PROFILEDROID: MULTI-LAYER PROFILING OF ANDROID APPLICATIONS XUETAO WEI LORENZO GOMEZ UNIVERSITY OF CALIFORNIA, RIVERSIDE PROFESSOR IULIAN NEAMTIU
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1 PROFILEDROID: MULTI-LAYER PROFILING OF ANDROID APPLICATIONS XUETAO WEI LORENZO GOMEZ PROFESSOR IULIAN NEAMTIU PROFESSOR MICHALIS FALOUTSOS UNIVERSITY OF CALIFORNIA, RIVERSIDE
2 WE DEPEND ON SMARTPHONES MORE AND MORE source:mobilehealthnews.com FDA approves Mobisante s smartphone ultrasound app [Feb 2011] source:defenseindustrydaily.com US Army CSDA initiative (Connecting Soldiers to Digital Applications) to replace handheld radio + BLUE FORCE tracker + portable GPS + video feed ROVER
3 ANDROID IS A POPULAR SMARTPHONE PLATFORM Operating system share of smartphone sales (US) 850,000 Android phones activated every day [Google letter to investors, April 2012]
4 BUT WE DON T UNDERSTAND APP BEHAVIOR source:washingtonpost.com
5 THE ANDROID APP MARKET IS A JUNGLE Will this app drain my battery? Which radio is best for me? Will this app leak my photos? Will this app tell my friends that I m a moron?
6 FIRST STEP TO MAINTAINING THE JUNGLE Provide a low-cost method to profile the behavior of an app Given a few short executions: What did the app do? How does the app use resources? What entities does it communicate with? What was the app supposed to do? Where there conflicts? Why?
7 BENEFICIAL TO Application developers Assess performance and security implications Make better use of resources End users Enhance user control and improve experience Push developers to make better use of resources
8 ANDROID APPS Written in Java, compiled into Dalvik VM bytecode Packaged as name.apk Signed with developer s private key Essentially a.zip file containing:.dex bytecode file (similar to.class) Manifest file (XML): permissions Permission model Permissions last FOREVER! Shown before install All or nothing Permissions alone aren t enough to describe app behavior
9 DYNAMIC ANALYSIS WITH PROFILEDROID Android Device Desktop/laptop Monitoring Profiling Android Debugging Bridge Goal Multi-level profiling based on static and dynamic application analysis
10 MONITORING AND ANALYSIS WITH PROFILEDROID
11 PROFILING FRAMEWORK: MONITORING Trace File Trace File Trace File Capture 3 user traces, 5 minutes per app
12 PROFILING FRAMEWORK: MONITORING Trace File Playback Logs Playback original trace and collect logs
13 A QUICK REPLAY DEMO
14 PROFILING FRAMEWORK: MONITORING Trace File Logs Logs Repeat playback 10 times per user (5 in morning and 5 at night, per app) Total of 30 runs of each app to build profile
15 PROFILING FRAMEWORK: MULTI-LAYER ANALYSIS Application Static Logs Application Framework Libraries, Android Runtime Linux Kernel Android software stack User OS Network What metrics can be used to capture app behavior?
16 SELECTED APPS Category Social Games Music & Audio Media & Video Shopping Travel Health & Fitness Communication Sports Reference App Facebook Angry Birds, Angry Birds$$ Pandora, Shazam, Shazam$$ Youtube Amazon Gasbuddy Instant Heart Rate, Instant Heart Rate$$ Dolphin browser ESPN Dictionary.com, Dictionary.com$$ total 27 apps: 19 free, 8 paid Wide range of apps, spanning many categories Popular apps with >1,000,000 installs
17 STATIC LAYER Source: manifest & bytecode decompilation Permissions (shown at install) Internet Location (GPS or network) Phone Intents (not shown at install) Resource use without permission via deputy apps Application Application Framework Libraries, Android Runtime Linux Kernel Static User OS Network
18 STATIC LAYER ANALYSIS RESULTS App Internet GPS Camera Mic Bluetooth Telephony Facebook p p i* p Dictionary.com p i i Instant Heart Rate p p i i Shazam p p p Total (out of 27) p = use via permissions i = use via intents (deputy apps) *for version originally tested March 2012
19 USER LAYER Source: logcat, /dev/input/event Input devices and events Touchscreen Physical Buttons Accelerometer Compass Light proximity sensor Application Application Framework Libraries, Android Runtime Linux Kernel Static User OS Network
20 USER LAYER ANALYSIS RESULTS
21 OPERATING SYSTEM LAYER Source: strace System call categories Network sockets File system VM & IPC Enforces isolation Overhead: scheduling, idling, IPC Application Application Framework Libraries, Android Runtime Linux Kernel Static User OS Network
22 OPERATING SYSTEM LAYER RESULTS App Intensity (syscalls/sec) Filesystem (%) Network (%) VM & IPC (%) Misc (%) Tiny Flashlight Facebook 1, Amazon InstHeartRate
23 NETWORK LAYER Source: tcpdump (packets and content) App traffic Origin (app's website) CDN and Cloud Google 3 rd party: ads & tracking Application Application Framework Libraries, Android Runtime Linux Kernel Static User OS Network
24 NETWORK LAYER RESULTS App Intensity (bytes/ sec) In/out ratio Origin (%) CDN+ Cloud (%) Google (%) 3 rd party (%) HTTP/ HTTPS split (%) Tiny Flashlight /- AdvTaskKiller /8 AdvTaskKiller$$ Facebook 4, /77 Amazon 7, /1 InstHeartRate /14 InstHeartRate$$ /80
25 APPLICATION THUMBNAILS High usage Medium usage Low usage
26 READING BETWEEN THE LINES Free apps are not as free as we might think % higher system call intensity Dramatically higher network traffic (usually ads&tracking) Bad for your dataplan, your battery life, and your privacy VM-based isolation comes at a cost 64 87% of system calls are due to VM and IPC
27 READING BETWEEN THE LINES Apps talk to many servers spread across many top-level domains AngryBirds$$: 4 domains, AngryBirds free: 8 domains Weatherbug: 13 domains, Shazam: 13 domains Most network traffic is not encrypted Google traffic is predominant Except for Amazon and Facebook which have 0 (zero) Google traffic
28 FUTURE WORK Expand study to include more apps User profiles Study the variance across users Fully automate process Profiler as an app to run on the device Provide summary of usage on close
29 QUESTIONS?
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