End-to-end deep neural networks and transfer learning for automatic analysis of nation-state malware

Ishai Rosenberg, Guillaume Sicard, Eli David

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Malware allegedly developed by nation-states, also known as advanced persistent threats (APT), are becoming more common. The task of attributing an APT to a specific nation-state or classifying it to the correct APT family is challenging for several reasons. First, each nation-state has more than a single cyber unit that develops such malware, rendering traditional authorship attribution algorithms useless. Furthermore, the dataset of such available APTs is still extremely small. Finally, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. In this paper, we use a deep neural network (DNN) as a classifier for nation-state APT attribution. We record the dynamic behavior of the APT when run in a sandbox and use it as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself. We also use the same raw features for APT family classification. Finally, we use the feature abstractions learned by the APT family classifier to solve the attribution problem. Using a test set of 1000 Chinese and Russian developed APTs, we achieved an accuracy rate of 98.6%.

Original languageEnglish
Article number390
JournalEntropy
Volume20
Issue number5
DOIs
StatePublished - 22 May 2018
Externally publishedYes

Keywords

  • Attribution
  • Cybersecurity
  • Deep learning
  • Family classification
  • Nation-state APT
  • Transfer learning

ASJC Scopus subject areas

  • Information Systems
  • General Physics and Astronomy
  • Electrical and Electronic Engineering
  • Mathematical Physics
  • Physics and Astronomy (miscellaneous)

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