FLOWPRINT: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic.pptx

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MahdiAyoub2

Presentation of Tesi di laurea triennale in Ingegneria Elettronica e Informatica, Università degli studi di Trieste.

Education

SUMMARY OF “FLOWPRINT: SEMI-SUPERVISED MOBILE-APP FINGERPRINTING
ON ENCRYPTED NETWORK TRAFFIC”
Tesi di laurea triennale in ingegneria elettronica e
informatica
ANNO ACCADEMICO 2022-2023
CANDIDATO: RELATORE:
MAHDI AYOUB PROF. ALBERTO BARTOLI
Thijs van Ede, Riccardo Bortolameotti, Andrea Continella, Jingjing Ren, Daniel J. Dubois, Martina Lindorfer, David Choffnes, Maarten van Steen, and
Andreas Peter. University of Twente, Bitdefender, UC Santa Barbara, Northeastern University, TU Wien.

Network secure
 Having a secure network is a critical requirement for large organizations,
since:
2
More
users
More
devices
More
apps
Harder
to
monitor
 Users can install, uninstall, and update apps
 Organizations need a solution that can work without prior
knowledge

Challenges of Data traffic
3
 The majority of mobile data traffic is encrypted.
 Data traffic is: - dynamic
- Homogeneous
- Evolving
Challenges

Extract features
4
Since the data
is encrypted, then extract
features:
 Temporal features
 Device features
 Destination features
 Size features
Only features that showed a high AMI score had been considered

Fingerprinting approach (I)
Clustering
Browser
isolation
Cross-
correlation
5
… . . .

Fingerprinting approach (II)
Fingerprinting
fingerprint
comparison
6
… . . .
Matching or updating
Example correlation graph for three apps

Evaluation
Dataset Accuracy of FlowPrint Accuracy of AppScanner
ReCon 0.9447 0.4284
Cross Platform 0.8923 0.5028
Andrubis (≥1000 flows) 0.8111 0.6005
7
Performance of FlowPrint approach in comparison to AppScanner in the app recognition
experiment.

Possible flaws
• Potential for evasion by using VPN or PROXY
• Timing of communication
• Low-traffic apps
• Invade users privacy
• …
8

Future application
 High accuracy in recognizing apps
 High precision in detecting previously unseen apps
 Possible solution to the problem of network security
 Promising tool for other uses
9

Thank you for your time
Grazie per l'attenzione
10

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FLOWPRINT: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic.pptx

1. SUMMARY OF “FLOWPRINT: SEMI-SUPERVISED MOBILE-APP FINGERPRINTING ON ENCRYPTED NETWORK TRAFFIC” Tesi di laurea triennale in ingegneria elettronica e informatica ANNO ACCADEMICO 2022-2023 CANDIDATO: RELATORE: MAHDI AYOUB PROF. ALBERTO BARTOLI Thijs van Ede, Riccardo Bortolameotti, Andrea Continella, Jingjing Ren, Daniel J. Dubois, Martina Lindorfer, David Choffnes, Maarten van Steen, and Andreas Peter. University of Twente, Bitdefender, UC Santa Barbara, Northeastern University, TU Wien.

2. Network secure  Having a secure network is a critical requirement for large organizations, since: 2 More users More devices More apps Harder to monitor  Users can install, uninstall, and update apps  Organizations need a solution that can work without prior knowledge

3. Challenges of Data traffic 3  The majority of mobile data traffic is encrypted.  Data traffic is: - dynamic - Homogeneous - Evolving Challenges

4. Extract features 4 Since the data is encrypted, then extract features:  Temporal features  Device features  Destination features  Size features Only features that showed a high AMI score had been considered

5. Fingerprinting approach (I) Clustering Browser isolation Cross- correlation 5 … . . .

6. Fingerprinting approach (II) Fingerprinting fingerprint comparison 6 … . . . Matching or updating Example correlation graph for three apps

7. Evaluation Dataset Accuracy of FlowPrint Accuracy of AppScanner ReCon 0.9447 0.4284 Cross Platform 0.8923 0.5028 Andrubis (≥1000 flows) 0.8111 0.6005 7 Performance of FlowPrint approach in comparison to AppScanner in the app recognition experiment.

8. Possible flaws • Potential for evasion by using VPN or PROXY • Timing of communication • Low-traffic apps • Invade users privacy • … 8

9. Future application  High accuracy in recognizing apps  High precision in detecting previously unseen apps  Possible solution to the problem of network security  Promising tool for other uses 9

10. Thank you for your time Grazie per l'attenzione 10

FLOWPRINT: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic.pptx

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