PIFT

Author:

Yoon Man-Ki1,Salajegheh Negin2,Chen Yin2,Christodorescu Mihai2

Affiliation:

1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

2. Qualcomm Research, Santa Clara, CA, USA

Abstract

Phones today carry sensitive information and have a great number of ways to communicate that data. As a result, malware that steal money, information, or simply disable functionality have hit the app stores. Current security solutions for preventing undesirable data leaks are mostly high-overhead and have not been practical enough for smartphones. In this paper, we show that simply monitoring just some instructions (only memory loads and stores) it is possible to achieve low overhead, highly accurate information flow tracking. Our method achieves 98% accuracy (0% false positive and 2% false negative) over DroidBench and was able to successfully catch seven real-world malware instances that steal phone number, location, and device ID using SMS messages and HTTP connections.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Reference23 articles.

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5. FlowDroid

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. FloVasion: Towards Detection of non-sensitive Variable Based Evasive Information-Flow in Android Apps;IETE Journal of Research;2020-03-02

2. LATCH;Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture;2019-10-12

3. A Dynamic Taint Analysis Framework Based on Entity Equipment;IEEE Access;2019

4. Distributed Computing Security Model Based on Type System;Applications and Techniques in Information Security;2018

5. T2Droid: A TrustZone-Based Dynamic Analyser for Android Applications;2017 IEEE Trustcom/BigDataSE/ICESS;2017-08

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