Generative adversarial network for cybersecurity applications

PhD Program in Electrical, Electronics and Communications Engineering


Marco Mellia –
Idilio Drago –
Antonio Lioy –

PhD Student: Francesca Soro

Context of the research activity

Cybersecurity is one of the biggest problem in the information society that is impacting all modern communication networks. More and more complicated threats are found on a daily basis, which make the complexity of identifying and designing countermeasures more and more difficult. Machine learning and Big Data offer scalable solutions to learn from labelled datasets and build models that can be used to detect attacks. Unfortunately, in the cybersecurity context, we lack the ability to obtain large datasets of labelled attacks, since threats continue to evolve over time. This call for novel solutions to face the problem. Recently, generative adversarial networks have been proposed as a means to generalize a sample labelled dataset and create artificially richer datasets. The involve two neural networks contesting with each other in a zero‐sum game framework, one that generates candidates while the second learn how to discriminate instances. The generative network’s training objective is to increase the error rate of the discriminative network (i.e., “fool” the discriminator network by producing novel synthesized instances that appear to have come from the true data distribution).   The research activity fits in the SmartData@PoliTo interdepartmental centre, that brings together competences from different fields, ranging from modelling to computer programming, from communications to statistics. The candidate will join this interdisciplinary team of experts and collaborate with them.


The objective of the research is to investigate the usage of generative adversarial networks for cybersecurity applications. Starting from a small labelled dataset of known attacks (e.g., simple scan, DDoS, or traffic generated by well‐known botnets), the system of the two neural networks will be used to generalize the model. Starting from this initial design, the system will then have to scale to process large amount of data, as typically encountered in the internet. To this extent, big data approaches will be investigated.

Skills and competencies for the development of the activity

The candidate must have excellent knowledge of computer networks, and good knowledge of machine learning methodologies. The candidate shall have also knowledge of Big Data platforms, such as Hadoop, Spark, Hive,  Flume.


Further information about the PhD program at Politecnico can be found here

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