Attacks on Sensing in Hostile Wireless Sensor-Actuator Environments

Czarlinska, A.; Luh, William; Kundur, Deepa

doi:10.1109/glocom.2007.193

Cited by 16 publications

(9 citation statements)

References 6 publications

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“…2) Malicious Sensor Network: In the second case, we assume the intruder has first randomly deployed a malicious WSN inside the monitored area as in [2], to disturb the observations of neighboring sensor nodes. Therefore, the compromised sensor nodes are initialized at the beginning of the experiments and then they continuously misbehave in order to better serve the intruder.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…A possible attacker strategy would be to tamper with sensors away from the attacker's true position to become falsely alarmed and prevent sensors close to the attacker from reporting detection. For instance in [2], the authors present a scenario with malicious actuator nodes deployed to perturb or distort the readings of neighboring sensor nodes within their actuation radius. Another possible attacker strategy would be to deploy various decoys inside the monitored area to cause confusion through a series of false alarms [3].…”

Section: Introductionmentioning

confidence: 99%

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Fault Tolerant Target Localization and Tracking in Wireless Sensor Networks Using Binary Data

Michaelides

Laoudias

Panayiotou

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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This paper investigates the use of a sensor network for localizing and tracking a moving target using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate a number of misbehaving nodes. SNAP (Subtract on Negative Add on Positive), is a simple event localization algorithm designed for WSNs applications that exhibits this fault tolerant behavior. The main contribution of this paper is to combine the decentralized implementation of SNAP with KalmanFilter techniques for tracking the movement of a target. This efficient tracking procedure provides fairly accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Tolerant Target Localization and Tracking in Wireless Sensor Networks Using Binary Data

Michaelides

Laoudias

Panayiotou

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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“…Alternatively, in a sophisticated attack scenario, the attacker may have compromised a number of sensors or deployed a malicious sensor network, so that several sensors deliberately reverse their original output to prevent intruder detection [9]. In this case, faulty sensors that fall inside the ROI of the source become non-alarmed; these are denoted as false negatives and shown as light gray circles in Fig.…”

Section: B Fault Modelmentioning

confidence: 96%

Sensor health state estimation for target tracking with binary sensor networks

Laoudias

Michaelides

Panayiotou

2013

2013 IEEE International Conference on Communications (ICC)

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We consider the problem of target (event source) tracking using a binary Wireless Sensor Network (WSN). For this problem, a WSN consisting of sensors that can detect the presence of a target in an area around them, should fuse the information received by the individual sensors in order to localize and track the target. This is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy depletion or adversary attacks. Using information from failed sensors during target tracking may lead to high estimation errors. Since failure of individual sensors is unavoidable, there is a need to estimate the health state of each sensor in order to ignore those sensors that are considered as faulty. The contribution of this work is the investigation of three different algorithms for estimating the sensors' health state simultaneously with target tracking.

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“…Whereas coded wireless transmission and decoding might produce a bit error with probability between 10 −6 to 10 −9 , the erroneous reporting rate of a particular scalar-sensor is not as easy to ascertain. Depending on the conditions of the harsh or hostile environment, we might expect a probability of sensor error that changes largely over time and over a wide range of values [5]. Furthermore, whereas we may be able to estimate the probability of sensor error due to harsh conditions, such estimation may not be possible in the case of an attacker who changes the attack statistics to avoid detection [5].…”

Section: Introductionmentioning

confidence: 93%

“…Depending on the conditions of the harsh or hostile environment, we might expect a probability of sensor error that changes largely over time and over a wide range of values [5]. Furthermore, whereas we may be able to estimate the probability of sensor error due to harsh conditions, such estimation may not be possible in the case of an attacker who changes the attack statistics to avoid detection [5]. Hence whether deployed in normal, harsh or hostile conditions, it is imperative to prevent and detect errors in scalar-reporting in order to achieve a reliable event-driven wVSN operation.…”

Section: Introductionmentioning

confidence: 99%

Attack vs. failure detection in event-driven wireless visual sensor networks

Czarlinska

Kundur

2007

Proceedings of the 9th Workshop on Multimedia &Amp; Security - MM&Sec '07

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In event-driven wireless Visual Sensor Networks (wVSNs), video nodes have access to additional data from scalar-sensors such as temperature or motion. The scalar-data may be used locally by the nodes instead of (or in conjunction with) vision technologies to control the potentially energy-costly transmission and storage of video frames and must thus be reliable. In this work we focus on the detection of occasional errors in such scalar-data sensors under both the scenario of harsh environmental conditions, and the scenario of hostile conditions involving an attacker. In the hostile case, the attack statistics may not be known to the cluster-head performing the error detection. We hence propose the use of a count detector in conjunction with Nash equilibrium analysis for the hostile case. We compare the detection performance of the count detector in hostile conditions to the performance of the optimal Neyman-Pearson (NP ) detector which may be used under harsh conditions (scenario where the error statistics may be estimated). Through analysis and simulations we conclude that in this severe regime of attack with missing statistics, the count detector performs reasonably well compared with the optimal NP detector with significance for reliable event-driven wVSN.

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Attacks on Sensing in Hostile Wireless Sensor-Actuator Environments

Cited by 16 publications

References 6 publications

Fault Tolerant Target Localization and Tracking in Wireless Sensor Networks Using Binary Data

Fault Tolerant Target Localization and Tracking in Wireless Sensor Networks Using Binary Data

Sensor health state estimation for target tracking with binary sensor networks

Attack vs. failure detection in event-driven wireless visual sensor networks

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