Open Source Software Tools for Anomaly Detection Analysis

Abstract

The goal of this report is to perform an analysis of software tools that could be employed to perform basic research and development of Anomaly-Based Intrusion Detection Systems. The software tools reviewed include; Environment for Developing KDD-Applications Supported by Index-Structures (ELKI), RapidMiner, SHOGUN (toolbox) Waikato Environment for Knowledge Analysis (Weka) (machine learning), and Scikit-learn. From the analysis, it is recommended to employ the SHOGUN (toolbox) or Scikit-learn as both tools are written in C++ and offers an interface for Python. The python language software is currently employed as a research tool within our in-house team of researchers.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2014
Accession Number
ADA599306

Entities

People

  • Robert F. Erbacher
  • Robinson Pino

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Autonomy
  • Cyber

DTIC Thesaurus Topics

  • Anomaly Detection
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Change Detection
  • Computer Languages
  • Computer Programming
  • Data Mining
  • Detection
  • Dimensionality Reduction
  • Graphical User Interface
  • Information Science
  • Intrusion Detection
  • Language
  • Machine Learning
  • Open Source Software
  • Python Programming Language
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Database Systems and Applications
  • Theoretical Analysis.

Technology Areas

  • AI & ML