How 1 Million App Calls can Tell you a Bit About Malware – Part 2

In my previous blog post, I described some of my findings regarding malicious mobile apps. In summary, I observed that there are POSIX abstractions, which are popular only for malicious apps. The findings were derived from a study that I did with some colleagues on POSIX (Portable Operating System Interface) abstractions. Recall that, a part of our study involved the examination of the POSIX calls that are used by both benign Android applications (~1 million) coming from the Google Play Store, and malicious Android applications (about 1260 of them) taken from a well-known dataset, which you can download from here.

Figure 1: Potentially Malicious Apps. The identification was based on an SVM Model.

Figure 1: Potentially Malicious Apps. The identification was based on an SVM Model.

Table 2: Indicative potentially malicious apps classified by the SVM model. These apps were identified as malicious by more than 15 antiviruses.

Table 2: Indicative potentially malicious apps classified by the SVM model. These apps were identified as malicious by more than 15 antiviruses.

We performed a further analysis on these results to check if we can create a more robust filter to detect malicious apps, than the simple filter described in my previous post (recall that this filter was based on the three most unpopular abstractions among benign applications and at the same time popular among malicious ones). Our attempt involved the following: we fed a set of benign apps (the 500 most popular apps of the Google Store) and the aforementioned dataset of the malicious apps, to an SVM (Support Vector Machine), a binary classifier that builds a model based on given features (abstractions in our case) to separate the two cases. In this way the classifier can classify a new app as malicious or not. By using the model on the same set of apps that we examined in the previous case, 1283 apps were identified as suspicious. Based on the antiviruses provided by the VirusTotal website again, we found that from these apps, 232 (18%) are potentially malicious. Even if the approach seems less robust than the previous one, Figure 1, illustrates that there are more cases of apps that were indicated as malicious by more than one antivirus. Table 1, presents applications that were filtered out by the SVM model, and were identified as malicious by more than 15 antiviruses.

Figure 2: Potentially Malicious Apps. The identification was based on the obfuscated libraries.

Figure 2: Potentially Malicious Apps. The identification was based on the obfuscated libraries.

Table 2: Indicative potentially malicious apps containing obfuscated libraries. These apps were identified as malicious by more than 22 antiviruses.

Table 2: Indicative potentially malicious apps containing obfuscated libraries. These apps were identified as malicious by more than 22 antiviruses.

Through our experiments, we came across a number of Android apps that included obfuscated libraries (991 apps in total). Given the fact that obfuscation techniques have been extensively encountered while analyzing Android malware, we decided to examine all the apps that contained such libraries by using the 54 antiviruses of the VirusTotal website. Surprisingly, almost half of the apps (481 in total — 48.53%) were classified as suspicious. An interesting observation is that the majority of these apps were indicated as potentially malicious by a large number of antiviruses — see Figure 2. Table 2, presents indicative apps that were identified as malicious by more than 22 antiviruses.

As it is clear, a malware detector cannot be based solely on observations like the aforementioned ones. However, such findings could be useful for the development of complex filters that can help find malicious software.

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About Dimitris Mitropoulos

Dimitris Mitropoulos is a software and security engineer at the Greek Research and Technology Network (GRNET) and a research associate at the Athens University of Economics and Business. Dr. Mitropoulos holds a BSc in Informatics and Telecommunications from the National and Kapodistrian University of Athens, an MSc and a PhD in Cyber Security from the Athens University of Economics and Business. He has also been a postdoctoral researcher at the Network Security Laboratory (NSL) of Columbia University in the City of New York. He has worked in the industry as a software engineer and has been involved in several EU and US funded R&D projects. His research interests include application security, systems security, software evolution and software engineering. He a member of ACM, IEEE, OWASP and SysSec.

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