DECODE : Detecting co-moving wireless devices

Chandrasekaran, G.; Ergin, Mesut Ali; Gruteser, Marco; Martin, Richard P.; Yang, Jie; Chen, Yingying

doi:10.1109/mahss.2008.4660051

Cited by 8 publications

(7 citation statements)

References 17 publications

(16 reference statements)

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“…In [12] signal variance is used with Hidden Markov Model (HMM) to eliminate oscillations between the static and mobile states for mobility detection. Further, [13] proposed to use correlation coefficients on RSS traces to detect wireless devices that are moving together.…”

Section: Related Workmentioning

confidence: 99%

Detecting Spoofing Attacks in Mobile Wireless Environments

Yang

Chen

Trappe

2009

2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

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Abstract-The flexibility and openness of wireless networks enables an adversary to masquerade as other devices easily. Identity-based spoofing attacks are serious network threats as they can facilitate a variety of advanced attacks to undermine the normal operation of networks. However, the existing mechanisms can only detect spoofing attacks when the victim node and the spoofing node are static. In this paper, we propose a method for detecting spoofing attacks in the mobile wireless environment, that is when wireless devices, such as the victim node and/or the spoofing node are moving. We develop the DEMOTE system, which exploits Received Signal Strength (RSS) traces collected over time and achieves an optimal threshold to partition the RSS traces into classes for attack detection. Further, our novel algorithm alignment prediction (ALP), when without the knowledge of spatial constraint of the wireless nodes, utilizes temporal constraints to predict the best RSS alignment of partitioned RSS classes for RSS trace reconstruction over time. Our approach does not require any changes or cooperation from wireless devices other than packet transmissions. Through experiments from an office building environment, we show that DEMOTE achieves accurate attack detection both in signal space as well as in physical space using localization and is generic across different technologies including IEEE 802.11 b/g and IEEE 802.15.4.

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Section: Related Workmentioning

confidence: 99%

Detecting Spoofing Attacks in Mobile Wireless Environments

Yang

Chen

Trappe

2009

2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

Self Cite

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show abstract

“…In [7] signal variance is used with Hidden Markov Model (HMM) to eliminate oscillations between the static and mobile states for mobility detection. Further, [8] proposed to use correlation coefficients on RSSI traces to detect wireless devices that are moving together.…”

Section: Related Workmentioning

confidence: 99%

“…Intuitively at more time points that 8' is in the neighborhood of 8 in the past, it will be more likely that 8 ' remains as the neighbor of 8 in the future.…”

Section: Percentage Of Study T Imementioning

confidence: 99%

“…We note that our results can be easily extended to more than two dimensions. We denote the location of mobile node 8 T(8tl (x, y), ... , 8tn> (x, y)). Given a set of trajectories T {T I , ... , Tn} of n nodes, our goal is to use T to predict the neighborhood of 8 at a future time point t i > t m .…”

Section: B Probability Modelmentioning

confidence: 99%

“…We further define the belief probability that node 8' is in the neighborhood of 8 where P (Q, resp.) and or «(jQ, resp.)…”

Section: B Probability Modelmentioning

confidence: 99%

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Prediction of near likely nodes in data-centric mobile wireless networks

Chen

Wang

Zheng

et al. 2009

MILCOM 2009 - 2009 IEEE Military Communications Conference

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Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

Yassin

Nasser

Awad

et al. 2017

IEEE Commun. Surv. Tutorials

747

444

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Abstract-The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

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DECODE : Detecting co-moving wireless devices

Abstract: We present the DECODE technique to determine from a remote receiver whether a set oftransmitters are co-moving, i.e., moving

Cited by 8 publications

References 17 publications

Detecting Spoofing Attacks in Mobile Wireless Environments

Detecting Spoofing Attacks in Mobile Wireless Environments

Prediction of near likely nodes in data-centric mobile wireless networks

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

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