add section in discussion, factorize capture process

BPV/qrs/bibli.bib

@@ -484,3 +484,124 @@ pages={938-946},
 doi={10.1109/TVT.2018.2884767}
 }
 
+
+@inproceedings{clark_current_2013,
+	address = {Berlin, Heidelberg},
+	series = {Lecture {Notes} in {Computer} {Science}},
+	title = {Current {Events}: {Identifying} {Webpages} by {Tapping} the {Electrical} {Outlet}},
+	isbn = {978-3-642-40203-6},
+	shorttitle = {Current {Events}},
+	doi = {10.1007/978-3-642-40203-6_39},
+	abstract = {Computers plugged into power outlets leak identifiable information by drawing variable amounts of power when performing different tasks. This work examines the extent to which this side channel leaks private information about web browsing to an observer taking measurements at the power outlet. Using direct measurements of AC power consumption with an instrumented outlet, we construct a classifier that correctly identifies unlabeled power traces of webpage activity from a set of 51 candidates with 99\% precision and 99\% recall. The classifier rejects samples of 441 pages outside the corpus with a false-positive rate of less than 2\%. It is also robust to a number of variations in webpage loading conditions, including encryption. When trained on power traces from two computers loading the same webpage, the classifier correctly labels further traces of that webpage from either computer. We identify several reasons for this consistently recognizable power consumption, including system calls, and propose countermeasures to limit the leakage of private information. Characterizing the AC power side channel may help lead to practical countermeasures that protect user privacy from an untrustworthy power infrastructure.},
+	language = {en},
+	booktitle = {Computer {Security} – {ESORICS} 2013},
+	publisher = {Springer},
+	author = {Clark, Shane S. and Mustafa, Hossen and Ransford, Benjamin and Sorber, Jacob and Fu, Kevin and Xu, Wenyuan},
+	editor = {Crampton, Jason and Jajodia, Sushil and Mayes, Keith},
+	year = {2013},
+	keywords = {Background Process, Parasitic Modulation, Power Consumption, Side Channel, Threat Model},
+	pages = {700--717},
+	file = {Full Text PDF:/home/grizzly/Zotero/storage/YCPP3Y6C/Clark et al. - 2013 - Current Events Identifying Webpages by Tapping th.pdf:application/pdf},
+}
+
+@inproceedings{10.1145-2899007.2899009,
+author = {Conti, Mauro and Nati, Michele and Rotundo, Enrico and Spolaor, Riccardo},
+title = {Mind The Plug! Laptop-User Recognition Through Power Consumption},
+year = {2016},
+isbn = {9781450342834},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/2899007.2899009},
+doi = {10.1145/2899007.2899009},
+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.},
+booktitle = {Proceedings of the 2nd ACM International Workshop on IoT Privacy, Trust, and Security},
+pages = {37–44},
+numpages = {8},
+keywords = {internet of things, energy consumption., smart meter, user identification, smart building, machine learning, intrusion detection},
+location = {Xi'an, China},
+series = {IoTPTS '16}
+}
+
+@inproceedings{michalevsky2015powerspy,
+  title={Powerspy: Location tracking using mobile device power analysis},
+  author={Michalevsky, Yan and Schulman, Aaron and Veerapandian, Gunaa Arumugam and Boneh, Dan and Nakibly, Gabi},
+  booktitle={24th $\{$USENIX$\}$ Security Symposium ($\{$USENIX$\}$ Security 15)},
+  pages={785--800},
+  year={2015}
+}
+
+  
+
+
+
+@INPROCEEDINGS{4531926,
+
+  author={Nilsson, D. K. and Larson, U. E.},
+
+  booktitle={ICC Workshops - 2008 IEEE International Conference on Communications Workshops}, 
+
+  title={Secure Firmware Updates over the Air in Intelligent Vehicles}, 
+
+  year={2008},
+
+  volume={},
+
+  number={},
+
+  pages={380-384},
+
+  doi={10.1109/ICCW.2008.78}}
+
+@INPROCEEDINGS{8726545,
+
+  author={Kolehmainen, Antti},
+
+  booktitle={2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)}, 
+
+  title={Secure Firmware Updates for IoT: A Survey}, 
+
+  year={2018},
+
+  volume={},
+
+  number={},
+
+  pages={112-117},
+
+  doi={10.1109/Cybermatics_2018.2018.00051}}
+
+@INPROCEEDINGS{8855288,
+
+  author={Hernandez Jimenez, Jarilyn and Goseva-Popstojanova, Katerina},
+
+  booktitle={2019 2nd International Conference on Data Intelligence and Security (ICDIS)}, 
+
+  title={Malware Detection Using Power Consumption and Network Traffic Data}, 
+
+  year={2019},
+
+  volume={},
+
+  number={},
+
+  pages={53-59},
+
+  doi={10.1109/ICDIS.2019.00016}}
+
+  @inproceedings{pothukuchi2021maya,
+  title={Maya: Using formal control to obfuscate power side channels},
+  author={Pothukuchi, Raghavendra Pradyumna and Pothukuchi, Sweta Yamini and Voulgaris, Petros G and Schwing, Alexander and Torrellas, Josep},
+  booktitle={2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA)},
+  pages={888--901},
+  year={2021},
+  organization={IEEE}
+}
+
+@misc{sun2017revisiting,
+      title={Revisiting Unreasonable Effectiveness of Data in Deep Learning Era}, 
+      author={Chen Sun and Abhinav Shrivastava and Saurabh Singh and Abhinav Gupta},
+      year={2017},
+      eprint={1707.02968},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}