Mauro Conti
ABSTRACT
The Internet of Things (IoT) paradigm, in conjunction with the one of smart cities, is pursuing toward the concept of smart buildings, i.e., ``intelligent'' buildings able to receive data from a network of sensors and thus to adapt the environment. IoT sensors can monitor a wide range of environmental features such as the energy consumption inside a building at fine-grained level (e.g., for a specific wall-socket). Some smart buildings already deploy energy monitoring in order to optimize the energy use for good purposes (e.g., to save money, to reduce pollution). Unfortunately, such measurements raise a significant amount of privacy concerns.
In this paper, we investigate the feasibility of recognizing the pair laptop-user (i.e., a user using her own laptop) from the energy traces produced by her laptop. We design MTPlug, a framework that achieves this goal relying on supervised machine learning techniques as pattern recognition in multivariate time series. We present a comprehensive implementation of this system and run a thorough set of experiments. In particular, we collected data by monitoring the energy consumption of two groups of laptop users, some office employees and some intruders, for a total of 27 people. We show that our system is able to build an energy profile for a laptop user with accuracy above 80%, in less than 3.5 hours of laptop usage. To the best of our knowledge, this is the first research that assesses the feasibility of laptop users profiling relying uniquely on fine-grained energy traces collected using wall-socket smart meters.
- J. Abonyi, B. Feil, S. Nemeth, and P. Arva. Principal component analysis based time series - segmentation application to hierarchical clustering for multivariate process data. In Proc. of IEEE ICCC, 2003.Google Scholar
- A. Akbar, M. Nati, F. Carrez, and K. Moessner. Contextual Occupancy Detection for Smart Office by Pattern Recognition of Electricity Consumption Data. http://iot-cosmos.eu/node/1566, 2014.Google Scholar
- M. Ambrosin, H. Hosseini, K. Mandal, M. Conti, and R. Poovendran. Verifiable and privacy-preserving fine-grained data-collection for smart metering. In Communications and Network Security (CNS), 2015 IEEE Conference on, pages 655--658. IEEE, 2015.Google ScholarCross Ref
- M. Basseville, I. V. Nikiforov, et al. Detection of abrupt changes: theory and application, volume 104. Prentice Hall Englewood Cliffs, 1993. Google ScholarDigital Library
- C. M. Bishop. Pattern recognition and machine learning. Springer, 2006. Google ScholarDigital Library
- S. S. Clark, H. Mustafa, B. Ransford, J. Sorber, K. Fu, and W. Xu. Current events: Identifying webpages by tapping the electrical outlet. In Computer Security-ESORICS 2013, pages 700--717. Springer, 2013.Google ScholarCross Ref
- M. Conti, L. Mancini, R. Spolaor, and N. V. Verde. Analyzing android encrypted network traffic to identify user actions. TIFS, 2016.Google ScholarCross Ref
- T. Do, S. Rawshdeh, and W. Shi. ptop: A process-level power profiling tool. In Proc. of HotPower. USENIX, 2009.Google Scholar
- A. Ebadat, G. Bottegal, D. Varagnolo, B. Wahlberg, and K. H. Johansson. Estimation of building occupancy levels through environmental signals deconvolution. In Proc. of ACM BuildSys, 2013. Google ScholarDigital Library
- C. Efthymiou and G. Kalogridis. Smart grid privacy via anonymization of smart metering data. In Proc. of IEEE SmartGridComm, 2010.Google ScholarCross Ref
- D. Genkin, I. Pipman, and E. Tromer. Get your hands off my laptop: Physical side-channel key-extraction attacks on pcs. Journal of Cryptographic Engineering, 5(2):95--112, 2015.Google ScholarCross Ref
- G. W. Hart. Nonintrusive appliance load monitoring. Proc. of the IEEE, 1992.Google ScholarCross Ref
- M. Jawurek, M. Johns, and K. Rieck. Smart metering de-pseudonymization. In Proc. of ACM ACSAC, 2011. Google ScholarDigital Library
- X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. Design and implementation of a high-fidelity ac metering network. In Proc. of IPSN, 2009. Google ScholarDigital Library
- G. Kalogridis, C. Efthymiou, S. Z. Denic, T. A. Lewis, and R. Cepeda. Privacy for smart meters: Towards undetectable appliance load signatures. In Proc. of IEEE SmartGridComm, 2010.Google ScholarCross Ref
- E. Keogh, S. Chu, D. Hart, and M. Pazzani. An online algorithm for segmenting time series. In Proc. of IEEE ICDM, 2001. Google ScholarDigital Library
- W. Kleiminger, C. Beckel, T. Staake, and S. Santini. Occupancy detection from electricity consumption data. In Proc. of ACM BuildSys, 2013. Google ScholarDigital Library
- J. Liang, S. K. Ng, G. Kendall, and J. W. Cheng. Load signature study - part ii: Disaggregation framework, simulation, and applications. PWRD, 2010.Google ScholarCross Ref
- J. Lifton, M. Feldmeier, Y. Ono, C. Lewis, J. Paradiso, et al. A platform for ubiquitous sensor deployment in occupational and domestic environments. In Proc. of IEEE IPSN, 2007. Google ScholarDigital Library
- G.-y. Lin, S.-c. Lee, and J. Y.-j. Hsu. Sensing from the panel: Applying the power meters for appliance recognition. In Proc. of AIAI. Springer, 2009.Google Scholar
- D. McIntire, T. Stathopoulos, and W. Kaiser. etop-sensor network application energy profiling on the leap2 platform. In Proc. of IEEE IPSN, 2007. Google ScholarDigital Library
- M. Nati, A. Gluhak, H. Abangar, and W. Headley. Smartcampus: A user-centric testbed for internet of things experimentation. In Proc. of IEEE WPMC, 2013.Google Scholar
- G. Procaccianti, L. Ardito, M. Morisio, et al. Profiling power consumption on desktop computer systems. In ICT-GLOW. Springer, 2011. Google ScholarDigital Library
- E. L. Quinn. Smart metering and privacy: Existing laws and competing policies. Available at SSRN 1462285, 2009.Google Scholar
- L. Rashidi, S. Rajasegarar, C. Leckie, M. Nati, A. Gluhak, M. A. Imran, and M. Palaniswami. Profiling spatial and temporal behaviour in sensor networks: A case study in energy monitoring. In Proc. of IEEE ISSNIP, 2014.Google ScholarCross Ref
- A. Reinhardt, P. Baumann, D. Burgstahler, M. Hollick, H. Chonov, M. Werner, and R. Steinmetz. On the accuracy of appliance identification based on distributed load metering data. In Proc. of IEEE SustainIT, 2012.Google Scholar
- A. Ridi and J. Hennebert. Hidden markov models for ilm appliance identification. Procedia Computer Science, 2014.Google ScholarCross Ref
- A. Savitzky and M. J. Golay. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry.Google Scholar
- M. Shinohara, M. Murakami, H. Iwane, S. Takahashi, S. Yamane, H. Anai, T. Sonoda, and N. Yugami. Development of an integrated control system using notebook pc batteries for reducing peak power demand. International Journal of Informatics Society, 5(3):109--118, 2013.Google Scholar
- S. Spiegel, J. Gaebler, A. Lommatzsch, E. De Luca, and S. Albayrak. Pattern recognition and classification for multivariate time series. In Proc. of ACM Sensor-KDD, 2011. Google ScholarDigital Library
- A. Zoha, A. Gluhak, M. A. Imran, and S. Rajasegarar. Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 2012.Google Scholar
- A. Zoha, A. Gluhak, M. Nati, and M. A. Imran. Low-power appliance monitoring using factorial hidden markov models. In Proc. of IEEE ISSNIP, 2013.Google ScholarCross Ref
Index Terms
- Mind The Plug! Laptop-User Recognition Through Power Consumption
Recommendations
Online identification of appliances from power consumption data collected by smart meters
The efficient use of resources is a matter of great concern in today's society, especially in the energy sector. Although the main strategy to decrease energy use has long been focused on supply, over the last few years, there has been a shift to the ...
Secure and energy-efficient smart building architecture with emerging technology IoT
AbstractWith the advent of the Internet-of-Things (IoT), it is considered to be one of the latest innovations that offer interesting opportunities for different vertical industries. One of the most relevant IoT technology areas is smart ...
Machine learning Techniques for Energy Theft Detection in AMI
ICSIM '18: Proceedings of the 2018 International Conference on Software Engineering and Information ManagementAdvanced Metering Infrastructure (AMI and smart meter) is considered as the basic building block for the development of smart grid in the power distribution system. a Smart meter is one of the keys elements of Advanced Metering Infrastructure, it ...
Comments