Outlier Classification Criterion for Multivariate Cyber Anomaly Detection

Abstract

Every day, intrusion detection systems catalogue millions of unsupervised data entries. This represents a big data problem for research sponsors within the Department of Defense. In a first response to this issue, raw data capture was transformed into usable vectors and an array of multivariate techniques implemented to detect potential outliers

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Document Details

Document Type
Technical Report
Publication Date
Mar 22, 2018
Accession Number
AD1056428

Entities

People

  • Alexander M. Trigo

Organizations

  • Air Force Institute of Technology

Tags

DTIC Thesaurus Topics

  • Anomaly Detection
  • Big Data
  • Change Detection
  • Classification
  • Coding
  • Data Analysis
  • Data Sets
  • Department Of Defense
  • Detection
  • Detectors
  • Digital Data
  • Digital Information
  • Engineering
  • Factor Analysis
  • Identities
  • Information Science
  • Intrusion
  • Intrusion Detection
  • Intrusion Detection Systems
  • Intrusion Detectors
  • Mathematics
  • Metadata
  • Multivariate Analysis
  • Notation
  • Photographic Film
  • Photographic Materials
  • Photographic Recording Media
  • Photography
  • Specialty Uses Of Chemicals
  • Transparencies

Readers

  • Cybersecurity.
  • Neural Network Machine Learning.
  • Technical Research and Report Writing.

Technology Areas

  • Cyber