The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Peek-a-boo: i see your smart home activities, even encrypted!
Open accessAbstract
A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles through the sensors already embedded in these devices. Sensor data contains a lot of sensitive information about the user and devices. However, an attacker inside or near a smart home environment can potentially exploit the innate wireless medium used by these devices to exfiltrate sensitive information from the encrypted payload (i.e., sensor data) about the users and their activities, invading user privacy. With this in mind, in this work, we introduce a novel multi-stage privacy attack against user privacy in a smart environment. It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying the types of IoT devices, their states, and ongoing user activities in a cascading style by only passively sniffing the network traffic from smart home devices and sensors. The attack effectively works on both encrypted and unencrypted communications. We evaluate the efficiency of the attack with real measurements from an extensive set of popular off-the-shelf smart home IoT devices utilizing a set of diverse network protocols like WiFi, ZigBee, and BLE. Our results show that an adversary passively sniffing the traffic can achieve very high accuracy (above 90%) in identifying the state and actions of targeted smart home devices and their users. To protect against this privacy leakage, we also propose a countermeasure based on generating spoofed traffic to hide the device states and demonstrate that it provides better protection than existing solutions.
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Cites
DÏoT: A Federated Self-learning Anomaly Detection System for IoT
Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00080 · Cited by 663
Self-citationPeek-a-Boo, I Still See You: Why Efficient Traffic Analysis Countermeasures Fail
Dyer, K. P., Coull, S. E., Ristenpart, T., & Shrimpton, T. (2012) · 2012 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2012.28 · Cited by 456
Inferring the source of encrypted HTTP connections
Liberatore, M., & Levine, B. N. (2006) · Proceedings of the 13th ACM conference on Computer and communications security · doi:10.1145/1180405.1180437 · Cited by 413
Touching from a distance: website fingerprinting attacks and defenses
Cai, X., Zhang, X. C., Joshi, B., & Johnson, R. (2012) · Proceedings of the 2012 ACM conference on Computer and communications security · doi:10.1145/2382196.2382260 · Cited by 394
Statistical identification of encrypted Web browsing traffic
Sun, Q., Simon, D. R., Wang, Y.-M., Russell, W., Padmanabhan, V. N., & Qiu, L. (2005) · Proceedings 2002 IEEE Symposium on Security and Privacy · doi:10.1109/secpri.2002.1004359 · Cited by 377
Behavioral Fingerprinting of IoT Devices
Bezawada, B., Bachani, M., Peterson, J., Shirazi, H. M., Ray, I., & Ray, I. (2018) · Workshop on Attacks and Solutions in Hardware Security (ASHES) · doi:10.1145/3266444.3266452 · Cited by 300
Analyzing Android Encrypted Network Traffic to Identify User Actions
Conti, M., Mancini, L. V., Spolaor, R., & Verde, N. V. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2478741 · Cited by 266
Self-citationRen, J., Dubois, D. J., Choffnes, D., Mandalari, A. M., Kolcun, R., & Haddadi, H. (2019) · Proceedings of the Internet Measurement Conference · doi:10.1145/3355369.3355577 · Cited by 256
On Inferring Application Protocol Behaviors in Encrypted Network Traffic
Wright, C. V., Monrose, F., Masson, G. M., & Chan, P. (2006) · Cited by 222
Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping
Apthorpe, N., Huang, D. Y., Reisman, D., Narayanan, A., & Feamster, N. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0040 · Cited by 192
Cited by
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic
van Ede, T., Bortolameotti, R., Continella, A., Ren, J., Dubois, D. J., Lindorfer, M., Choffnes, D., van Steen, M., & Peter, A. (2020) · Proceedings 2020 Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.24412 · Cited by 339
Machine Learning-Powered Encrypted Network Traffic Analysis: A Comprehensive Survey
Shen, M., Ye, K., Liu, X., Zhu, L., Kang, J., Yu, S., Li, Q., & Xu, K. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208196 · Cited by 265
Post-Quantum Era Privacy Protection for Intelligent Infrastructures
Malina, L., Dzurenda, P., Ricci, S., Hajny, J., Srivastava, G., Matulevicius, R., Affia, A.-A. O., Laurent, M., Sultan, N. H., & Tang, Q. (2021) · IEEE Access · doi:10.1109/access.2021.3062201 · Cited by 111
Informing the Design of Privacy-Empowering Tools for the Connected Home
Seymour, W., Kraemer, M. J., Binns, R., & Van Kleek, M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376264 · Cited by 41
Metadata
- Authors
- Acar, A., Fereidooni, H., Abera, T., Sikder, A. K., Miettinen, M., Aksu, H., Conti, M., Sadeghi, A.-R., & Uluagac, S.
- Venue
- Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks
- Publisher
- ACM
- Published
- 2020-07-08
- Pages
- 207-218
- OpenAlex
- W2886320151
- Cited by
- 264
- References
- 24
- FWCI
- 17.03
- Type
- conference paper
- Language
- en
- Open access
- green
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- Acar, A., Fereidooni, H., Abera, T., Sikder, A. K., Miettinen, M., Aksu, H., Conti, M., Sadeghi, A.-R., & Uluagac, S. (2020). Peek-a-boo: i see your smart home activities, even encrypted! Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks. https://doi.org/10.1145/3395351.3399421
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