Blocking Without Breaking: Identification and Mitigation of Non-Essential IoT Traffic

[1] IoT Analytics, “IoT 2019 in review: The 10 most relevant IoT developments of the year,” https://iot-analytics.com/iot-2019-in-review/, [Online; accessed Nov. 2020]. Search in Google Scholar

[2] J. Ren, D. J. Dubois, D. Choffnes, A. M. Mandalari, R. Kolcun, and H. Haddadi, “Information exposure from consumer IoT devices: A multidimensional, network-informed measurement approach,” in Proceedings of the Internet Measurement Conference, 2019. Search in Google Scholar

[3] H. Mohajeri Moghaddam, G. Acar, B. Burgess, A. Mathur, D. Y. Huang, N. Feamster, E. W. Felten, P. Mittal, and A. Narayanan, “Watching you watch: The tracking ecosystem of over-the-top TV streaming devices,” in CCS’19, 2019. Search in Google Scholar

[4] J. Varmarken, H. Le, A. Shuba, A. Markopoulou, and Z. Shafiq, “The TV is smart and full of trackers: Measuring smart TV advertising and tracking,” PETS’20, vol. 2020, no. 2, pp. 129–154, 2020. Search in Google Scholar

[5] S. J. Saidi, A. M. Mandalari, R. Kolcun, H. Haddadi, D. J. Dubois, D. Choffnes, G. Smaragdakis, and A. Feldmann, “A haystack full of needles: Scalable detection of IoT devices in the wild,” in IMC’20, 2020, pp. 87–100. Search in Google Scholar

[6] Pi-Hole: A black hole for Internet advertisements, https://pi-hole.net/, [Online; accessed Nov. 2020]. Search in Google Scholar

[7] ico., “Principle (c): Data minimisation,” https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/principles/data-minimisation/, [Online; accessed Nov. 2020]. Search in Google Scholar

[8] ——, “Principle (b): Purpose limitation,” https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/principles/purpose-limitation/, [Online; accessed Mar. 2021]. Search in Google Scholar

[9] IFTTT, Inc., “IFTTT helps every thing work better together,” https://ifttt.com, [Online; accessed Mar. 2021]. Search in Google Scholar

[10] SmartThings, Inc., “SmartThings: One simple home system. A world of possibilities.” https://www.smartthings.com, [Online; accessed Mar. 2021]. Search in Google Scholar

[11] Roku Inc., “Roku Developer Documentation: Development Environment Overview,” https://sdkdocs.roku.com/display/sdkdoc/Development+Environment+Overview, [Online; accessed Feb. 2021]. Search in Google Scholar

[12] Amazon.com Inc., “Developer Tools Menu (Fire TV),” https://developer.amazon.com/docs/fire-tv/developer-tools.html, [Online; accessed Feb. 2021]. Search in Google Scholar

[13] R. Trimananda, J. Varmarken, A. Markopoulou, and B. Demsky, “Packet-level signatures for smart home device events,” in NDSS’20, 2020. Search in Google Scholar

[14] A. Acar, H. Fereidooni, T. Abera, A. K. Sikder, M. Miettinen, H. Aksu, M. Conti, A.-R. Sadeghi, and S. Uluagac, “Peek-a-Boo: I see your smart home activities, even encrypted!” in WiSec’20, 2020, p. 207–218. [Online]. Available: https://doi.org/10.1145/3395351.3399421 Search in Google Scholar

[15] Pi-Hole LLC Blocking Mode, https://docs.pi-hole.net/ftldns/blockingmode, [Online; accessed Nov. 2020]. Search in Google Scholar

[16] WaLLy3K, “The big blocklist collection,” https://firebog.net, [Online; accessed Nov. 2020]. Search in Google Scholar

[17] Mother of All AD-BLOCKING, “The big blocklist collection,” https://forum.xda-developers.com/showthread.php?t=1916098, [Online; accessed Nov. 2020]. Search in Google Scholar

[18] Kromtech Alliance Corp, “Stopad for TV,” https://stopad.io/, [Online; accessed Nov. 2020]. Search in Google Scholar

[19] Ashish Kumar Singh and V. Potdar, “Blocking online advertising - a state of the art,” in 2009 IEEE International Conference on Industrial Technology, Feb 2009, pp. 1–10. Search in Google Scholar

[20] Consumer Reports, “Home security cameras from top brands lack basic digital security measures,” https://www.consumerreports.org/wireless-security-cameras/home-security-cameras-from-top-brands-lack-basic-digital-security-measures/, [Online; accessed Nov. 2020]. Search in Google Scholar

[21] A. Subahi and G. Theodorakopoulos, “Ensuring compliance of IoT devices with their privacy policy agreement,” in FiCloud’18. IEEE, 2018, pp. 100–107. Search in Google Scholar

[22] C. Welch, “I guess I have to watch ads everywhere on my $1,500 LG TV now,” https://www.theverge.com/tldr/2021/3/10/22323790/lg-oled-tv-commercials-content-store, [Online; accessed Mar. 2021]. Search in Google Scholar

[23] R. Nithyanand, S. Khattak, M. Javed, N. Vallina-Rodriguez, M. Falahrastegar, J. E. Powles, E. De Cristofaro, H. Haddadi, and S. J. Murdoch, “Adblocking and counter blocking: A slice of the arms race,” in 6th USENIX Workshop on Free and Open Communications on the Internet (FOCI 16), 2016. Search in Google Scholar

[24] E. Lear, R. Droms, and D. Romascanu, “RFC 8520: Manufacturer usage description specification,” 2019. Search in Google Scholar

[25] A. Hamza, D. Ranathunga, H. H. Gharakheili, M. Roughan, and V. Sivaraman, “Clear as MUD: Generating, validating and applying IoT behavioral profiles,” in SIGCOMM ’18 Workshop on IoT S&P, 2018. Search in Google Scholar

[26] C. Haar and E. Buchmann, “FANE: A firewall appliance for the smart home,” in FedCSIS ’19, 2019, pp. 449–458. Search in Google Scholar

[27] A. K. Simpson, F. Roesner, and T. Kohno, “Securing vulnerable home IoT devices with an in-hub security manager,” in PerCom ’17 Workshops, 2017, pp. 551–556. Search in Google Scholar

[28] N. Gupta, V. Naik, and S. Sengupta, “A firewall for internet of things,” in 2017 9th International Conference on Communication Systems and Networks (COMSNETS), 2017, pp. 411–412. Search in Google Scholar

[29] J. Habibi, D. Midi, A. Mudgerikar, and E. Bertino, “Heimdall: Mitigating the internet of insecure things,” IEEE Internet of Things Journal, vol. 4, no. 4, pp. 968–978, 2017. Search in Google Scholar

[30] E. Lastdrager, C. Hesselman, J. Jansen, and M. Davids, “Protecting home networks from insecure IoT devices,” in NOMS 2020, 2020, pp. 1–6. Search in Google Scholar

[31] ShieldIOT, https://shieldiot.io/, [Online; accessed Nov. 2020]. Search in Google Scholar

[32] Fingbox, https://www.fing.com/, [Online; accessed Nov. 2020]. Search in Google Scholar

[33] Bitdefender, https://www.bitdefender.com/iot/, [Online; accessed Nov. 2020]. Search in Google Scholar

[34] D. Y. Huang, N. Apthorpe, F. Li, G. Acar, and N. Feamster, “IoT inspector: Crowdsourcing labeled network traffic from smart home devices at scale,” Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., vol. 4, no. 2, Jun. 2020. Search in Google Scholar

[35] N. Apthorpe, D. Reisman, and N. Feamster, “A smart home is no castle: Privacy vulnerabilities of encrypted IoT traffic,” DAT’16, 2016. [Online]. Available: http://arxiv.org/abs/1705.06805 Search in Google Scholar

[36] H. Tahaei, F. Afifi, A. Asemi, F. Zaki, and N. B. Anuar, “The rise of traffic classification in IoT networks: A survey,” Journal of Network and Computer Applications, vol. 154, p. 102538, 2020. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1084804520300126 Search in Google Scholar

[37] Y. Meidan, M. Bohadana, A. Shabtai, J. D. Guarnizo, M. Ochoa, N. O. Tippenhauer, and Y. Elovici, “ProfilIoT: A machine learning approach for IoT device identification based on network traffic analysis,” in SAC ’17, 2017, pp. 506–509. [Online]. Available: https://doi.org/10.1145/3019612.3019878 Search in Google Scholar

[38] M. Miettinen, S. Marchal, I. Hafeez, N. Asokan, A. Sadeghi, and S. Tarkoma, “IoT SENTINEL: Automated device-type identification for security enforcement in IoT,” in ICDCS’17, 2017, pp. 2177–2184. Search in Google Scholar

[39] I. Hafeez, M. Antikainen, A. Y. Ding, and S. Tarkoma, “IoT-KEEPER: Detecting malicious IoT network activity using online traffic analysis at the edge,” IEEE Transactions on Network and Service Management, vol. 17, no. 1, pp. 45–59, 2020. Search in Google Scholar

[40] N. Apthorpe, D. Y. Huang, D. Reisman, A. Narayanan, and N. Feamster, “Keeping the smart home private with smart(er) IoT traffic shaping,” PETS, vol. 2019, no. 3, pp. 128 – 148, 2019. [Online]. Available: https://content.sciendo.com/view/journals/popets/2019/3/article-p128.xml Search in Google Scholar

[41] A. Alshehri, J. Granley, and C. Yue, “Attacking and protecting tunneled traffic of smart home devices,” in CODASPY ’20, 2020, p. 259–270. Search in Google Scholar

• Fortified Multi-Party Computation: Taking Advantage of Simple Secure Hardware Modules

• Secure integer division with a private divisor

• SwapCT: Swap Confidential Transactions for Privacy-Preserving Multi-Token Exchanges

• Multiparty Homomorphic Encryption from Ring-Learning-with-Errors

• Residue-Free Computing

• Differentially Private Naïve Bayes Classifier Using Smooth Sensitivity

• CrowdNotifier: Decentralized Privacy-Preserving Presence Tracing

• Blocking Without Breaking: Identification and Mitigation of Non-Essential IoT Traffic

• ZKSENSE: A Friction-less Privacy-Preserving Human Attestation Mechanism for Mobile Devices

• You May Also Like... Privacy: Recommendation Systems Meet PIR

• Domain name encryption is not enough: privacy leakage via IP-based website fingerprinting

• Editors’ Introduction

• Mercurial Signatures for Variable-Length Messages

• Supervised Authorship Segmentation of Open Source Code Projects

• HashWires: Hyperefficient Credential-Based Range Proofs

• Gage MPC: Bypassing Residual Function Leakage for Non-Interactive MPC

• Less is More: A privacy-respecting Android malware classifier using federated learning

• Privacy-Preserving Approximate k-Nearest-Neighbors Search that Hides Access, Query and Volume Patterns

• Unifying Privacy Policy Detection

• “I would have to evaluate their objections”: Privacy tensions between smart home device owners and incidental users

• Managing Potentially Intrusive Practices in the Browser: A User-Centered Perspective

• DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing

• LogPicker: Strengthening Certificate Transparency Against Covert Adversaries

• Oblivious DNS over HTTPS (ODoH): A Practical Privacy Enhancement to DNS

• Private Stream Aggregation with Labels in the Standard Model

• SoK: Privacy-Preserving Computation Techniques for Deep Learning

• Privacy Preference Signals: Past, Present and Future

• “We, three brothers have always known everything of each other”: A Cross-cultural Study of Sharing Digital Devices and Online Accounts

• SoK: Efficient Privacy-preserving Clustering