- Research Article
- Open Access
A Secure Localization Approach against Wormhole Attacks Using Distance Consistency
© Honglong Chen et al. 2010
- Received: 1 September 2009
- Accepted: 21 September 2009
- Published: 11 November 2009
Wormhole attacks can negatively affect the localization in wireless sensor networks. A typical wormhole attack can be launched by two colluding attackers, one of which sniffs packets at one point in the network and tunnels them through a wired or wireless link to another point, and the other relays them within its vicinity. In this paper, we investigate the impact of the wormhole attack on the localization and propose a novel distance-consistency-based secure localization scheme against wormhole attacks, which includes three phases of wormhole attack detection, valid locators identification and self-localization. The theoretical model is further formulated to analyze the proposed secure localization scheme. The simulation results validate the theoretical results and also demonstrate the effectiveness of our proposed scheme.
- Wireless Sensor Network
- Packet Loss
- Neighboring Locator
- Receive Signal Strength Indicator
- Malicious Node
Wireless sensor networks (WSNs)  consist of a large amount of sensor nodes which cooperate among themselves by wireless communications to solve problems in fields such as emergency response systems, military field operations, and environment monitoring systems. Nodal localization is one of the key techniques in WSNs. Most of current localization algorithms estimate the positions of location-unknown nodes based on the position information of a set of nodes (locators) and the internode measurements such as distance measurements or hop counts. Localization in WSNs has drawn growing attention from the researchers, and comprehensive approaches [2–6] are proposed. However, most of the localization systems are vulnerable under the hostile environment where malicious attacks, such as the replay attack or compromise attack , can disturb the localization procedure. Security, therefore, becomes a significant concern of the localization process in hostile environment.
The wormhole attack is a typical kind of secure attacks in WSNs. It is launched by two colluding external attackers  which do not authenticate themselves as legitimate nodes to the network. When starting a wormhole attack, one attacker overhears packets at one point in the network, tunnels these packets through the wormhole link to another point in the network, and the other attacker broadcasts the packets among its neighborhood nodes. This can cause severe malfunctions on the routing and localization procedures in WSNs. Khabbazian et al.  point out how the wormhole attack impacts on building the shortest path in routing protocols. For the localization procedure under wormhole attacks, some range-free approaches [9, 10] have been proposed. We will propose a range-based secure localization scheme under wormhole attacks in this paper.
To prevent the effect of wormhole attack on the range-based localization, we propose a distance-consistency-based secure localization scheme including three phases: wormhole attack detection, valid locators identification and self-localization. The wormhole attack detection is designed to detect different types of wormhole attacks. For the valid locators identification, different identification schemes are proposed under different wormhole attacks. Both basic approach and enhanced approach are devised using these identification schemes. We formulate the theoretical model to analyze the probability of detecting wormhole attacks and the probability of successfully identifying all valid locators. Simulation results show the effectiveness of our proposed scheme and validate the theoretical results.
As a summary, this paper makes the following contributions:
a novel wormhole attack detection scheme is proposed to detect the existence of a wormhole attack and to further determine the type of the wormhole attack;
a basic identification approach is designed to identify the valid locators for the sensor. Two independent algorithms are proposed to handle different wormhole attacks;
an enhanced identification approach is developed which achieves better performances than the basic approach;
theoretical analysis on the probability of detecting wormhole attacks and the probability of successfully identifying all valid locators are conducted and verified by simulations.
simulations are conducted to further demonstrate the effectiveness of the proposed secure localization schemes.
The remainder of this paper is organized as follows. In Section 2, we discuss the related work on the secure localization. Section 3 describe the network model and the attack model of the system. The secure localization scheme is proposed in Section 4. Section 5 gives the theoretical analysis and Section 6 presents the simulation results. Section 7 concludes the paper and outlines our future work.
To resist the compromise attack, Liu et al.  propose the range-based and range-free secure localization schemes, respectively. For the range-based scheme, a Minimum Mean Square Estimation method is used to filter out inconsistent beacon signals. For the range-free scheme, the nodes adopt the voting-based location estimation which can ignore the minor votes imposed by the malicious nodes. SPINE  utilizes the verifiable multilateration and verification of positions of mobile devices into the secure localization in the hostile network. The mechanism in  introduces a set of covert base stations (CBS), whose positions are unknown to the attackers, to check the validity of the nodes. ROPE  is a robust positioning system with a location verification mechanism that verifies the location claims of the sensors before data collection. A suit of techniques in  are introduced to detect malicious beacons which can negatively affect the localization of nodes by providing incorrect information. TSCD  proposes a novel secure localization approach to defend against the distance-consistent spoofing attack using the consistency check on the distance measurements.
To detect the existence of wormhole attacks, researchers propose some wormhole attack detection approaches. In , packet leashes based on the notions of geographical and temporal leashes are proposed to detect the wormhole attack. Wang and Bhargava  detect the wormhole attack by means of visualizing the anomalies introduced by incorrect distance measurements between two nodes caused by the wormhole attack. Reference  further extends the method in  for large scale network by selecting some feature points to reduce the overlapping issue and preserving the major topology features. In , a detection scheme is elaborated by checking whether the maximum number of independent neighbors of two nonneighbor nodes is larger than the threshold.
To achieve secure localization in a WSN suffered from wormhole attacks, SeRLoc  first detects the wormhole attack based on the sector uniqueness property and communication range violation property using directional antennas, then filters out the attacked locators. HiRLoc  further utilizes antenna rotations and multiple transmit power levels to improve the localization resolution. The schemes in  can also be applied into the localization against wormhole attacks. However, SeRLoc and HiRLoc need extra hardware such as directional antennae, and cannot obtain satisfied localization performance in that some attacked locators may still be undetected. Reference  requires a large amount of computation and possibly becomes incompetent when malicious locators are more than the legitimate ones. In , Chen et al. propose to make each locator build a conflicting-set and then the sensor can use all conflicting sets of its neighboring locators to filter out incorrect distance measurements of its neighboring locators. The limitation of the scheme is that it only works properly when the system has no packet loss. As the attackers may drop the packets purposely, the packet loss is inevitable when the system is under a wormhole attack. Compared to the scheme in , the distance-consistency-based secure localization scheme proposed in this paper can obtain high localization performance when the system has certain packet losses. Furthermore, it works well even when the malicious locators are more than the legitimate ones, which causes the malfunction of the scheme in .
In this section, we build the network model and the attack model, describe the related definitions, and analyze the effect of the wormhole attack on the range-based localization, after which we classify the locators into three categories.
3.1. Network Model
Three different types of nodes are deployed in the network, including locators, sensors, and attackers. The locators, with their own locations known in advance (by manual deployment or GPS devices), are deployed independently in the network with the probability of Poisson distribution. Each locator has a unique identification. The attackers collude in pairs to launch a wormhole attack to interfere with the self-localization of the sensors. All the nodes in the network are assumed to have the same transmission range . However, the communication range between two wormhole attackers can be larger than , as they can communicate with each other using certain communication technique.
3.2. Attack Model
The network is assumed to be deployed in hostile environment where wormhole attacks exist to disrupt the localization of sensors. During the wormhole attack, one attacker sniffs packets at one point in the network and tunnels them through the wormhole link to another point. Being as external attackers that cannot compromise legitimate nodes or their cryptographic keys, the wormhole attackers cannot acquire the content, for example, the type of the sniffed packets. However, the attackers may drop off the received packets randomly which severely deteriorates the sensor's localization process. We assume that the length of the wormhole link is larger than so that the endless packet transmission loop caused by both attackers is avoided.
All the locators that can exchange messages with the sensor, either via the wormhole link or not, are called neighboring locators ( -locators) of the sensor. Among these neighboring locators, the ones that can exchange messages with the sensor via the wormhole link are called dubious locators ( -locators), as their distance measurements may be incorrect and distort the localization; the locators that lie in the transmission range of the sensor are called valid locators ( -locators), as the sensor can obtain correct distance measurements with respect to them and assist the localization.
In this paper, we denote the set of -locators, -locators, and -locators as , and . For the scenario in Figure 1(a), , , and . It is obvious that .
Wormhole Attack Detection: The sensor detects the existence of a wormhole attack using the proposed detection schemes, and identifies whether it is under a duplex wormhole attack or a simplex wormhole attack.
4.1. Wormhole Attack Detection
We assume that each locator periodically broadcasts a beacon message within its neighboring vicinity. The beacon message will contain the ID and location information of the source locator. When the network is threatened by a wormhole attack, some affected locators will detect the abnormality through beacon message exchanges. The following scenarios are considered abnormal for locators: a locator receives the beacon message sent by itself; a locator receives more than one copy of the same beacon message from another locator via different paths; a locator receives a beacon message from another locator, whose location calculated based on the received message is outside the transmission range of receiving locator. When the locator detects the message abnormality, it will consider itself under a wormhole attack. Moreover, if the locator detects the message abnormality under the first scenario, that is, the locator receives the beacon message sent by itself, it will further derive that it is under a duplex wormhole attack. The beacon message has two additional bits to indicate these two statuses for each locator:
detection bit: this bit will be set to 1 if the locator detects the message abnormality through beacon message exchanges; otherwise, this bit will be 0;
type bit: this bit will be 1 if the locator detects itself under a duplex wormhole attack; otherwise, this bit will be 0.
When the sensor performs self-localization, it broadcasts a Loc_req message to its -locators. As soon as the locator receives the Loc_req message from the sensor, it replies with an acknowledgement message Loc_ack similar to the beacon message, which includes the ID and location information of the locator. The Loc_ack message also includes above two status bits. When the sensor receives the Loc_ack message, it can measure the distance from the sending locator to itself using the RSSI. The sensor also calculates the response time of each -locator based on the Loc_ack message using the approach in  to countervail the random delay on the MAC layer of the locator: when broadcasting the Loc_req packet, the sensor records the local time . Every locator gets the local time by time-stamping the packet at the MAC layer (i.e., the time when the packet is received at the MAC layer) instead of time-stamping the packet at the application layer. Similarly, when responding to the Loc_ack packet, the locator puts the local time at the MAC layer; both and are attached in the Loc_ack packet. When receiving the Loc_ack packet, the sensor gets its local time , and calculates the response time of the locator as . Note that this response time only eliminates the random delay at the MAC layer of the locators, but not the delay affected by attackers.
When conducting the localization, the sensor may also detect the message abnormality when it receives the Loc_req message sent by itself. Moreover, the sensor can check the detection bit of the Loc_ack message to decide if its -locator is under a wormhole attack or not.
We propose to use the following two detection schemes for the sensor to detect the wormhole attack.
Detection Scheme D1
If the sensor detects that it receives the Loc_req message sent from itself, it can determine that it is currently under a duplex wormhole attack. For example, when the sensor is under the duplex wormhole attack as shown in Figure 1(a), the Loc_req message transmitted by the sensor can travel from via the wormhole link to and then arrive at after being relayed by . Similarly, the Loc_req message can also travel from through the wormhole link to and then be received by . Thus, can determine that it is currently under a duplex wormhole attack.
Detection Scheme D2
If the sensor detects that the detection bit of the received Loc_ack message from any -locator is set to 1, can determine that it is under a simplex wormhole attack. Note that when using detection scheme D2, the sensor may generate a false alarm if the sensor is outside the transmission areas of the attackers but any of its -locators is inside the transmission areas of the attackers. However, this will only trigger the validate locators identification process but not affect the self-localization result.
The pseudocode of the wormhole attack detection is shown in Algorithm 1. The sensor broadcasts a Loc_req message for self-localization. When receiving the Loc_req message, each -locator replies a Loc_ack message with the status bits indicating whether it has detected the abnormality. The sensor measures the distances to its -locators based on the Loc_ack messages using RSSI method and calculates the response time of each -locator. If the sensor receives the Loc_req message sent by itself (detection scheme D1), it determines that it is under a duplex wormhole attack. Otherwise, if the sensor is informed by any -locator that the abnormality is detected (detection scheme D2), it declares that it is under a simplex wormhole attack. If no wormhole attack is detected, the sensor conducts the MLE localization.
Algorithm 1: Wormhole attack detection scheme.
1: Sensor broadcasts a Loc_req message.
including the message abnormality detection result.
3: Sensor waits for the Loc_ack messages to measure the
4: if sensor detects the attack using scheme D1 then
5: A duplex wormhole attack is detected.
6: else if sensor detects the attack using scheme D2 then
7: A simplex wormhole attack is detected.
9: No wormhole attack is detected.
10: end if
4.2. Basic Valid Locators Identification Approach
4.2.1. Duplex Wormhole Attack
When detecting that it is currently under a duplex wormhole attack, the sensor tries to identify all its -locators before the self-localization. Take in Figure 1(a) for example, when receiving the Loc_req message from the sensor, will respond a Loc_ack message to the sensor. As the sensor lies in the transmission range of , the Loc_ack message can be received by the sensor directly. In addition, the Loc_ack message can also travel from via the wormhole link to then arrive at the sensor. Therefore, the sensor can receive the Loc_ack message from for more than once. However, there will be three different scenarios: the locator lies in the transmission range of the sensor and its message is received by the sensor for three times (such as in Figure 1(a)); the locator lies out of the transmission range of the sensor and its message is received by the sensor for twice (such as in Figure 1(a)); the locator lies in the transmission range of the sensor and its message is received by the sensor for twice (such as in Figure 1(a)). We can see that and are -locators, but not . The sensor will use the following valid locator identification scheme to find the -locators.
Identification Scheme I1
When the sensor is under a duplex wormhole attack, if the sensor receives the Loc_ack message of an -locator for three times and the type bit in the Loc_ack message is set to 1, this -locator will be considered as a -locator (such as in Figure 1(a)). As the sensor only countervails the MAC layer delay of the locators but not that of the attackers when calculating the response time, the message traveling via the wormhole link has taken a longer response time. Thus, the distance measurement based on the Loc_ack message from this -locator which takes the shortest response time will be considered correct. If the sensor receives the Loc_ack message of an -locator just twice and the type bit in the Loc_ack message is set to 1, this -locator will be treated as a -locator (such as in Figure 1(a)). For the last scenario, if the sensor receives the Loc_ack message of an -locator twice and the type bit in the Loc_ack message is set to 0, this -locator will be considered as a -locator, and the distance measurement based on the Loc_ack message with a shorter response time will be considered as correct (such as in Figure 1(a)).
Distance Consistency Property of Valid Locators
Assuming a set of locators and corresponding measured distances , where is the location of locator and is the measured distance from the sensor to , . Based on and , the estimated location of the sensor is . The mean square error of the location estimation is . The distance consistency property of valid locators states that the mean square error of the location estimation based on the correct distance measurements is lower than a small threshold while the mean square error of the location estimation based on the distance measurements which contains some incorrect ones is not lower than the threshold.
Identification Scheme I2
If the sensor has determined no less than two valid locators using identification scheme I1, it can identify other valid locators by checking whether the distance estimation is consistent. A predefined threshold of the mean square error is determined, that is, a distance estimation with a mean square error smaller than is considered to be consistent. As shown in Figure 1(a), the sensor can identify and as -locators and obtain the correct distance measurements to them. For other undetermined locators, the sensor can identify them one by one. For example, to check whether is a -locator, the sensor can estimate its own location based on the distance measurements to and . As the distance measurement to is incorrect, the mean square error of the estimated distance measurements may exceed , which means that is not a -locator. When the sensor checks the distance consistency of , and , it can get that the mean square error is lower than , thus is treated as a -locator, and the distance measurement to is correct. After checking each of the undetermined -locators, the sensor can identify all -locators with the correct distance measurements.
4.2.2. Simplex Wormhole Attack
If the sensor detects that it is under a simplex wormhole attack, it will adopt the following valid locators identification schemes.
Identification Scheme I3
When the sensor under a simplex wormhole attack as shown in Figure 1(b), if the sensor receives the Loc_ack message of an -locator twice, this -locator will be considered as a -locator. For example, when in Figure 1(b) replies a Loc_ack message to the sensor, this message will travel through two different paths to the sensor, one directly from to the sensor and the other from to via the wormhole link to the sensor. Therefore, the sensor can conclude that is a -locator. To further obtain the correct distance measurement to , the sensor compares the response times of the Loc_ack message from through different paths, and the distance measurement with a shorter response time is considered correct. Similarly, can also be identified as a -locator and its correct distance measurement can be obtained.
Identification Scheme I4
When the sensor is under a simplex wormhole attack as shown in Figure 1(b), if the spatial property is violated by two -locators, it is obviously that one of them is a -locator and the other is a -locator. Take and in Figure 1(b) for example, the distance between them is larger than , after receiving Loc_ack messages from them, the sensor can detect that the spatial property does not hold by these two -locators. The response times of both -locators can be used to differentiate the -locator from the -locator. As the Loc_ack message from travels via the wormhole link to the sensor, it will take a longer response time than that from . The sensor will regard as a -locator and as a -locator because has a shorter response time. The distance measurement to is also considered correct.
Identification Scheme I5
When the sensor is under a simplex wormhole attack, similar to identification scheme I2, if the sensor detects at least two -locators using identification schemes I3 and I4, it can identify other -locators based on the distance consistency property of -locators. Take the scenario in Figure 1(b) for example, the sensor can identify , and as -locators and obtain the correct distance measurements to them. The sensor can further identify other -locators by checking the distance consistency. A mean square error smaller than can be obtained when the sensor estimates its location based on , and because they are all -locators. So the sensor can conclude that is a -locator and the distance measurement to is correct.
The procedure of basic valid locators identification approach is listed in Algorithm 2: If the sensor detects that it is under a duplex wormhole attack, it will conduct identification scheme I1 to detect -locators. As the distance consistency check needs as least three locators, if the sensor identifies no less than two -locators, it can use identification scheme I2 to identify other -locators. On the other hand, if the sensor detects that it is under a simplex wormhole attack, it adopts identification schemes I3 and I4 to identify the -locators. After that, if at least two -locators are identified, the sensor conducts identification scheme I5 to detect other -locators.
Algorithm 2: Basic Valid Locators Identification Approach.
5: end if
10: end if
11: end if
4.3. Enhanced Valid Locators Identification Approach
In the basic valid locators identification approach, if the sensor identifies less than three -locators, it will terminate the self-localization because the MLE method used in the self-localization needs at least three distance measurements. However, when using the identification schemes based on distance consistency property of -locators, many -locators may not be identified if the threshold of mean square error, , is set inappropriately a small value.
To overcome the above problem, we propose an enhanced valid locators identification approach which can adaptively adjust the threshold to make the sensor easier to identify more -locators: If the sensor detects that it is under a duplex wormhole attack, it conducts identification scheme I1 to detect -locators. If the sensor identifies no less than two -locators, it repeats to identify other -locators using identification scheme I2 and update the with an increment of until at least three -locators are identified or is larger than . On the other hand, if the sensor detects that it is under a simplex wormhole attack, it adopts schemes I3 and I4 to identify the -locators. If at least two -locators are identified, the sensor repeats to conduct scheme I5 to detect other -locators and update with an increment of until at least three -locators are identified or is larger than . The procedure of the enhanced valid locators identification approach is listed in Algorithm 3.
Algorithm 3: Enhanced Valid Locators Identification Approach.
8: end if
16: end if
17: end if
After the wormhole attack detection and valid locators identification, the sensor can identify -locators from its -locators. Furthermore, the sensor can estimate the correct distance measurements to the -locators. When the sensor obtains at least three correct distance measurements to its -locators, it conducts the MLE localization based on these distance measurements and the locations of the corresponding -locators.
5.1. Probability of Wormhole Attack Detection
Here, is the region in which is more than away from at least one of the locators in , that is the area of the corresponding shading region in Figure 3. Note that if any locator lies in , the sensor can identify it as a -locator using identification scheme I4.
In this section, we present the simulation results to demonstrate the effectiveness of the proposed secure localization scheme and to validate our theoretical results. The network parameters are set as follows: the transmission range of all types of nodes is identical and is set to 15 m; the density of locators /m2 (with the average degree around 4); the standard deviation of the distance measurement ; the label of the axis denotes the ratio of the length of the wormhole link (i.e., the distance between two attackers) to the transmission range. The threshold for the distance consistency . For the enhanced secure localization scheme, and .
In this paper, we analyze the impact of the wormhole attack on the range-based localization. We propose a novel distance-consistency-based secure localization mechanism against wormhole attacks including the wormhole attack detection, valid locators identification and self-localization. To analyze the performance of our proposed scheme, we build the theoretical model for calculating the probability of detecting the wormhole attack and the probability of identifying all -locators. We also present the simulation results to demonstrate the out-performance of our schemes and the validity of the proposed theoretical analysis. Although the proposed approach is described based on the RSSI method, it can be easily applied to the localization approaches based on the time-of-arrival (ToA) or time-difference-of-arrival (TDoA) methods.
In the future, our work will focus on the secure localization when the sensor is under multiple wormholes' attack simultaneously. We also intend to consider the secure localization when different nodes have different transmission ranges.
This work is supported in part by Grants PolyU 5236/06E, PolyU 5243/08E, A-PJ16, NSFC 60873223, NSFC 90818010, and ZJU-SKL ICT0903.
- Akyildiz I, Su W, Sankarasubramaniam Y, Cayirci E: A survey on sensor networks. IEEE Communications Magazine 2002, 40(8):102-114. 10.1109/MCOM.2002.1024422View ArticleGoogle Scholar
- Savvides A, Han C, Strivastava M: Dynamic fine-grained localization in ad-hoc networks of sensors. Proceedings of the ACM Annual International Conference on Mobile Computing and Networking (MOBICOM '01), July 2001, Rome, Italy 166-179.View ArticleGoogle Scholar
- Zhao M, Servetto SD: An analysis of the maximum likelihood estimator for localization problems. Proceedings of the 2nd International Conference on Broadband Networks (ICBN '05), October 2005, Boston, Mass, USA 59-67.Google Scholar
- Bahl P, Padmanabhan VN: RADAR: an in-building RF-based user location and tracking system. Proceedings of the 9th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '00), March 2000, Tel Aviv, Israel 2: 775-784.Google Scholar
- Li Z, Trappe W, Zhang Y, Nath B: Robust statistical methods for securing wireless localization in sensor networks. Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN '05), April 2005, Los Angeles, Calif, USA 91-98.Google Scholar
- Mao G, Fidan B, Anderson BDO: Wireless sensor network localization techniques. Computer and Telecommunications Networking 2007, 51(10):2529-2553.MATHGoogle Scholar
- Capkun S, Hubaux JP: Secure positioning of wireless devices with application to sensor networks. Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '05), March 2005 2: 1917-1928.View ArticleGoogle Scholar
- Khabbazian M, Mercier H, Bhargava VK: Wormhole attack in wireless ad hoc networks: analysis and countermeasure. Proceedings of the Global Telecommunications Conference (GLOBECOM '06), December 2006, San Francisco, Calif, USAGoogle Scholar
- Lazos L, Poovendran R: SeRLoc: robust localization for wireless sensor networks. ACM Transactions on Sensor Networks 2005, 1(1):73-100. 10.1145/1077391.1077395View ArticleGoogle Scholar
- Lazos L, Poovendran R: HiRLoc: high-resolution robust localization for wireless sensor networks. IEEE Journal on Selected Areas in Communications 2006, 24(2):233-246.View ArticleGoogle Scholar
- Boukerche A, Oliveira HABF, Nakamura EF, Loureiro AAF: Secure localization algorithms for wireless sensor networks. IEEE Communications Magazine 2008, 46(4):96-101.View ArticleGoogle Scholar
- Srinivasan A, Wu J: A survey on secure localization in wireless sensor networks. Encyclopedia of Wireless and Mobile Communications 2007.Google Scholar
- Liu D, Ning P, Du W: Attack-resistant location estimation in sensor networks. Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN '05), April 2005, Los Angeles, Calif, USA 99-106.Google Scholar
- Capkun S, Cagalj M, Srivastava M: Secure localization with hidden and mobile base stations. Proceedings of the 25th IEEE International Conference on Computer Communications Societies (INFOCOM '06), April 2006, Barcelona, SpainGoogle Scholar
- Lazos L, Poovendran R, Capkun S: ROPE: robust position estimation in wireless sensor networks. Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN '05), April 2005, Los Angeles, Calif, USA 324-331.Google Scholar
- Liu D, Ning P, Du W: Detecting malicious beacon nodes for secure location discovery in wireless sensor networks. Proceedings of the 25th IEEE International Conference on Distributed Computing Systems (ICDCS '05), June 2005Google Scholar
- Chen H, Lou W, Ma J, Wang Z: TSCD: a novel secure localization approach for wireless sensor networks. Proceedings of the 2nd International Conference on Sensor Technologies and Applications (SensorComm '08), August 2008, Cap Esterel, France 661-666.Google Scholar
- Hu YC, Perrig A, Johnson DB: Packet leashes: a defense against wormhole attacks in wireless networks. Proceedings of the 22nd Annual Conference of the IEEE Computer and Communications Societies (INFOCOM '03), April 2003, San Franciso, Calif, USA 3: 1976-1986.Google Scholar
- Wang W, Bhargava B: Visualization of wormholes in sensor networks. Proceedings of the ACM Workshop on Wireless Security (WiSec '04), 2004, New York, NY, USA 51-60.Google Scholar
- Wang W, Lu A: Interactive wormhole detection and evaluation. Information Visualization 2007, 6(1):3-17.Google Scholar
- Maheshwari R, Gao J, Das SR: Detecting wormhole attacks in wireless networks using connectivity information. Proceedings of the 26th Annual IEEE Conference on Computer Communications (INFOCOM '07), May 2007, Anchorage, Alaska, USA 107-115.Google Scholar
- Chen H, Lou W, Wang Z: Conflicting-set-based wormhole attack resistant localization in wireless sensor networks. Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing (UIC '09), July 2009, Brisbane, AustraliaGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.