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Publicação
DATA SCIENCE IN CYBERSECURITY
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| 2.15 MB | Adobe PDF |
Resumo(s)
The new generation of communication networks has brought with them the
digitalization of companies and services that have changed not only the way
we communicate with each other but also the way we exchange personal and
confidential data between people and entities. The IoT is one of the technological
paradigms that benefits the most from these new forms of connectivity. The
IoT allows us to be always connected to people, companies, our homes, our
cities, our intelligent equipment and allows us to automate tasks or control
situations remotely that would not be possible without this type of equipment
and technology. But with the globalization of networks and services, the need to
protect our data and our privacy is something to be concerned about. Although
there are already several security options, both in companies and in our service
providers, the amount of data that is currently generated far exceeds the capacity,
of humans and systems, to analyze what is happening on our networks.
In this context, the dissertation presented here will make use of data science and
implement machine learning techniques to deal with the volume of data generated
by an IoT network. As a scenario, the network of a smart city was chosen, where
an intrusion detection system will be placed, supported by a machine learning
model so that it is possible to detect any type of activity that is not recognized
as being its normal production behavior. The anomaly detection methodology
was implemented through machine learning algorithms that enabled the classification
of network flows as benign or malicious. By comparing supervised and
unsupervised classification algorithms, we found that with a dataset from an IoT
network and with flows previously categorized as normal traffic and malicious
traffic, supervised classifiers manage to obtain the best results, although they are
limited if there is one attack that has not been considered in the given dataset.
By combining, in this dissertation, an intrusion detection system with data
science and specifically with machine learning models, it was demonstrated that
this is a valid cybersecurity solution and that it constitutes an additional layer in
terms of ensuring the security of our networks, services, and data.
Descrição
Palavras-chave
IoT IoT Network Inteligência artificial Algoritmos Internet das coisas Automação Segurança da informação