AI-driven Wireless Attack Detection and Escape (AI-WADE) Summary Report

Abstract

Spectrum battlefield is characterized by a complex multi-domain environment with fast and unknown dynamics. Wireless channel, network topology, user mobility and traffic, and interference effects, which includes in-network and out-network interference as well as adversarial jamming, changes over time. Conventional statistical and rule-based methods cannot be effectively applied for attack detection and prediction due to the complexity of the spectrum environment. Deep Learning (DL), particularly Deep Reinforcement Learning (RL) has emerged as a powerful means for spectrum awareness by learning from and adapting to spectrum dynamics. However, DL models can forget the behavior and the appropriate responses to a known threats as they learn to adapt to a new unseen type of wireless threat.

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Document Details

Document Type
Technical Report
Publication Date
Jan 18, 2024
Accession Number
AD1219113

Entities

People

  • Sastry Kompella

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Network Security
  • Computer Vision
  • Deep Learning
  • Department Of Defense
  • Learning
  • Machine Learning
  • Network Topology
  • Networks
  • Neural Networks
  • Reinforcement Learning
  • Security
  • Spectra
  • Transitions
  • Wireless Computer Networks
  • Wireless Networks

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Maritime Combat Support and Expeditionary Logistics.
  • Neural Network Machine Learning.

Technology Areas

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