Network/Computer Security Workshop, May 06 The Robustness of Localization Algorithms to Signal Strength Attacks A Comparative Study Yingying Chen, Konstantinos.

Slides:



Advertisements
Similar presentations
Security and Sensor Networks By Andrew Malone and Bryan Absher.
Advertisements

Secure Location Verification with Hidden and Mobile Base Stations -TMC Apr, 2008 Srdjan Capkun, Kasper Bonne Rasmussen, Mario Cagalj, Mani Srivastava.
Our Wireless World Our Wireless World
Sensor-Based Abnormal Human-Activity Detection Authors: Jie Yin, Qiang Yang, and Jeffrey Junfeng Pan Presenter: Raghu Rangan.
Computer Science Dr. Peng NingCSC 774 Adv. Net. Security1 CSC 774 Advanced Network Security Topic 7.3 Secure and Resilient Location Discovery in Wireless.
On the Implications of the Log-normal Path Loss Model: An Efficient Method to Deploy and Move Sensor Motes Yin Chen, Andreas Terzis November 2, 2011.
Fault-Tolerant Target Detection in Sensor Networks Min Ding +, Dechang Chen *, Andrew Thaeler +, and Xiuzhen Cheng + + Department of Computer Science,
Computer Networks Group Universität Paderborn Ad hoc and Sensor Networks Chapter 9: Localization & positioning Holger Karl.
Tracking Fine-grain Vehicular Speed Variations by Warping Mobile Phone Signal Strengths Presented by Tam Vu Gayathri Chandrasekaran*, Tam Vu*, Alexander.
RADAR: An In-Building RF-based User Location and Tracking System Paramvir Bahl and Venkata N. Padmanabhan Microsoft Research.
1/24 Passive Interference Measurement in Wireless Sensor Networks Shucheng Liu 1,2, Guoliang Xing 3, Hongwei Zhang 4, Jianping Wang 2, Jun Huang 3, Mo.
A Beacon-Less Location Discovery Scheme for Wireless Sensor Networks Lei Fang (Syracuse) Wenliang (Kevin) Du (Syracuse) Peng Ning (North Carolina State)
Location Tracking1 Multifloor tracking algorithms in Wireless Sensor Networks Devjani Sinha Masters Project University of Colorado at Colorado Springs.
More routing protocols Alec Woo June 18 th, 2002.
Chess Review May 11, 2005 Berkeley, CA Tracking Multiple Objects using Sensor Networks and Camera Networks Songhwai Oh EECS, UC Berkeley
LYU0401 Location-Based Multimedia Mobile Service Clarence Fung Tilen Ma Supervisor: Professor Michael Lyu Marker: Professor Alan Liew.
1 Robust Statistical Methods for Securing Wireless Localization in Sensor Networks - Zang Li, Wade Trappe, Yanyong Zhang, Badri Nath Presented By: Vipul.
Range-free Localization Schemes for Large Scale Sensor Networks
The Feasibility of Launching and Detecting Jamming Attacks in Wireless Networks Authors: Wenyuan XU, Wade Trappe, Yanyong Zhang and Timothy Wood Wireless.
Localization Techniques in Wireless Networks Presented by: Rich Martin Joint work with: David Madigan, Wade Trappe, Y. Chen, E. Elnahrawy, J. Francisco,
RFID Object Localization Gabriel Robins and Kirti Chawla Department of Computer Science University of Virginia
WINLAB SCPL: Indoor Device-Free Multi-Subject Counting and Localization Using Radio Signal Strength Rutgers University Chenren Xu Joint work with Bernhard.
Rutgers: Gayathri Chandrasekaran, Tam Vu, Marco Gruteser, Rich Martin,
Basics of Wireless Locationing Mikko Asikainen, Msc University of Eastern Finland Department of Computer Science.
Secure Localization Algorithms for Wireless Sensor Networks proposed by A. Boukerche, H. Oliveira, E. Nakamura, and A. Loureiro (2008) Maria Berenice Carrasco.
MoteTrack: Robust, Decentralized Approach to RF- based Location Tracking Konrad Lorinz and Matt Welsh Harvard University, Division of Engineering and Applied.
1 Secure Cooperative MIMO Communications Under Active Compromised Nodes Liang Hong, McKenzie McNeal III, Wei Chen College of Engineering, Technology, and.
Wireless Sensor Networking for “Hot” Applications: Effects of Temperature on Signal Strength, Data Collection and Localization.
How Does Topology Affect Security in Wireless Ad Hoc Networks? Ioannis Broustis CS 260 – Seminar on Network Topology.
Sensor Positioning in Wireless Ad-hoc Sensor Networks Using Multidimensional Scaling Xiang Ji and Hongyuan Zha Dept. of Computer Science and Engineering,
A Survey of Secure Location Schemes in Wireless Networks /5/21.
Authors: Sheng-Po Kuo, Yu-Chee Tseng, Fang-Jing Wu, and Chun-Yu Lin
SCPL: Indoor Device-Free Multi-Subject Counting and Localization Using Radio Signal Strength Chenren Xu†, Bernhard Firner†, Robert S. Moore ∗, Yanyong.
M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha.
Dynamic Coverage Enhancement for Object Tracking in Hybrid Sensor Networks Computer Science and Information Engineering Department Fu-Jen Catholic University.
Introduction to Sensor Networks Rabie A. Ramadan, PhD Cairo University 3.
Localization With Mobile Anchor Points in Wireless Sensor Networks
Bayesian Indoor Positioning Systems Presented by: Eiman Elnahrawy Joint work with: David Madigan, Richard P. Martin, Wen-Hua Ju, P. Krishnan, A.S. Krishnakumar.
Tracking with Unreliable Node Sequences Ziguo Zhong, Ting Zhu, Dan Wang and Tian He Computer Science and Engineering, University of Minnesota Infocom 2009.
The Collocation of Measurement Points in Large Open Indoor Environment Kaikai Sheng, Zhicheng Gu, Xueyu Mao Xiaohua Tian, Weijie Wu, Xiaoying Gan Department.
Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and David H.C. Du Dept. of.
The Limits of Localization Using Signal Strength: A Comparative Study Eiman Elnahrawy, Xiaoyan Li, and Richard Martin Dept. of Computer Science, Rutgers.
Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors Weikuan Yu Dept. of Computer and Info. Sci. The Ohio State University.
Computer Science 1 CSC 774 Advanced Network Security Distributed detection of node replication attacks in sensor networks (By Bryan Parno, Adrian Perrig,
Improving MBMS Security in 3G Wenyuan Xu Rutgers University.
Algorithms for Wireless Sensor Networks Marcela Boboila, George Iordache Computer Science Department Stony Brook University.
1 Robust Statistical Methods for Securing Wireless Localization in Sensor Networks (IPSN ’05) Zang Li, Wade Trappe Yanyong Zhang, Badri Nath Rutgers University.
Secure routing in wireless sensor network: attacks and countermeasures Presenter: Haiou Xiang Author: Chris Karlof, David Wagner Appeared at the First.
Rushing Attacks and Defense in Wireless Ad Hoc Network Routing Protocols ► Acts as denial of service by disrupting the flow of data between a source and.
WINLAB Improving RF-Based Device-Free Passive Localization In Cluttered Indoor Environments Through Probabilistic Classification Methods Rutgers University.
A New Hybrid Wireless Sensor Network Localization System Ahmed A. Ahmed, Hongchi Shi, and Yi Shang Department of Computer Science University of Missouri-Columbia.
A Passive Approach to Sensor Network Localization Rahul Biswas and Sebastian Thrun International Conference on Intelligent Robots and Systems 2004 Presented.
RADAR: an In-building RF-based user location and tracking system
11/25/2015 Wireless Sensor Networks COE 499 Localization Tarek Sheltami KFUPM CCSE COE 1.
Secure In-Network Aggregation for Wireless Sensor Networks
Performance Study of Localization Techniques in Zigbee Wireless Sensor Networks Ray Holguin Electrical Engineering Major Dr. Hong Huang Advisor.
Ad Hoc Network.
Adversary models in wireless security Suman Banerjee Department of Computer Sciences Wisconsin Wireless and NetworkinG Systems (WiNGS)
© 2007 Sean A. Williams 1 Ecolocation: A Sequence Based Technique for RF Localization in Wireless Sensor Networks Authors: Kiran Yedavalli, Bhaskar Krishnamachari,
1 Routing security against Threat models CSCI 5931 Wireless & Sensor Networks CSCI 5931 Wireless & Sensor Networks Darshan Chipade.
Secure positioning in Wireless Networks Srdjan Capkun, Jean-Pierre Hubaux IEEE Journal on Selected area in Communication Jeon, Seung.
Smartphone-based Wi-Fi Pedestrian-Tracking System Tolerating the RSS Variance Problem Yungeun Kim, Hyojeong Shin, and Hojung Cha Yonsei University Bing.
LEMON: An RSS-Based Indoor Localization Technique Israat T. Haque, Ioanis Nikolaidis, and Pawel Gburzynski Computing Science, University of Alberta, Canada.
E. Elnahrawy, X. Li, and R. Martin Rutgers U.
RF-based positioning.
Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors
Presenter: Yawen Wei Author: Loukas Lazos and Radha Poovendran
Indoor Location Estimation Using Multiple Wireless Technologies
RFID Object Localization
Presentation transcript:

Network/Computer Security Workshop, May 06 The Robustness of Localization Algorithms to Signal Strength Attacks A Comparative Study Yingying Chen, Konstantinos Kleisouris, Xiaoyan Li, Wade Trappe, and Richard P. Martin Dept. of Computer Science Wireless Information Network Laboratory Rutgers University May 16 th, 2006

Network/Computer Security Workshop, May 06 Background Localizing sensor nodes is the building block for high-level applications: Tracking, monitoring, and geometric-based routing Location-based services become more prevalent Received Signal Strength (RSS) is an attractive basis for indoor localization algorithms: Reuse the existing communication infrastructure , , Bluetooth support the technology Tremendous cost saving

Network/Computer Security Workshop, May 06 Motivation Localization infrastructure became the target of malicious attacks (non-conventional security threats) Important to understand how localization is affected by non-cryptographic attacks Study the susceptibility of RSS-based localization algorithms to signal strength attacks: Unanticipated power losses and gains Attacks to the transmitting device or individual landmarks.

Network/Computer Security Workshop, May 06 Goal Study the behavior of RSS-based localization algorithms to signal strength attacks Generate attack detection mechanisms for localization algorithms Improve the current algorithms to tolerant attacks Develop attack resistant algorithms

Network/Computer Security Workshop, May 06 High Level Results The average performance of all the algorithms in response to an attack is about the same General rule of thumb: easy to conduct attack by 15 dB and cause the localization error of feet Need to make localization more robust to signal strength attacks Preliminary work shows possibility of attack detection

Network/Computer Security Workshop, May 06 Outline Background and motivation RF-based localization algorithms Conducting signal strength attacks Measuring attack susceptibility Experimental study Analysis and discussion Conclusion Related work Future research

Network/Computer Security Workshop, May 06 Summary of Algorithms under Study Area-basedPoint-based 1.Simple Point Matching (SPM) 2. Area Based Probability (ABP) 3. Bayesian Networks (BN) 4. RADAR (R1) 5. Averaged RADAR (R2) 6. Gridded RADAR (GR) 7. Highest Probability (P1) 8. Averaged Highest Probability (P2) 9. Gridded Highest Probability (GP) Offline and online phases (attack during online) Matching vs. signal to distance

Network/Computer Security Workshop, May 06 A Generalized Localization Model Physical Space ( D ) Signal Space ( R ) F G S1S1 S2S2 SnSn : a single point or a region

Network/Computer Security Workshop, May 06 Outline Background and motivation RF-based localization algorithms Conducting signal strength attacks Measuring attack susceptibility Experimental study Analysis and discussion Conclusion Related work Future research

Network/Computer Security Workshop, May 06 Signal Strength Attacks Materials – easy to access Attacks – simple to perform with low cost Linear relationship - linear attack model Two approaches: Attack on the entire set of landmarks Attack on a single landmark

Network/Computer Security Workshop, May 06 Outline Background and motivation RF-based localization algorithms Conducting signal strength attacks Measuring attack susceptibility Experimental study Analysis and discussion Conclusion Related work Future research

Network/Computer Security Workshop, May 06 Attack Susceptibility Metrics Estimator distance error Estimator precision Hölder metrics Relates the magnitude of the perturbation in signal space to its effect on the localization results:

Network/Computer Security Workshop, May 06 Outline Background and motivation RF-based localization algorithms Conducting signal strength attacks Measuring attack susceptibility Experimental study Analysis and discussion Conclusion Related work Future research

Network/Computer Security Workshop, May 06 Experimental Setup (CoRE and Industrial Lab) - Floor plan: 200ft x 80ft (16000 ft 2 ) - Deployment of 4 landmarks (somewhat co-linear) (somewhat co-linear) training points, 170 testing points - Floor plan: 225ft x 144ft (32400 ft 2 ) - Deployment of 5 landmarks (more evenly distributed) (more evenly distributed) training points, 138 testing points

Network/Computer Security Workshop, May 06 Error Analysis CoRE - all landmarks attenuation attack (10/15/25 dB)

Network/Computer Security Workshop, May 06 Error Analysis All landmarks amplification attack (10 dB) CoREIndustrial Lab

Network/Computer Security Workshop, May 06 Linear Response Attenuation Attack - All landmarks; Landmark 1, 2 and 3 All landmarks: ~ 1.55 ft/dB, single landmark: ~ 0.64 ft/dB

Network/Computer Security Workshop, May 06 Worst-case Error CoRE: attenuation attack BN, R1, R2: 4ft/dB P1, P2: 3ft/dB ABP, GP, GR, SPM: 2ft/dB Exception: SPM ~ 0.61

Network/Computer Security Workshop, May 06 Precision Study: Example of Localization Results in CoRE NormalAttenuation attack (25dB) Landmark 1 SPM ABP BN

Network/Computer Security Workshop, May 06 Conclusion Localization error of all algorithms scales similarly under attack With single exception of Bayesian Networks algorithm to individual landmark attacks The average susceptibility to an attack is essentially identical In order to lessen the worst-case effect of a potential attack, desirable to employ algorithms that perform averaging

Network/Computer Security Workshop, May 06 Conclusion (cont.) Degraded gracefully: linear scaling in localization error to attacks No algorithm “collapses” in response to an attack All landmarks attack: ft/dB Single landmark attack: ft/dB Rule of thumb: easy to attack by 15 dB, cause localization error of ft Precision increased for all three area-based algorithms: A decrease and a spatial-shift in the returned area – bias is introduced ABP significantly shrank the returned areas in response to large changes in signal strength – attack detection

Network/Computer Security Workshop, May 06 Related Work Category of localization algorithms: Range-based [hightower01design, GPS, nissanka00 ], range-free [shang03, niculescu01aps], scene matching [youssef03localization,roos02stat, battiti02stat, bahl00] Aggregate [dohertyl01, shang03] or singular (only refer to landmarks) Non-cryptographic attacks affect localization: Wormhole attacks [hu03packet] – shorten the distance between two nodes Compromised nodes [zang05robust]; compromised landmarks [liu05attack] Pursue for secure localization algorithms Distance bounding protocol [ Capkun05 ] to upper-bound the distance between two nodes Hidden and mobile base stations [Capkun06] to verify location estimate Use directional antenna and distance bounding [lazos05] to achieve security Robust statistical methods [zang05robust] to achieve reliable localization

Network/Computer Security Workshop, May 06 Future Research Study different attack models: Attacks performed by the directional antenna Develop attack detection mechanisms for RF- based localization algorithms Extend the current algorithms to tolerant attacks Derive attack resistant algorithms Goal: adversaries can not affect localization !

Network/Computer Security Workshop, May 06 Thank you &Questions