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Highlight and execute suspicious paths in Android malware

Mourad Leslous 1, 2
2 CIDRE - Confidentialité, Intégrité, Disponibilité et Répartition
CentraleSupélec, Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : The last years have known an unprecedented growth in the use of mobile devices especially smartphones. They became omnipresent in our daily life because of the features they offer. They allow the user to install third-party apps to achieve numerous tasks. Smartphones are mostly governed by the Android operating system. It is today installed on more than 80% of the smartphones. Mobile apps collect a huge amount of data such as email addresses, contact list, geolocation, photos and bank account credentials. Consequently, Android has become a favorable target for cyber criminals. Thus, understanding the issue, i.e., how Android malware operates and how to detect it, became an important research challenge. Android malware frequently tries to bypass static analysis using multiple techniques such as code obfuscation and dynamic code loading. To overcome these limitations, many analysis techniques have been proposed to execute the app and monitor its behavior at runtime. Nevertheless, malware developers use time and logic bombs to prevent the malicious code from executing except under certain circumstances. Therefore, more actions are needed to trigger it and monitor its behavior. Recent approaches try to automatically characterize the malicious behavior by identifying the most suspicious locations in the code and forcing them to execute. They strongly rely on the computation of application global control flow graphs (CFGs). However, these CFGs are incomplete because they do not take into consideration all types of execution paths. These approaches solely analyze the application code and miss the execution paths that occur when the application calls a framework method that in turn calls another application method. We propose in this dissertation a tool, GPFinder, that automatically exhibits execution paths towards suspicious locations in the code by computing global CFGs that include edges representing explicit and implicit interprocedural calls. It also gives key information about the analyzed application in order to understand how the suspicious code was injected into the application. To validate our approach, we use GPFinder to study a collection of 14,224 malware samples, and we evaluate that 72.69% of the samples have at least one suspicious code location which is only reachable through implicit calls. Triggering approaches mainly use one of the following strategies to run a specific portion of the application's code: the first approach heavily modifies the app to launch the targeted code without keeping the original behavioral context. The second approach generates the input to force the execution flow to take the desired path without modifying the app's code. However, it is sometimes hard to launch a specific code location just by fuzzing the input. For instance, when the application performs a hash on the input data and compares the result to a fixed string to decide which branch of the condition to take, the fuzzing program should reverse the hashing function, which is obviously a hard problem. We propose in this dissertation a tool, TriggerDroid, that has a twofold goal: force the execution of the suspicious code and keep its context close to the original one. It crafts the required framework events to launch the right app component and satisfies the necessary triggering conditions to take the desired execution path. To validate our approach, we led an experiment on a dataset of 135 malware samples from 71 different families. Results show that our approach needs more refinement and adaptation to handle special cases due to the highly diverse malware dataset that we analyzed. Finally, we give a feedback on the experiments we led on different malware datasets, and we explain our experimental process. Finally, we present the Kharon dataset, a collection of well documented Android malware that can be used to understand the malware landscape.
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Mourad Leslous. Highlight and execute suspicious paths in Android malware. Cryptography and Security [cs.CR]. Université Rennes 1, 2018. English. ⟨NNT : 2018REN1S090⟩. ⟨tel-02132759⟩

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