Data mining file sharing metadata: A comparison between Random Forests Classificiation and Bayesian Networks
Independent thesis Basic level (degree of Bachelor), 15 credits / 22,5 HE creditsStudent thesis
In this comparative study based on experimentation it is demonstrated that the two evaluated machine learning techniques, Bayesian networks and random forests, have similar predictive power in the domain of classifying torrents on BitTorrent file sharing networks.
This work was performed in two steps. First, a literature analysis was performed to gain insight into how the two techniques work and what types of attacks exist against BitTorrent file sharing networks. After the literature analysis, an experiment was performed to evaluate the accuracy of the two techniques.
The results show no significant advantage of using one algorithm over the other when only considering accuracy. However, ease of use lies in Random forests’ favour because the technique requires little pre-processing of the data and still generates accurate results with few false positives.
Place, publisher, year, edition, pages
2015. , 43 p.
machine learning, random forests, bayesian network, bittorrent, file sharing
IdentifiersURN: urn:nbn:se:his:diva-11180OAI: oai:DiVA.org:his-11180DiVA: diva2:823863
Subject / course
Computer Science - Specialization in Systems Development
Mellin, Jonas, Lektor
Steinhauer, Joe, Lektor