Santanu Kumar Dash

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The Android ecosystem has witnessed a surge in malware, which not only puts mobile devices at risk but also increases the burden on malware analysts assessing and categorizing threats. In this paper, we show how to use machine learning to automatically classify Android malware samples into families with high accuracy, while observing only their runtime(More)
Malware evolves perpetually and relies on increasingly so- phisticated attacks to supersede defense strategies. Data-driven approaches to malware detection run the risk of becoming rapidly antiquated. Keeping pace with malware requires models that are periodically enriched with fresh knowledge, commonly known as <i>retraining</i>. In this work, we propose(More)
With more than two million applications, Android marketplaces require automatic and scalable methods to efficiently vet apps for the absence of malicious threats. Recent techniques have successfully relied on the extraction of lightweight syntactic features suitable for machine learning classification, but despite their promising results, the very nature of(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(More)
Amorphous Data Parallelism has proven to be a suitable vehicle for implementing concurrent graph algorithms effectively on multi-core architectures. In view of the growing complexity of graph algorithms for information analysis, there is a need to facilitate modular design techniques in the context of Amorphous Data Parallelism. In this paper, we(More)