Annamalai Narayanan

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In this paper, we present subgraph2vec, a novel approach for learning latent representations of rooted subgraphs from large graphs inspired by recent advancements in Deep Learning and Graph Kernels. These latent representations encode semantic substructure dependencies in a continuous vector space, which is easily exploited by statistical models for tasks(More)
Applications that run on mobile operating systems such as Android use in-app advertisement libraries for monetization. Recent research reveals that many ad libraries, including popular ones pose threats to user privacy. Some aggressive ad libraries involve in active privacy leaks with the intention of providing targeted ads. Few intrusive ad libraries are(More)
In the arms race of attackers and defenders, the defense is usually more challenging than the attack due to the unpredicted vulnerabilities and newly emerging attacks every day. Currently, most of existing malware detection solutions are individually proposed to address certain types of attacks or certain evasion techniques. Thus, it is desired to conduct a(More)
In this paper, we propose a novel graph kernel specifically to address a challenging problem in the field of cyber-security, namely, malware detection. Previous research has revealed the following: (1) Graph representations of programs are ideally suited for malware detection as they are robust against several attacks, (2) Besides capturing topological(More)
Malware has posed a major threat to the Android ecosystem. Existing malware detection tools mainly rely on signature- or feature- based approaches, failing to provide detailed information beyond the mere detection. In this work, we propose a precise semantic model of Android malware based on Deterministic Symbolic Automaton (DSA) for the purpose of malware(More)
It is well-known that malware constantly evolves so as to evade detection and this causes the entire malware population to be non-stationary. Contrary to this fact, prior works on machine learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i.e., features) do not change over time. In this work,(More)
Android applications typically contain multiple third-party libraries and recent studies have shown that the presence of third-party libraries may introduce privacy risks and security threats. Furthermore, researchers have reported the importance of considering the third-party libraries for their program analysis tasks. A reason being that the presence of(More)
Existing Android malware detection approaches use a variety of features such as security-sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviors with inherent(More)
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