# Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks

arXiv: https://arxiv.org/abs/1803.06762 · HTML: https://mlsanity.com/paper/1803.06762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold](https://mlsanity.com/paper/2111.00626/) (2021)

## Abstract (arXiv)

Software-defined networking (SDN) is a new paradigm that allows developing more flexible network applications. SDN controller, which represents a centralized controlling point, is responsible for running various network applications as well as maintaining different network services and functionalities. Choosing an efficient intrusion detection system helps in reducing the overhead of the running controller and creates a more secure network. In this study, we investigate the performance of the well-known anomaly-based intrusion detection approaches in terms of accuracy, false alarm rate, precision, recall, f1-measure, area under ROC curve, execution time and Mc Nemar's test. Precisely, we focus on supervised machine-learning approaches where we use the following classifiers: Decision Trees (DT), Extreme Learning Machine (ELM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Neural Networks (NN), Support Vector Machines (SVM), Random Forest (RT), K Nearest-Neighbour (KNN), AdaBoost, RUSBoost, LogitBoost and BaggingTrees where we employ the well-known NSL-KDD benchmark dataset to compare the performance of each one of these classifiers.