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Malware Collusions Karim O. Elish, Danfeng (Daphne) Yao, and - PowerPoint PPT Presentation

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On the Need of Precise Inter-App ICC Classification for Detecting Android Malware Collusions Karim O. Elish, Danfeng (Daphne) Yao, and Barbara G. Ryder Department of Computer Science Virginia Tech May 21, 2015 Problem and Motivation Malware

  1. On the Need of Precise Inter-App ICC Classification for Detecting Android Malware Collusions Karim O. Elish, Danfeng (Daphne) Yao, and Barbara G. Ryder Department of Computer Science Virginia Tech May 21, 2015

  2. Problem and Motivation Malware Threat to Mobile OS [CIO Insight, 2012] • Threats • Abuse of system resources • Leak of sensitive data 2

  3. Malware Evolution: App Collusion • Collusion refers to the scenario where two or more apps interact with each other to perform malicious tasks – Directly: Android Intent-based inter-component communication (ICC) – Indirectly: shared files,…etc. • Existing solutions assume the attack model of a single malicious app, and thus cannot detect collusion An example of permissions and operations being split between colluding apps 3

  4. Existing Solutions & Limitations Solution Analysis Collusion Limitation Type Classification Policies XManDroid - Dynamic Permissions - High false alerts [NDSS’12] - Pair of apps Combinations - Scalability - Circumvented by long chain of collusion CHEX - Static No - Vulnerability analysis only [CCS’12] - Single app - Can not track data via ICC ComDroid - Static No - Vulnerability analysis only [MobiSys’11] - Can ’t track path from public component - Single app to critical operation -> false alerts Epicc - Static No - Same as ComDroid [USENIX13] - Single app Amandroid - Static No - No analysis/info on how to connect ICC [CCS’14] - Single app among apps 4

  5. Our Goal To characterize ICC and to experimentally demonstrate the difficulties and technical challenges associated with app collusion detection 5

  6. Static Characterization of ICC • We developed a static analysis tool ( ICC Map) to model the Intent-based ICC of Android apps • ICC Map captures all types of communication (internal and external) of an app – <ICCName k , sourceComponent k , targetComponent k , typeOfCommunication k >, Partial ICC map for “abc.ssd.TrafficInfoCheck” app 6

  7. Experimental Evaluation • We statically construct ICC Maps of 2,644 benign apps collected from Google Play • The objectives of the study: 1. How often do benign apps perform inter-app communications with other apps? 2. How effective is the existing collusion detection solution (namely XManDroid) in terms of false positive rate? 7

  8. Experimental Evidence 9 8 7 # of App Pairs 6 5 4 3 2 1 0 Policy # 8 Policy # 9 Policy # 10 Policy # 11 Existing collusion detection solution (XManDroid) triggers a large number of false alerts in benign app pairs (11 out of 20 benign app pairs are misclassified as collusion) Subset of XManDroid’s policy 8

  9. Collusion Detection: Challenges Challenges & Problems: • Many benign apps interacts with other apps • Analysis scalability with minimum complexity • Existing solution produces large number of false alerts Solution for detecting malware collusion needs: • To be able to characterize the context associated with communication channels with fine granularity • To define security policies to classify benign ICC flows from colluding ones with low false alerts • To be scalable to a large number of apps (e.g., tens of thousands of apps) 9

  10. Improving Collusion Detection with Deep Cross- App Data-flow Analysis • ICC involving non-sensitive data or request should NOT be alerted, despite of the sensitive permission combination (ACCESS_FINE_LOCATION and INTERNET) • We argue that there is a need for a more practical solution based on in-depth static flow analysis that captures the context associated with the ICC 10

  11. Improving Collusion Detection with Deep Cross- App Data-flow Analysis Deep static data-flow analysis in both source and destination apps (requires new program analysis algorithms and data structures) 11

  12. Conclusions & Future Work • This work demonstrates experimentally the challenges to detect malware collusion • Future work – We plan to utilize our ICC Map for app collusion detection and define more fine-grained security policies to reduce false alerts • App collusion analysis has many useful applications: – Enable app store to perform massive screening of the apps to detect possible collusion – Enable the user to check apps before installing to detect possible collusion with the pre-installed apps 12

  13. Thank You… Questions? 13

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