensing result report is considered trustworthy if the final decision is not to use the spectrum (D = 0) and ni’s advice is also not to use the spectrum (di = 0); or if the final decision is to use the spectrum (D = 1), and ni’s advice is also
to use the spectrum (di = 1), and there is no complaint from the PUBS about interference caused by the SU using the channel. 2)
1: Normalize the rating vectors in the testimonies. 2: Initialize each normalized rating vector as a unique cluster. 3: Repeat 4: Merge two clusters with the shortest 2-norm distance together. 5: Until the predefined number of clusters appear. 6: Repeat 7: d = the smallest of the furthest distances between clusters C1 and C2. 8: if (C1 or C2 is a bordering cluster) and (d < d1) then 10: Merge C1 and C2 11: end-if 12: if !(C1 or C2 is a bordering cluster) and (d < d2) then 13: Merge C1 and C2 14: end-if 15: Until ∀ ≥ 1 and ∀ ≥ 2 .
Otherwise, ni’s sensing result report is considered untrustworthy.
The expected value E(X) of the Beta reputation system is calculated
by using the formula: , which is an intuitive candidate to evaluate
+
the overall trustworthiness of a SU node. In the Beta reputation model, in order to reduce the gyration of the trustworthiness evaluation when the values of α and β are very low, the technique of Laplace Smoothing [9] is applied. Therefore, a SU node ni’s trustworthiness (τi) estimated using the Beta reputation system is denoted as:
+1 = (2) ( +1)+( +1)
In our study, each SU node keeps a table of [αi, βi] vectors all the cooperators ni it has previously interacted with. From such a table, evidence for a known cooperator node can be extracted and shared with other SU nodes in the form of < , , [ , ] > (i represents the identity of the potential cooperator SU, and is the identity of the witness SU) to facilitate the formation of a reputation estimation about a particular SU node by nodes which have not cooperated with it before. With this additional source of information, the reputation evaluation for a cooperator SU node (ni) at the cooperative sensing initiator SU node is: =
+1
+ +2
+
(1− )
∑ =1
+1
+ +2
Algorithm 1. The proposed clustering based witness selection method.
However, if Q is preset to a large value, then some similar vectors might not be clustered together in the first stage. Therefore, we need the second clustering stage, in which the merging process continues until it stops at a stage where a different set of merging criteria is met. The clustering approach first calculates the furthest distance between any two clusters resulting from the first stage of clustering. Then, it merges together the two clusters with the minimum furthest distance if the furthest distance of the two clusters is smaller than the predefined distance threshold (d1 or d2) as shown in Algorithm 1. The merging process continues until no furthest distance between any two clusters is smaller than the distance threshold. The aim of this stage is to continue merging rating vectors with similarity together but ensure that the rating vectors with obvious difference are not merged by controlling the distance threshold value. In our case, after normalizing the values of α and β in the vector to the range of [0, 1], if either of them is close to 1 (e.g., α = 0.95), the normalized vector is considered to be a bordering vector; a cluster with at least one bordering vector is regarded as a bordering cluster. The threshold values d1 and d2 need to be set empirically after observing the performance of the proposed method under the target network environment for a trial period. d1 should be set more strictly than d2 since bordering vectors are more likely to contain unfair testimonies. Suppose the testimony from one SU node nj regarding the target SU node ni is a two column vector: [ , ] (e.g., [4, 6]), where is the number of trustworthy sensing reports received from ni and is the number of untrustworthy sensing reports received from ni. Before conducting clustering, the testimony vectors are normalized first by using + to divide and respectively (e.g., [4, 6] after normalization becomes [0.4, 0.6]). Assuming that each normalized testimony vector represents the coordinates of a point in the 2dimensional space, the proposed clustering-based method is
(3)
where ∈ [0,1] denotes the weight to be placed on the direct trust evidence about ni and the (1 − ) represents the weight to be placed on the indirect trust evidence about ni (in the case where direct trust evidence is not available, is set to 0); the local evidence about ni is represented by αi and βi; and represent the testimony provided by SU node nj about ni; N is the total number of witness SU nodes which provide their testimonies about ni. C. Clustering-based Witness Selection Method The proposed witness selection method is based on the twostage clustering approach [10]. The two-stage clustering approach works as follows. The clustering is performed by the cooperative sensing initiator SU node on the reports (which contain the rating vectors) from the witness SU nodes about all its neighboring SU nodes. The data points for the clustering operation are the rating vectors from witness testimonies. In the first stage, the clustering approach divides the vectors into a predefined number (Q) of clusters. The vector from each witness node is initially regarded as a cluster by itself, and two clusters with the shortest 2-norm distance are merged together to form a new cluster. Then, two clusters with the shortest 2-norm distance are selected from the resulting clusters from the previous round of clustering. The process is repeated until the predefined Q clusters are left. The primary aim of this stage is to merge the vectors with the closest similarity together.
simulation enables the SU nodes to build up observations on the behavior of other SU nodes and form a basis for their trust evaluation.
applied to produce the clustering result. By following the majority rule, we consider the cluster which includes the greatest number of points as the trustworthy cluster. And the SU nodes whose normalized vectors are included in the trustworthy cluster are considered as trustworthy witness SU nodes. The testimonies provided by these witness SU nodes are then used to calculate ni’s reputation. In this way, Equation (3) is reformulated as follows: =
+1
+ +2
+
(1− ) ′
∑
′
+1 ′ ′ =1 ′ ′
+ +2
After 100 cooperative sensing operations, the 101st node joins the group. From then on, it will take the role of the initiator SU. The 100 nodes will provide the new node their sensing results and testimonies for other nodes. As shown in Figure 3, currently, there are three types of collusion attacks on trust management systems that have been well recognized [12].
(4)
1) Badmouthing: where the false evidence causes the evaluated trustworthiness of a SU node to decrease. 2) Ballot-stuffing: where the false evidence causes the evaluated trustworthiness of a SU node to increase. 3) White washing: where a node with low reputation reenters the system with a new identity and a clean slate default reputation so that it can misbehave again.
′
′
and where is the number of honest witness SUs, and ′ represent the testimony provided by a trustworthy witness SU node nj’ about ni. IV. EXPERIMENTAL EVALUATIONS In our experiments, computer-based simulations were designed to verify the effectiveness of the proposed clustering based witness selection method against different misbehaviors by the witness SU nodes when sharing testimonies.
In this experiment, we study the proposed approach under the badmouthing and ballot-stuffing attack scenarios which the proposed approach is designed to counteract. Assuming that badmouthing and ballot-stuffing SU nodes attack all other nodes in every cooperative sensing operation, the simulations were conducted for 100 more steps after the 101st node joins the group by varying the proportion of attacker nodes in the population. We compare the expected reputation (the ground truth in our simulations), reputation after clustering and reputation without clustering (evaluated using the trust model in Section III.B).
D. Simulation Setup
Collusion Attacks
Positive Reputation Bias
Reputation Reset
Negative Reputation Bias
Ballot Stuffing Attack
White Washing Attack
Badmouthing Attack
E. Analysis of Results Based on the results from the simulations, we have plotted the evaluated reputation values using different approaches for each SU node from the population after each round of simulation. As shown in Figure 4, the SU node ni shares its true spectrum sensing results with other nodes with only about a 30% probability. Under ballot-stuffing attack scenarios, the attacker nodes attempt to distort other nodes’ evaluation of ni’s reputation by reporting an increased number of trustworthy interactions in their testimonies. As the percentage of ballotstuffing SU nodes in the population increase from 0% to 45%, it can be observed that the reputation evaluation model without the proposed witness selection method produced increasingly positively distorted reputation evaluations for ni. However, the reputation evaluation model equipped with the proposed witness selection method is able to estimate the reputation of ni close to the ground truth.
Figure 3. The most common types of collusion attacks on trust models.
The simulation consists of 101 SU nodes forming a small decentralized cooperative spectrum sensing group to try to transmit over 1 licensed channel. The state of the licensed channel changes between in-use and not in-use in each step of the simulation randomly. One node plays the role of a newly joint node. For the first 1000 simulation steps, 100 cooperative sensing operations (using the trust evaluation method in Section III.B) among the remaining 100 SU nodes were simulated. In each round of simulation, the 100 SU nodes have equal probability of being the cooperative sensing initiator. The probability that a SU node shares incorrect spectrum sensing decisions with others follows a uniform distribution between 0 and 1. The 100 nodes are assigned with random probability values for sharing their sensing decisions truthfully for each round of simulation, and the value is assumed to remain unchanged until the end of that round. This stage of the
The results for badmouthing attacks are illustrated in Figure 5. The SU node ni shares its true spectrum sensing results with other nodes with about 40% probability. Under badmouthing attack scenarios, the attacker nodes attempt to distort other nodes’ evaluation of ni’s reputation by reporting an increased number of untrustworthy interactions in their testimonies. As the percentage of badmouthing SU nodes in the population increase from 0% to 45%, it can be observed that the reputation evaluation model without the proposed witness selection
is applicable for situations where the majority of the nodes in a group are not compromised.
method produced increasingly negatively distorted reputation evaluations for ni. However, the reputation evaluation model equipped with the proposed witness selection method is able to estimate the reputation of ni close to the ground truth.
V. CONCLUSIONS AND FUTURE WORK In this paper, we proposed a clustering based witness selection method that enables the existing trust-aware CRNs to make use of indirect trust evidence more safely. To the best of our knowledge, the proposed method is one of the first attempts to address this problem in CRNs, and can potentially help the propagation of trust information in CRNs to reduce the vulnerability of SU nodes with few direct trust evidence to rely on. Experimental results have demonstrated the robustness of the method in counteracting the adverse effects of two of the most common types of witness misbehaviors. In subsequent research, the proposed method will be studied under more complex attack scenarios in large CRNs. Machine learning based approaches will be investigated to enable the proposed method to dynamically adapt the values of key parameters based on the conditions of the network environment so as to reduce the need for human intervention. ACKNOWLEDGMENT This research is partially supported by the Singapore Millennium Foundation (SMF).
Figure 4. The performance of the proposed clustering-based witness selection method against ballot-stuffing attack (vertical axis: reputation value; horizontal axis: percentage of ballot-stuffing SU nodes in the network).
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Figure 5. The performance of the proposed clustering-based witness selection method against badmouthing attack (vertical axis: reputation value; horizontal axis: percentage of badmouthing SU nodes in the network).
The simulations were only carried out for the cases where the proportion of the attacker nodes is less than 50% of the total population in a cooperative spectrum sensing group. As the proposed witness selection method assumes the cluster with the largest size to be the most trustworthy cluster, the method
