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:
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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
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The majority of mobile data traffic is encrypted.
Data traffic is: - dynamic
- Homogeneous
- Evolving
Challenges
4. Extract features
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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
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
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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
• …
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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
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