Using Deep Reinforcement Learning to Simulate Security Analyst

Abstract

The goal of this project was to overcome conventional limitations of deep learning approaches and advance the use of machine learning approaches to understand, identify, and analyze security incidents. In general, main-stream classifiers require a full description of the sample (in our scenario this means all possible information about the security incident) and perform the classification in one step, which is in a sharp contrast to the modus operandi of the analyst, whose investigation is composed of a sequence of actions and decisions.

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Document Details

Document Type
Technical Report
Publication Date
Jun 03, 2022
Accession Number
AD1175136

Entities

People

  • Tomas Pevny

Organizations

  • Czech Technical University in Prague

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Classification
  • Deep Learning
  • Learning
  • Machine Learning
  • Neural Networks
  • Reinforcement Learning
  • Scientific Research
  • Security
  • Universities

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Software Engineering.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks