On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks

Damian Jankowski, Marek Amanowicz

International Journal of Electronics and Telecommunications 2016 article cited by 50

Source Open-access PDF More in Software & Coding

importance 4/10 ai assisted Network Security and Intrusion Detection network security stage: research

Problem

Detecting malicious activity in Software Defined Network data planes requires efficient monitoring methods.

Why it matters

Intrusion detection protects network infrastructure from attacks, with direct economic and security consequences.

What the AI did

Machine learning methods (Self Organizing Maps and Learning Vector Quantization, plus enhanced versions) were tested for detecting malicious traffic using statistics generated by native SDN mechanisms in a virtual test environment.

Results

not stated in source

Impact

The abstract states results were compared with other SDN-based IDS but gives no concrete numbers, so impact cannot be quantified.

Record

Venue	International Journal of Electronics and Telecommunications
Published	2016
Work type	article
Authors	Damian Jankowski, Marek Amanowicz
Citations	50 (OpenAlex, at import time)
References	17
Model or method	Self Organizing Maps, Learning Vector Quantization (and enhanced versions)
Organization	not stated in source
Primary source	https://doi.org/10.1515/eletel-2016-0033
Open access	gold
Keywords	intrusion detection, SDN, machine learning, SOM, LVQ, network security

Importance rating

4 / 10

real but narrower advance

SDN intrusion-detection concept tested only in a virtual environment; abstract gives no results, scale, or deployment evidence, so this is a real but narrow security advance.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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