A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge
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The document proposes a cloud-assisted method for securely grouping client devices at the network edge using wireless connections. It involves a 3-way handshake between clients and the cloud to establish a shared hashkey that is then used as a wireless SSID, allowing direct communication. Modeling of mobility traces shows this approach can improve effective communication range and session duration compared to direct device-to-device grouping. Infrastructure nodes identified via trace analysis may further enhance performance by pre-fetching handshake information. The method provides a basic component that could also be integrated with blockchain-based edge security solutions.
A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge
- 1. A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806 presented as CyberSciTech 2019 @ Fukuoka 2019/08/06 Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 1/9 1/9
- 2. Theoretical comm. capabilities at network edge • vast majority of (smartphone) apps today run online ◦ for example, see if you can find an offline news reader • yet there are at least 3 separate methods for P2P WiFi WiFi 3G Connectivity WebApp Cloud New paradigm Beacon Stuffing Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 1/9 1/9
- 3. Cloud-Backed Device Groups at NetEdge • devices should not trust each other when forming groups • a 3-way cloud-in-the-middle handshake is proposed • locally visible info is meaningless code, which makes sense only when run via the cloud • this paper: a hashkey as SSID Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 2/9 2/9
- 4. The 3-Way Handshake Cloud Client A Client B id, GPS, APs Seeking group! Check -in HashkeyHashkey >> SSID Grouping request Matching Check-in B s hashkey Hashkey >> SSID Direct comm. • both sides need to ① put up WiFi APs and ➁ run AP scans ◦ doable on Android? I have had mixed results, but the worst case is to toggle between AP and scan • the matching part is non-trivial: the cloud can pack ① GPS, ➁ WiFi signal level, ③ mobility trace/prediction, ④ sampling in dense crowds ... into a smart grouping candidate list Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 3/9 3/9
- 5. Session Modeling • the biggest impact comes from lazy beaconing, no practical way to make it better • always-on-alert mode for short periods of time is possible, but out of scope for now Cloud API Client A Effective range Walking path Lazy Beaconing WiFi Direct GroupingDiscovery Client B Local traffic Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 4/9 4/9
- 6. Trace-Based Modeling 0 1 2 3 4 5 6 7 8 9 Time order 20 40 60 80 100 120 140 160 180 200 distance 0 1 2 3 4 5 6 7 8 9 Time order 0 0.2 0.4 0.6 0.8 1 speed • mobility traces from crawdad (Statefair) • mined the trace and extracted pairwise encounters • each such encounter is extraced and made ready for easy replay Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 5/9 5/9
- 7. d2d vs d2i cases • gridify the trace, track counts for each grid cell to visualize the best spots for the infra nodes – those are slow mobility, high density grid cells Speed map Density mapMap for potential infrastructure hubs Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 6/9 6/9
- 8. Performance • infra-assisted models have poorer overall performance (few nodes), but can be used to further enhance the D2D performance • these are aggregate views, better visualizations are needed to see details 3 5 10 15 30 60 90 180 5 7 10 15 20 25 50 03 01 00 00 00 00 00 00 05 04 01 01 01 00 00 00 10 08 06 04 03 01 00 00 20 18 14 11 09 03 02 00 26 25 22 20 16 06 03 01 32 31 30 27 23 12 07 02 55 53 51 50 48 41 33 12 Effectiverange(meters) Session duration (seconds) 25 25 25 25 25 13 09 06 06 06 06 06 06 06 34 34 34 34 34 18 13 09 06 06 06 06 06 04 03 02 05 05 05 05 05 03 02 01 03 03 03 03 03 02 02 01 01 01 01 01 01 01 01 01 16 16 16 16 16 08 05 03 02 02 02 02 02 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 3 5 10 15 30 60 90 180 5 7 10 15 20 25 50 26 26 26 26 26 14 10 06 11 11 11 11 11 06 03 02 08 08 08 08 08 04 03 01 04 04 04 04 04 02 02 01 02 02 02 02 02 01 01 31 31 31 31 31 15 10 07 02 02 02 02 02 02 02 02 02 02 02 02 02 WiFiRange(meters) Session duration Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 7/9 7/9
- 9. Wrapup • the infra-assisted case is best described as much better performance for some of the clients • otherwise, infra nodes facilitate much faster handshake due to prefetch ◦ 3-way handshakes inevitably infer additional cost • side note ① already implemented and tried on Android and RPi • side note ➁ the proposal can be used as a component in a blockchain-based security at network edge01 01 M.Zhanikeev ”The Last Man Standing Technique for Proof-of-Location in IoT Infrastructures at Network Edge” Wireless Comm. and Mobile Comp. (2019) Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 8/9 8/9
- 10. That’s all, thank you ... Marat Zhanikeev – maratishe@gmail.com — PDF: bit.do/190806A Method for Cloud-Assisted Secure Wireless Grouping of Client Devices at Network Edge 9/9 9/9