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Found 7 papers, 2 papers with code
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications
Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e. g., incorporating several semantic views of an app such as API sequences, system calls, etc.)
graph2vec: Learning Distributed Representations of Graphs
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
Ranked #1 on
Malware Detection
on Android Malware Dataset
Context-aware, Adaptive and Scalable Android Malware Detection through Online Learning (extended version)
Contrary to this fact, most of the prior works on Machine Learning based Android malware detection have assumed that the distribution of the observed malware characteristics (i. e., features) does not change over time.
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks.
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel.
Adaptive and Scalable Android Malware Detection through Online Learning
In order to perform scalable detection and to adapt to the drift and evolution in malware population, an online passive-aggressive classifier is used.
Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection
To address this, we develop the Contextual Weisfeiler-Lehman kernel (CWLK) which is capable of capturing both these types of information.
