READ ME File For 'Dataset supporting the University of Southampton Doctoral Thesis "An investigation into passive information gathering for uncooperative ad-hoc mesh network assessment"'

Dataset DOI: 10.5258/SOTON/D3011

ReadMe Author: Michael O'Sullivan, University of Southampton 

This dataset supports the thesis entitled "An investigation into passive information gathering for uncooperative ad-hoc mesh network assessment"
AWARDED BY: Univeristy of Southampton
DATE OF AWARD: [2024]



DESCRIPTION OF THE DATA

This dataset contains:

Data for thesis contains result from multiple simulation runs. The dataset is in 2 part, the first being the data that supports Phase 2: Routing Algorithm Reconnaissance in Ad-Hoc Mesh Networks.

The first experiment contains results for 6 machine learning techniques: Support Vector Machine,Random Forest, Convolutional Neural Network, Bernoulli Naive Bayes, Gaussian Naive Bayes and Deep Forest. For each of these various combination of network packet fields sizes were used. These were ‘Subtype’, ‘Header Length’ , ‘Frame Length’ and UDP length and concerns 2 or 3 classes of routing algorithms.

The second experiment contains results for 3 machine learning techniques: Support Vector Machine,Random Forest and Convolutional Neural Network. It uses the same network packet fields.

The third experiment is a repeat of the second experiment but with the number of nodes being incrementally reduced.

The other dataset supports Phase 3: Predicting Node Importance In Temporal Dynamic Networks.
The data for this phase contains information for  link prediction techniques and the accuracy of the prediction vs ground truth. This is from 700 simulation runs of the mesh network with each of the 3 mobility models.



Date of data collection: [2019-2023]

Licence:
[CC BY]


Related publication:
[O'Sullivan, M., Aniello, L., & Sassone, V. (2020). A methodology to select topology generators for ad hoc mesh network simulations. Journal of Communications, 15(10). https://doi.org/10.12720/jcm.15.10.741-746]


Date that the file was created: March, 2024