Secure Data Collection for Wireless Sensor Networks

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1 Secure Data Collection for Wireless Sensor Networks Haengrae Cho 1 and Soo-Young Suck 2 1 Department of Computer Engineering, Yeungnam University, Republic of Korea 2 Department of R&D, Gyeongbuk Institute of IT Convergence Industry Technology, Republic of Korea Abstract Wireless sensor networks (WSNs) are used to collect various data in environment monitoring applications. The most comprehensive way of data collection is to make every sensor node report periodically its sensing data to a base node. To reduce the energy consumption due to excessive communication, the network is partitioned into a set of spatial clusters with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their sensing data to the base node. The base node may predict the missed data of non-samplers using the spatial correlation between sensor nodes. The spatial clustering is vulnerable to internal security threat such as node compromise. If the samplers are compromised and report incorrect data intentionally, then the WSN should be contaminated rapidly due to the process of data prediction at the base node. In this paper, we propose intrusion detection algorithms for secure data collection in the WSN. The algorithms consist of cluster head monitoring and member node monitoring to cope with security attacks where either a cluster head or member nodes are compromised. Keywords: Wireless Sensor Network, Data Collection, Spatial Clustering, Security, Attack Detection 1. Introduction A wireless sensor networks (WSN) typically consist of a large number of small battery-powered sensor nodes. In order to sustain sensor nodes to run for a long period, it is critical to save energy in sensor operations [1]. The primary functions of WSN are to collect data for observation and analysis of physical phenomena. There are two types of data collection in WSN: event-based and periodic approach [5]. In event-based data collection, sensor nodes are responsible for detecting and reporting (to a base node) events such as spotting moving targets. They perform local filtering and sometimes collaborate with each other to detect events. On the other hand, in periodic data collection, every node reports periodically its sensing data to the base node. Many researches prefer the periodic approach because it enables arbitrary data analysis at the base node [3], [5]. Extracting the vast amounts of data generated by largescale, high-density WSN can cause a wide range of problems. Sensing always and transmitting every data would cause sensor nodes to drain their batteries soon. Furthermore, the limited communication bandwidth prevents all the acquired data from being propagated successfully toward the base node. This means that we need an energy-efficient way of data collection to prolong the lifetime of WSN by keeping the energy consumption at minimum. Spatial clustering is a representative way of saving energy in periodic data collection [6], [9]. It partitions the network into a set of clusters where a cluster includes sensor nodes with similar sensing data. For each cluster, only a few sensor nodes (samplers) report their data to the base node. All the rest of sensor nodes can save their energy by keeping in sleep mode. The base node may predict the missed data using the spatial correlation between sensor nodes. To balance the energy consumption, sensor nodes within a cluster can share the workload equally. The WSN is vulnerable to security threats both external and internal due to unreliable wireless channels, unattended operation of sensor nodes, and resource constraint [2], [12]. Node compromise is a major type of internal attacks. Compromised sensor nodes release all the security information to the adversary. Then, the adversary can easily launch internal attacks with data alteration, message negligence, selective forwarding, and jamming [4], [8], [10]. Note that the node compromise is especially problematic for periodic data collection applications, where only the samplers may report data to the base node. If the samplers are compromised and report incorrect data intentionally, then the WSN should be contaminated rapidly due to the process of missing data prediction at the base node. This means that detecting and defending against node compromise are inevitable tasks to guarantee the correctness of data collection at WSN. In this paper, we propose intrusion detection algorithms for secure data collection in the WSN. The algorithms consist of cluster head monitoring and sampler monitoring to cope with internal security attacks where either a cluster head or member nodes are compromised. In the cluster head monitoring, neighbor nodes of a cluster head collaboratively monitor their cluster head. The member node monitoring algorithm is divided into two variants: monitoring by neighbors (MBN) and monitoring by cluster head (MBCH). They are based on spatial clustering and try to detect compromised nodes with energy-efficient manner. The MBN explores the spatial correlation with a sampler and its neighbors. Neighbors have a role to watchdog. They listen promiscuously to the sampler s broadcasting transmission and monitor whether the sampler is compromised or not. The MBCH does not impose any additional roles to sensor nodes for security. Instead, the cluster head has to monitor the transmitted data

2 Base Node Cluster 1 Cluster k CH Sensor node Data collection tree Cluster connection tree Connectivity Fig. 1: Model of a WSN of samplers and compares them with the sensor readings of non-sampler nodes. The rest of this paper is organized as follows. Section 2 presents the related work. Section 3 describes our model of the WSN and the data collection. Section 4 describes the proposed algorithms in detail and discusses some performance issues. Finally, Section 5 concludes this paper. 2. Related Work Most of previous intrusion detection algorithms proposed for the WSN did not consider the underlying data collection architectures [4], [8], [10]. The two exceptions are [7] and [11]. Authors of [7] partition the WSN into several δ-groups by extending the distributed spatial clustering algorithm [9]. Sensor nodes within the same group are physically close to each other and their sensed data are dissimilar by at most δ. To detect compromised nodes, they partition the δ-group into equal-sized sub-groups. Each sub-group monitors the entire δ-group in turn to reduce the total power consumption. However, they did not consider the location information of monitoring nodes and reporting nodes. If a monitoring node is not located in the routing path between a reporting node and the base node, it cannot detect whether the reporting node is compromised or not. Furthermore, there is no central decision point and thus every node in the cluster has to decide the node compromise if alert messages are broadcast from the monitoring nodes. Authors of [11] proposed a collaboration-based intrusion detection algorithm to detect and revoke compromised nodes in a cluster through less energy consumption. Similar to this paper, they propose separate algorithms for cluster head monitoring and member node monitoring. To monitor cluster head, all member nodes of a cluster are divided into several groups. Then every monitor group takes turns to monitor the cluster head in a cluster round time. The cluster head monitors its member nodes and is responsible to detect the compromised member nodes. Note that this is contrary to [7] where every node has to decide the node compromise. The main problem of [11] is that they did not present how to detect the misbehavior of each sensor node. This should be performed on the basis of several monitoring attributes about sensed data and communication behaviors [7], [8], [10]. Furthermore, for cluster head monitoring, they did not consider the location information of monitoring nodes and the cluster head. This should cause to decrease the detection accuracy. Unlike previous cluster-based intrusion detection algorithms, our algorithm is completely integrated into the underlying data collection architecture. Our algorithm considers the WSN structure, cluster formation, and data collection procedures. This enables our algorithms to optimize energy consumption for message transmission and sampling. 3. Model of WSN and Data Collection Figure 1 shows our model of a WSN. The WSN follows layered network architectures [5]. Let S = {s 1,..., s n } be a set of all sensor nodes in the WSN. A data collection tree is used to propagate the sensed data of each node to the base node. The base node is the root of the data collection tree. The WSN is partitioned into disjoint clusters, where a sensor node is selected as a cluster head (CH). A cluster connection tree is used to establish the communication between the CH and the other nodes in the cluster. Each sensor node is assumed to be able to communicate only with its neighbors. The set of neighbor nodes of sensor node s i is denoted by nbr(s i ). The nodes that can communicate with each other form a connectivity graph. s i is forced to sample periodically, and let D[s i ] be a vector of sampled readings of s i and L be its length. The correlation of two sensor nodes s i and s j are defined as [5]: Corr(s i, s j ) = (D[s i] E[D[s i ]]) (D[s j ] E[D[s j ]]) T L Var(D[s i ]) Var(D[s j ]) (1) Definition 1. Two sensor nodes, s i and s j, are strongly correlated if 1 Corr(s i, s j ) δ, where 0 δ 1.

3 Definition 2. A set of sensor nodes C is called a cluster it the following two conditions hold for every pair of s i C and s j C: (1) s i can communicate with s j directly or via any nodes in C, and (2) s i and s j are strongly correlated. The construction of clusters based on spatial correlation is an interesting research issue and has been studied by many researchers [3], [5], [6], [9]. In this paper, we assume that the WSN is already partitioned into several clusters as Figure 1 shows. For each cluster, the CH maintains the correlation information for every pair of sensor nodes in the cluster. When a new cluster is established, the CH sends a data mean vector (E[D[s i ]]) and a data covariance matrix (Corr(s i, s j ) Var(D[si ]) Var(D[s j ])) for every pair of sensor nodes s i and s j to the base node. Each sensor node in the cluster periodically samples its sensor data and sends it to the CH. We call the period of sampling as a forced-sampling period (τ f ). The CH evaluates the degree of correlation periodically and starts a new cluster construction phase if sensor nodes in the cluster are not strongly correlated anymore. Each node in a cluster becomes a sampler with a probability λ. Combining this randomized scheduling with the round robin scheduling, we can guarantee that at least one node becomes a sampler for each cluster. A sampler sends its sensing data to the base node for every τ d through the data collection tree. Then the base node can predict the sensing data of non-sampler nodes with the data mean vector and the data covariance matrix [5]. We call τ d as a data-sampling period. Note that we can save energy significantly by setting τ f to be much longer than τ d. 4. Secure Data Collection Algorithms In this section, we first present a cluster head monitoring algorithm. Then we describe two sampler monitoring algorithms, monitoring by neighbors (MBN) and monitoring by cluster head (MBCH). Finally, we analyze our algorithms qualitatively. 4.1 Cluster Head Monitoring In periodic data collection framework, the CH has a primary role and thus its security demand is higher than that of the other sensor nodes. The role of the CH can be summarized as follows. The CH constructs a cluster that consists of sensor nodes which are strongly correlated to that of the CH. After constructing the cluster, the CH sends a data mean vector (E[D[s i ]]) and a data covariance matrix (Corr(s i, s j ) Var(D[s i ]) Var(D[s j ])) for every pair of sensor nodes s i and s j to the base node. The base node will use them to derive the parameters of the probabilistic models used in predicting the values of non-sampler nodes. For each forced-sampling period, the CH evaluates the degree of correlation periodically and starts a new cluster construction phase if sensor nodes in the cluster are not strongly correlated anymore. The CH is responsible to detect compromised nodes in its cluster. The first three roles of the CH correspond to the cluster maintenance. Detecting the misbehavior of the CH at the cluster maintenance is a challenge, since most decisions of the CH comes from the raw information of member nodes such as the history of sensor readings. This cannot be done simply by neighbor based detection of communication behavior [8], [10]. A potential solution is to assign several CHs in a cluster and to make them monitor with each other. However, member nodes must suffer from heavy communication overhead to report their sensor readings to every CH. We let this issue as a future work of this paper. In this paper, we concentrate the last role of the CH. Some sensor nodes will report the monitoring information to the CH using the member node monitoring algorithm of Section 4.2. Then the CH checks the information and announces to every member node in the cluster when some sampler is compromised. This decision could be incorrect if the CH itself is compromised. Suppose that nbr(ch) is the set of neighbor nodes of the CH. Then each sensor node s i in nbr(ch) executes the following steps. 1) For each data-sampling period, s i overhears the monitoring information sent to the CH. It delivers the information to every other node in nbr(ch). 2) s i also overhears the decision message sent from the CH. If the decision is different from that of itself, it decides that the CH is compromised and announces its decision to every other node in nbr(ch). 3) If more than a predefined percentage of sensor nodes in nbr(ch) decide that the CH is compromised, one of them reports it to the base node. Then the base node segregates the CH from the WSN by broadcasting the decision to every node and assigns a new CH for the cluster. Note that neighbor nodes of the CH would drain its battery rapidly due to the monitoring task. The primary source of energy consumption is to make consensus among neighbor nodes. We can reduce the energy consumption by considering the type of false alarm. For examples, if we allow false negative errors where a correct node is determined as compromised, neighbor nodes need not exchange their decisions when the CH announces that some node is compromised. Furthermore, most data collection algorithms select new CH periodically to prolong the lifetime of the WSN. This means that the energy consumption of neighbor nodes can also be distributed to other nodes.

4 4.2 Sampler Monitoring The CH has a role to decide whether a sampler is compromised or not. For each data-sampling period, samplers report their sensor readings to the base node through the data collection tree. If the CH does not locate on the routing path from the sampler to the base node, it cannot detect the compromised sampler. Hence, we modify the data collection process by reporting the monitoring information to the CH. In this paper, we propose two sampler monitoring algorithms. The algorithms have different strategies to select monitoring nodes that report to the CH and to define monitoring information to be reported Monitoring by Neighbors (MBN) In the MBN, every neighbor node of a sampler has a role to a watchdog that monitors the message sent from the sampler. The following modifications of data collection procedures are required for the MBN to determine whether a sampler is compromised or not. The CH has complete location information for every sensor node in its cluster to identify neighbor nodes of the sampler. Each sensor node has a correlation vector to every other sensor node in the cluster. Suppose that a sensor node s s is selected as a sampler. s s notifies itself to the CH 1, and the CH wakes up the sensor nodes in nbr(s s ). For each node s i nbr(s s ), it reads its sensing data for every τ d. Then s i overhears the message from s s no matter whether or not s i is involved in the communication. If the sensor reading sent from s s is not strongly correlated to that of s i, s i reports to the CH. If more than half sensor nodes of nbr(s s ) report to the CH, the CH decides that s s is compromised and reports it to the base node. After that, the base node ignores the data sent from s s. Each sensor node also excludes the compromised node in selecting the next-hop forwarder to realize the secure routing Monitoring by Cluster Head (MBCH) In the MBCH, the CH has a role to detect the compromised node. The following modifications of data collection procedures are required for the MBCH to determine whether a sampler is compromised or not. For each cluster, at least two samplers should be selected for every data-sampling period. Samplers are required to send its sensor readings to the CH. In the MBCH, the CH performs the detection of compromised node by two ways: (1) comparison between samplers 1 To force this procedure to compromised node, the CH may assign some unique id to the sampler in response to the notification. The base node should reject a message from the sampler if it does not contain the id. and (2) comparison between sampler and non-samplers. Suppose that two sensor nodes, s i and s j, are selected as samplers of a cluster. For every data-sampling period, s i and s j send their sensor readings to the CH. The CH then forwards the message to the base node only if they are strongly correlated. Otherwise, one of samplers could be compromised. In this case, the CH does not forward the message and waits until the next forced-sampling period to collect every sampling data and to determine the compromised node. Note that two samplers are not enough if both of them could be compromised. To check if such condition happens, the CH compares the validity of samplers with sensor readings of every non-sampler node for some forced-sampling period. If the samplers are compromised, the CH reports them to the base node. Then the base node invalidates previous sensing data sent from the compromised samplers. If we increase the number of samplers, the WSN should be more secure at the cost of increased energy consumption. This shows an interesting tradeoff between energy consumption and security enforcement. 4.3 Qualitative Analysis The performance of MBN and MBCH depends on several factors of WSN, such as network density, capacity of sensor nodes, sampling cost, and so on. In this section, we analyze the pros and cons of two algorithms for each factor. Network density: The MBN is effective only if there are sufficient neighbor nodes for each sampler. This is because the majority vote is performed to determine if the sampler is compromised. If the number of neighbor nodes is not enough, the MBN may be exposed to the unsafe case when both the sampler and its neighbor nodes are compromised at the same time. On the other hand, the MBCH is less dependent on the network density due to the additional step of comparison at the forced-sampling period. Note that the MBN does not support the comparison at the forced-sampling period, since the sampler is not required to send its sensor readings to the CH. Capacity of sensor nodes: The MBN requires that every sensor node can store correlation information to every other sensor node in the cluster. Furthermore, neighbor nodes of a sampler has to (1) sample at each datasampling period, (2) overhear the message sent from the sampler, (3) calculates the correlation between itself and the sampler, and (4) report to the CH in case of correlation mismatch. This means that the MBN spends a lot of memory resources and computing resources of sensor nodes. On the other hand, the MBCH does not spend any additional memory space of sensor nodes for security enforcement. It just requires more samplers. However, since the CH detects the node compromise for itself, the CH can drain its energy more rapidly. This

5 means that the MBCH depends on the CH replacement algorithm to prolong the lifetime of WSN. Sampling cost: If the sampling cost is not expensive, the overhead of the MBN to make neighbor nodes sample at every data-sampling period is not significant. In this case, maintaining additional samplers at the MBCH may take much overhead due to communication cost of the samplers. Note that the message overhearing at promiscuous mode of the MBN spends much less energy compared with the message transmission at samplers. However, if the sampling cost is high, sensor nodes of the MBN would spend more energy especially for the dense network. Detection time of compromised node: The MBN can detect the comprised node as soon as the majority of neighbor nodes vote. On the other hand, the MBCH cannot determine the compromised node promptly until the next forced-sampling period. Note that the MBCH can reduce the delay if it operates many samplers and applies majority vote between them. However, many samplers should result in increased energy consumption and network traffic. [5] B. Gedik, L. Liu, and P. S. Yu, ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 18, pp , [6] T. D. Le, N. D. Pham, and H. Choo, Towards a Distributed Clustering Scheme based on Spatial Correlation in WSNs," in Proc International Wireless Communications and Mobile Computing Conference (IWCMC 2008). [7] G. Li, J. He, and Y. Fu, Group-based Intrusion Detection System in Wireless Sensor Networks," Computer Communications, vol. 31, pp , [8] F. Liu, X. Cheng, and D. Chen, Insider Attacker Detection in Wireless Sensor Networks," in Proc. IEEE INFOCOM 2007, pp [9] A. Meka and A. K. Singh, Distributed Spatial Clustering in Sensor Networks," in Proc. 10th International Conference on Extending Database Technology (EDBT 2006). [10] A. Stetsko, L. Folkman, and V. Matya, Neighbor-based Intrusion Detection for Wireless Sensor Networks," in Proc th International Conference on Wireless and Mobile Communications (ICWMC 2010) pp [11] W-T. Su, K-M. Chang, and Y-H. Kuo, ehip: An Energy-Efficient Hybrid Intrusion Prohibition System for Cluster-based Wireless Sensor Networks," Computer Networks, vol. 51, pp , [12] M. Xie, S. Han, B. Tian, and S. Parvin, Anomaly Detection in Wireless Sensor Networks: A Survey," Journal of Network and Computer Applications, vol. 34, pp , Conclusions In this paper, we propose intrusion detection algorithms for secure data collection in the WSN. The proposed algorithms are composed of cluster head monitoring algorithm and sampler monitoring algorithm. The sampler monitoring algorithm is also composed of two sub-algorithms, monitoring by neighbors (MBN) and monitoring by cluster head (MBCH). They are based on spatial clustering and try to detect compromised nodes with energy-efficient manner. Unlike previous security algorithms for WSN, our algorithms consider the underlying data collection architectures. The security task is completely integrated to data collection algorithm. This enables our algorithms to optimize energy consumption for message transmission and sampling. We are investigating quantitative analysis of the secure data collection using our simulation model. The simulation model is developed with CSIM package and implements variety of WSN configurations and workloads. References [1] G. Anastasi, M. Conti, M. D. Francesco, and A. Passarella, Energy Conservation in Wireless Sensor Networks: A Survey," Ad Hoc Networks, vol. 7, pp , [2] X. Chen, K. Makki, K. Yen, and N. Pissinou, Sensor Network Security," IEEE Communications Surveys & Tutorials, vol. 11, pp , [3] H. Cho, Distributed Multidimensional Clustering based on Spatial Correlation in Wireless Sensor Networks," Computer Systems Science and Engineering, vol. 26, pp , [4] X. Du, Detection of Compromised Sensor Nodes in Heterogeneous Sensor Networks," in Proc International Conference on Communications (ICC 2008), pp

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