Signals Processing

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Broadly speaking, signal processing covers signal acquisition and reconstruction, quality improvement, signal compression and feature extraction. Each of these processes introduces vulnerabilities into communications and other systems. The research articles cited here explore trust between networks, steganalysis, tracing passwords across networks, and certificates.

  • “Evaluating Geometrical Parameters of Disperse Structures by the Images”. Tatiana Ruzova, Alexander Tolstopyat, Vladimir Yeliseyev, and Leonid Fleer. Signal Processing Research PP.49-54, 3-2013. (ID#:14-1180) Available at: http://www.seipub.org/spr/
  • “Texture Based Steganalysis of Grayscale Images Using Neural Network”. Arooj Nissar, A. H. Mir. Signal Processing Research PP.17-24, 3-2013. (ID#:14-1181) Available at: http://www.seipub.org/spr/
  • “Re³: Relay Reliability Reputation for Anonymity Systems”  Anupam Das, Nikita Borisov, Prateek Mittal, and Matthew Caesar, ASIACCS, June 2014 (to appear; not yet available online.) (ID#:14-1182)
  • “The Tangled Web of Password Reuse” Anupam Das, Joseph Bonneau, Matthew Caesar, Nikita Borisov, and XiaoFeng Wang, NDSS, February 2014 (to appear) (ID#:14-1183) Available at: http://hatswitch.org/~nikita/papers/password-reuse-ndss14.pdf The authors address how an attacker can take advantage of a known password from one site to improve their ability to determine user passwords at other sites. Their research suggests that close to 50% of users reuse the same password for multiple sites.
  • “Exploiting Innocuous Activity for Correlating Users Across Sites” O. Goga, H. Lei, S. H. K. Parthasarathi, G. Friedland, R. Sommer, and R. Teixeira. Proceedings of the World Wide Web Conference (WWW), Rio de Janeiro, Brazil May 2013. (ID#:14-1184) Available at: https://www.icsi.berkeley.edu/pubs/networking/ICSI_exploitinginnocuousactivity13.pdf The authors researched the ways in which attackers find accounts on multiple social network sites all belonging to a single user by exploiting activity that is part of the posted content. Their findings suggest that content itself may provide enough information to connect the accounts to a single user.
  • “Secloud: A cloud-based comprehensive and lightweight security solution for smartphones”. Saman Zonouz, Amir Houmansadr, Robin Berthier, Nikita Borisov, and William H. Sanders, Computers & Security, September 2013 DOI (ID#:14-1186) Available at: http://www.sciencedirect.com/science/article/pii/S016740481300031X (fee required)
  • “A building code for building code: putting what we know works to work”. Carl E. Landwehr. Proceedings of the 29th Annual Computer Security Applications Conference Pages 139-147, ACM New York, NY, 2013. (ID#:14-1188) Available at: http://dl.acm.org/citation.cfm?doid=2523649.2530278 (fee required) The author suggests an approach to capturing and implementing lessons-learned about how to build secure software.
  • “Systems Thinking for Safety and Security” William Young and Nancy Leveson. Proceedings of the 29th Annual Computer Security Applications Conference Pages 139-147, ACM New York, NY, 2013. (ID#:14-1189) Available at: http://dl.acm.org/citation.cfm?doid=2523649.2530277 (fee required) Although the security and safety communities face similar challenges, there appears to have been little exchange of information between security and safety professionals. The authors suggest a framework for safety that may be applicable to security.
  • “Leveraging SDN Layering to Systematically Troubleshoot Networks”. B. Heller, C. Scott, N. McKeown, S. Shenker, A. Wundsam, H. Zeng, S. Whitlock, V. Jeyakumar, N. Handigol, M. McCauley, K. Zarifis and P. Kazemian. Proceedings of ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking (HotSDN '13), Hong Kong, China, August 2013. (ID#:14-1190) Available at: http://www.icsi.berkeley.edu/pubs/networking/leveragingsdn13.pdf
  • “Less Pain, Most of the Gain: Incrementally Deployable ICN”. S. Fayazbakhsh, Y. Lin, A. Tootonchian, A. Ghodsi, T. Koponen, B. Maggs, KC Ng, V. Sekar, and S. Shenker. Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM 2013), pp. 147-158, Hong Kong, China, August 2013. (ID#:14-1191) Available at: http://www.icsi.berkeley.edu/pubs/networking/lesspain13.pdf
  • "Simultaneous Target and Multipath Positioning," Li Li; Krolik, J.L., Selected Topics in Signal Processing, IEEE Journal of , vol.8, no.1, pp.153,165, Feb. 2014. (ID#:14-1194) Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6658859&isnumber=6712940  This paper addresses the problem of target geo-localization in complex multipath environments such as indoor and urban settings. In both radar applications (where targets are non-cooperative) and navigation in GPS-denied areas using RF signals (where the subject is cooperative), multipath propagation is a well-known cause of large geo-location errors except in rare cases when a very accurate channel model is available. This work addresses more typical situations of uncertain channel properties by jointly estimating target position and multipath parameters. The proposed Simultaneous Target and Multipath Positioning (STAMP) approach involves an application of multi-scan multi-hypothesis data association to approximate recursive Bayesian estimates of both moving target location as well as specular reflector and point scatterer locations. STAMP achieves joint estimation by exploiting the different dynamics of targets (e.g., people moving) versus channel parameters (e.g., fixed wall locations). Algorithm performance is evaluated in simulation for radar localization of a non-cooperative target in an uncertain urban multipath environment. In addition, the successful demonstration of STAMP geolocation using real wideband microwave data collected in an actual building foyer with unknown floor plan is discussed. Finally, the issue of identifiability of both target and multipath parameters is explored via analysis of the Cramer-Rao Lower Bound (CRLB) on joint estimation of target and multipath parameters in both line-of-sight and non-line-of-sight scenarios.
  • "A Bayesian Approach to Device-Free Localization: Modeling and Experimental Assessment," Savazzi, S.; Nicoli, M.; Carminati, F.; Riva, M., Selected Topics in Signal Processing, IEEE Journal of , vol.8, no.1, pp.16,29, Feb. 2014.  (ID#:14-1195) Available at:  http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6644290&isnumber=6712940 Device-free positioning allows to localize and track passive targets (i.e., not carrying any electronic device) moving in an area monitored by a dense network of low-power and battery-operated wireless sensors. The technology is promising for a wide number of applications, ranging from ambient intelligence in smart spaces, intrusion detection, emergency and rescue operations in critical areas. In this paper, a new approach is proposed where both the average path-loss and the fluctuations of the received signal strength induced by the moving target are jointly modeled based on the theory of diffraction. A novel stochastic model is derived and used for the evaluation of fundamental performance limits. The model is proved to be tight enough to be adopted for real-time estimation of the target location. The proposed localization system is validated by extensive experimental studies in both indoor and outdoor environments. The model calibration is addressed in practical scenarios to compare the performance of different Bayesian online localization methods. The test-bed system supports efficient and flexible target tracking, without requiring any action from the end-users. In addition, the technology is proven to be readily applicable over the existing IEEE 802.15.4 compliant PHY layer standard, by adapting the low-level MAC firmware.
  • "Device-Free Person Detection and Ranging in UWB Networks," Kilic, Y.; Wymeersch, H.; Meijerink, A.; Bentum, M.J.; Scanlon, W.G., Selected Topics in Signal Processing, IEEE Journal of , vol.8, no.1, pp.43,54, Feb. 2014. (ID#:14-1196) Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6600737&isnumber=6712940  We present a novel device-free stationary person detection and ranging method, that is applicable to ultra-wide bandwidth (UWB) networks. The method utilizes a fixed UWB infrastructure and does not require a training database of template waveforms. Instead, the method capitalizes on the fact that a human presence induces small low-frequency variations that stand out against the background signal, which is mainly affected by wideband noise. We analyze the detection probability, and validate our findings with numerical simulations and experiments with off-the-shelf UWB transceivers in an indoor environment.

 

Note:

Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to SoS.Project (at) SecureDataBank.net for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.