References of "Cavallaro, Lorenzo"
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See detailEuphony: Harmonious Unification of Cacophonous Anti-Virus Vendor Labels for Android Malware
Hurier, Médéric UL; Suarez-Tangil, Guillermo; Dash, Santanu Kumar et al

in MSR 2017 (2017, May 21)

Android malware is now pervasive and evolving rapidly. Thousands of malware samples are discovered every day with new models of attacks. The growth of these threats has come hand in hand with the ... [more ▼]

Android malware is now pervasive and evolving rapidly. Thousands of malware samples are discovered every day with new models of attacks. The growth of these threats has come hand in hand with the proliferation of collective repositories sharing the latest specimens. Having access to a large number of samples opens new research directions aiming at efficiently vetting apps. However, automatically inferring a reference ground-truth from those repositories is not straightforward and can inadvertently lead to unforeseen misconceptions. On the one hand, samples are often mis-labeled as different parties use distinct naming schemes for the same sample. On the other hand, samples are frequently mis-classified due to conceptual errors made during labeling processes. In this paper, we analyze the associations between all labels given by different vendors and we propose a system called EUPHONY to systematically unify common samples into family groups. The key novelty of our approach is that no a-priori knowledge on malware families is needed. We evaluate our approach using reference datasets and more than 0.4 million additional samples outside of these datasets. Results show that EUPHONY provides competitive performance against the state-of-the-art. [less ▲]

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See detailUnderstanding Android App Piggybacking
Li, Li UL; Li, Daoyuan UL; Bissyande, Tegawendé François D Assise UL et al

Poster (2017, May)

The Android packaging model offers adequate opportunities for attackers to inject malicious code into popular benign apps, attempting to develop new malicious apps that can then be easily spread to a ... [more ▼]

The Android packaging model offers adequate opportunities for attackers to inject malicious code into popular benign apps, attempting to develop new malicious apps that can then be easily spread to a large user base. Despite the fact that the literature has already presented a number of tools to detect piggybacked apps, there is still lacking a comprehensive investigation on the piggybacking processes. To fill this gap, in this work, we collect a large set of benign/piggybacked app pairs that can be taken as benchmark apps for further investigation. We manually look into these benchmark pairs for understanding the characteristics of piggybacking apps and eventually we report 20 interesting findings. We expect these findings to initiate new research directions such as practical and scalable piggybacked app detection, explainable malware detection, and malicious code location. [less ▲]

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See detailUnderstanding Android App Piggybacking: A Systematic Study of Malicious Code Grafting
Li, Li UL; Li, Daoyuan UL; Bissyande, Tegawendé François D Assise UL et al

in IEEE Transactions on Information Forensics & Security (2017)

The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has ... [more ▼]

The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has produced approaches and tools to identify piggybacked apps, the literature lacks a comprehensive investigation into such phenomenon. We fill this gap by 1) systematically building a large set of piggybacked and benign apps pairs, which we release to the community, 2) empirically studying the characteristics of malicious piggybacked apps in comparison with their benign counterparts, and 3) providing insights on piggybacking processes. Among several findings providing insights, analysis techniques should build upon to improve the overall detection and classification accuracy of piggybacked apps, we show that piggybacking operations not only concern app code but also extensively manipulates app resource files, largely contradicting common beliefs. We also find that piggybacking is done with little sophistication, in many cases automatically, and often via library code. [less ▲]

Detailed reference viewed: 233 (24 UL)