Understanding the Limits of Artificial Intelligence for Warfighters Volume 2, Distributional Shift in Cybersecurity Datasets

Abstract

The occurrence of distributional shift can reduce the performance of machine-learning (ML) systems. This issue is of particular relevance to cybersecurity datasets because the signatures of cyberattacks can shift rapidly and unpredictably in many different ways the data environment is both high-dimensional and highly nonstationary. In seeking a solution to the detection of such a shift along with mitigation, we can create andenhance ML models so that they are more robust and effective. Therefore, detecting and mitigating the adverse effects of distributional shift is paramount to effectively defending against cyberattacks.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2024
Accession Number
AD1218234

Entities

People

  • Anthony Jacques
  • Erik Van Hegewald
  • Gavin S. Hartnett
  • Joshua Steier
  • Lance Menthe

Organizations

  • RAND Corporation

Tags

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Change Detection
  • Computer Languages
  • Computers
  • Cyberattacks
  • Cybersecurity
  • Data Mining
  • Information Processing
  • Information Science
  • Information Systems
  • Intrusion Detection
  • Intrusion Detectors
  • Machine Learning
  • Network Science
  • Neural Networks

Fields of Study

  • Computer science

Readers

  • Cybersecurity.
  • Distributed Systems and Data Platform Development
  • Radio communications and signal processing.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Cyber