A Study on Constructing Knowledge Graph and Graph-based Deep Learning for Prediction and Ranking Problems in Cybersecurity

Abstract

This research aims at studying how to build knowledge graph representation for cybersecurity by performing open information extraction techniques on a large scale of text documents. After that, we will investigate how deep learning can be applied for knowledge graph representation in cybersecurity. We will consider the use of attention mechanism in graph deep learning model and compare various deep learning models which can be used for learning from graph representation data. Besides, we will study semi-supervised learning frameworks for utilizing unlabeled data for improving the performance of prediction and ranking problems. The proposed method will be applied for detecting malware and code flaws in cybersecurity.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2022
Accession Number
AD1166641

Entities

People

  • Le-Minh Nguyen

Organizations

  • Japan Advanced Institute of Science and Technology

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Classification
  • Computational Science
  • Computer Languages
  • Covid-19
  • Cybersecurity
  • Deep Learning
  • Dimensionality Reduction
  • Language
  • Machine Learning
  • Neural Networks
  • Scientific Research
  • Semi-Supervised Learning
  • Students
  • Supervised Machine Learning
  • Systems Engineering
  • Training

Fields of Study

  • Computer science

Readers

  • Cybersecurity.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks
  • Cyber
  • Cyber - Cryptography