Anomaly Detection using String Analysis for Android Malware Detection - CISIS 2013

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Presentation at CISIS 2013 International conference of the paper: Anomaly Detection using String Analysis for Android Malware Detection

Technology Business

Anomaly Detection using
String Analysis for
Android Malware Detection
Borja Sanz
Igor Santos
Xabier Ugarte-Pedrero
Carlos Laorden
Javier Nieves
Pablo G. Bringas

In the past 10 years
mobile phones
have evolved

In fact, now they are called
smartphones

1955
Android devices activated
since
I started this presentation

1972
Android devices activated
since
I started this presentation

2006
Android devices activated
since
I started this presentation

2023
Android devices activated
since
I started this presentation

Emails
Pictures
Vídeos
IM
Web history
Documents
Geoposition
Movements
Microphone
Camera
Call history
Wallet

Would you
lend me
your
smartphone?
(no doubts)
Have you
ever lost a
cellphone?
(feeling?)

We carry
sensitive information
in our pockets

What about
security?
is there
malware
in Android?

Malwareis one of the most
important issues in Android

But I onlydownload apks
from the market…

Anomaly detection
method for
Android malware
detection

Benign software
(goodware)
Malicious software
(malware)

Malware dataset
AV evaluation
Evaluation of samples based on the
detected AV
Threshold defintion
Remove duplicate samples
1,938
samples
333
samples

Strings
const-string v6, "TEST CONSTANT"
const-string v6, "THE VARIABLE"

THE VARIABLE TEST CONSTANT( , , )
S1 ( 0 , 1 , 1 )
S2 ( 1 , 0 , 0 )

?
?
Anomaly detection
d
d < threshold?
> threshold?

Manhattan distance
Euclidean distance
Cosine distance

Minimum distance
Maximum distance
Mean distance

Minimum
distance
Maximum
distance
Mean
distance
Manhattan
distance
Euclidean
Distance
Cosine
distance

Minimum
distance
Maximum
distance
Mean
distance
Manhattan
distance
Euclidean
distance
10
thresholds

TPR, FPR, Accuracy
Manhattan Euclidean Cosine

Area Under the Curve
Manhattan Euclidean Cosine
0.88

Only consider
bening samples to
measure distances

References
1. Androides: http://fondosbonitos.com/file/663/2560x1440/crop/androides.jpg
2. Nexus phone: http://p.playserver1.
com/ProductImages/5/5/7/3/1/4/5/2/25413755_700x700min_1.jpg
3. Apple Hacker: http://techbeat.com/wp-content/uploads/2012/09/Apple-Hacker-
Heads-to-Twitter.jpg
4. Botnet costume: http://jon.oberheide.org/blog/wp-
content/uploads/2007/01/costume2.jpg
5. Zombie bird: http://payload66.cargocollective.com/1/1/49299/3633335/an2.jpg
6. Toy Story command rescue: http://img.rakuten.
com/PIC/4498966/0/1/500/4498966.jpg
7. Back to the future car: http://www.wallpaperfo.
com/Abstract/High_definition/cars_high_definition_back_to_the_future_delorean_dmc1
2_1920x1080_wallpaper_518/download_2560x1440
8. Long way: http://hakimiyetimilliye.org/wp-content/uploads/2013/02/%D8%B7%D8%
B1%D9%8A%D9%82-1.jpg

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Anomaly Detection using String Analysis for Android Malware Detection - CISIS 2013

1. Anomaly Detection using String Analysis for Android Malware Detection Borja Sanz Igor Santos Xabier Ugarte-Pedrero Carlos Laorden Javier Nieves Pablo G. Bringas

2. In the past 10 years mobile phones have evolved

3. In fact, now they are called smartphones

4. 108 million iPhones sold

5. 1955 Android devices activated since I started this presentation

6. 1972 Android devices activated since I started this presentation

7. 2006 Android devices activated since I started this presentation

8. 2023 Android devices activated since I started this presentation

9. 1.5 million every day

10.

11. It is a revolution

12. Emails Pictures Vídeos IM Web history Documents Geoposition Movements Microphone Camera Call history Wallet

13. Let me ask you…

14. Would you lend me your smartphone? (no doubts) Have you ever lost a cellphone? (feeling?)

15. We carry sensitive information in our pockets

16. What about security? is there malware in Android?

17. Malware in Android

18. Malwareis one of the most important issues in Android

19. But I onlydownload apks from the market…

20.

21. New detection methods are necessary

22. Anomaly detection method for Android malware detection

23. 1Dataset creation

24. Benign software (goodware) Malicious software (malware)

25.

26. Malware dataset AV evaluation Evaluation of samples based on the detected AV Threshold defintion Remove duplicate samples 1,938 samples 333 samples

27. Goodware dataset game 333 samples

28. 2Feature selection

29. Strings const-string v6, "TEST CONSTANT" const-string v6, "THE VARIABLE"

30. THE VARIABLE TEST CONSTANT( , , ) S1 ( 0 , 1 , 1 ) S2 ( 1 , 0 , 0 )

31. t1 t2 t3 D1 D2 D3 D9 D7 D5 D6

32. 3Anomaly Detection

33. ? ? Anomaly detection d d < threshold? > threshold?

34. Manhattan distance Euclidean distance Cosine distance

35. d d Anomaly detection

36. Minimum distance Maximum distance Mean distance

37. Minimum distance Maximum distance Mean distance Manhattan distance Euclidean Distance Cosine distance

38. 10 different thresholds

39. Anomaly detection d d > threshold? > threshold?

40. min max

41. Minimum distance Maximum distance Mean distance Manhattan distance Euclidean distance 10 thresholds

42. 5-fold Cross-validation

43. 4Results

44. TPR, FPR, Accuracy Manhattan Euclidean Cosine

45. Area Under the Curve Manhattan Euclidean Cosine 0.88

46. Only consider bening samples to measure distances

47. Future work

48. Other feature sets

49. Other distances and selection rules

50. Dynamic analysis

51. We still have a long way to go

52. References 1. Androides: http://fondosbonitos.com/file/663/2560x1440/crop/androides.jpg 2. Nexus phone: http://p.playserver1. com/ProductImages/5/5/7/3/1/4/5/2/25413755_700x700min_1.jpg 3. Apple Hacker: http://techbeat.com/wp-content/uploads/2012/09/Apple-Hacker- Heads-to-Twitter.jpg 4. Botnet costume: http://jon.oberheide.org/blog/wp- content/uploads/2007/01/costume2.jpg 5. Zombie bird: http://payload66.cargocollective.com/1/1/49299/3633335/an2.jpg 6. Toy Story command rescue: http://img.rakuten. com/PIC/4498966/0/1/500/4498966.jpg 7. Back to the future car: http://www.wallpaperfo. com/Abstract/High_definition/cars_high_definition_back_to_the_future_delorean_dmc1 2_1920x1080_wallpaper_518/download_2560x1440 8. Long way: http://hakimiyetimilliye.org/wp-content/uploads/2013/02/%D8%B7%D8% B1%D9%8A%D9%82-1.jpg

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