Towards Deep Federated Defenses Against Malware in Cloud Ecosystems
- URL: http://arxiv.org/abs/1912.12370v1
- Date: Fri, 27 Dec 2019 23:46:06 GMT
- Title: Towards Deep Federated Defenses Against Malware in Cloud Ecosystems
- Authors: Josh Payne and Ashish Kundu
- Abstract summary: In cloud computing environments with many virtual machines, containers, and other systems, an epidemic of malware can be highly threatening to business processes.
We introduce a hierarchical approach to performing malware detection and analysis using several recent advances in machine learning on graphs, hypergraphs, and natural language.
- Score: 0.24366811507669117
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In cloud computing environments with many virtual machines, containers, and
other systems, an epidemic of malware can be highly threatening to business
processes. In this vision paper, we introduce a hierarchical approach to
performing malware detection and analysis using several recent advances in
machine learning on graphs, hypergraphs, and natural language. We analyze
individual systems and their logs, inspecting and understanding their behavior
with attentional sequence models. Given a feature representation of each
system's logs using this procedure, we construct an attributed network of the
cloud with systems and other components as vertices and propose an analysis of
malware with inductive graph and hypergraph learning models. With this
foundation, we consider the multicloud case, in which multiple clouds with
differing privacy requirements cooperate against the spread of malware,
proposing the use of federated learning to perform inference and training while
preserving privacy. Finally, we discuss several open problems that remain in
defending cloud computing environments against malware related to designing
robust ecosystems, identifying cloud-specific optimization problems for
response strategy, action spaces for malware containment and eradication, and
developing priors and transfer learning tasks for machine learning models in
this area.
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