Fabio Massimo Zennaro

University of Oslo

Filter (signal processing)Machine learningMathematicsComputer scienceData set

16Publications

2H-index

14Citations

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Publications 14

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Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges and Tabular Q-Learning

#1Fabio Massimo Zennaro (University of Oslo)H-Index: 2

#2Laszlo Erdodi (University of Oslo)H-Index: 1

Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by trained human attackers and its success critically depended on the available expertise. Automating this practice constitutes a non-trivial problem, as the range of actions that a human expert may attempts against a system and the range of knowledge she relies on to take her decisions are hard to capture. In this paper,...

#1Alexander Egiazarov (University of Oslo)

#2Vasileios Mavroeidis (University of Oslo)H-Index: 5

Last. Kamer Vishi (University of Oslo)H-Index: 2

view all 4 authors...

In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get the most attention. Even though surveillance cameras are assumed to be a powerful tool, their effect in preventing crime is far from clear due to either limitation in the ability of humans to vigilantly monitor video surveillance or for the simple reason that they are operating passively. In this paper, we present a w...

#1Alexander Egiazarov (University of Oslo)

#2Vasileios Mavroeidis (University of Oslo)H-Index: 5

Last. Kamer Vishi (University of Oslo)H-Index: 2

view all 4 authors...

In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get the most attention. Even though surveillance cameras are assumed to be a powerful tool, their effect in preventing crime is far from clear due to either limitation in the ability of humans to vigilantly monitor video surveillance or for the simple reason that they are operating passively. In this paper, we present a w...

#1Fabio Massimo Zennaro (University of Oslo)H-Index: 2

#2Ke Chen (University of Manchester)H-Index: 27

In this paper we examine a formalization of feature distribution learning (FDL) in information-theoretic terms relying on the analytical approach and on the tools already used in the study of the information bottleneck (IB). It has been conjectured that the behavior of FDL algorithms could be expressed as an optimization problem over two information-theoretic quantities: the mutual information of the data with the learned representations and the entropy of the learned distribution. In particular...

Analyzing and Storing Network Intrusion Detection Data Using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings

#1Fabio Massimo Zennaro (University of Oslo)H-Index: 2

In this paper we offer a preliminary study of the application of Bayesian coresets to network security data. Network intrusion detection is a field that could take advantage of Bayesian machine learning in modelling uncertainty and managing streaming data; however, the large size of the data sets often hinders the use of Bayesian learning methods based on MCMC. Limiting the amount of useful data is a central problem in a field like network traffic analysis, where large amount of redundant data c...

An empirical evaluation of the approximation of subjective logic operators using Monte Carlo simulations

#1Fabio Massimo Zennaro (University of Oslo)H-Index: 2

#2Magdalena Ivanovska (University of Oslo)H-Index: 5

Last. Audun Jøsang (University of Oslo)H-Index: 42

view all 3 authors...

Abstract In this paper we analyze the use of subjective logic as a framework for performing approximate transformations over probability distribution functions. As for any approximation, we evaluate subjective logic in terms of computational efficiency and bias. However, while the computational cost may be easily estimated, the bias of subjective logic operators have not yet been investigated. In order to evaluate this bias, we propose an experimental protocol that exploits Monte Carlo simulatio...

Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings

#1Fabio Massimo Zennaro (University of Oslo)H-Index: 2

In this paper we offer a preliminary study of the application of Bayesian coresets to network security data. Network intrusion detection is a field that could take advantage of Bayesian machine learning in modelling uncertainty and managing streaming data; however, the large size of the data sets often hinders the use of Bayesian learning methods based on MCMC. Limiting the amount of useful data is a central problem in a field like network traffic analysis, where large amount of redundant data c...

#1Fabio Massimo Zennaro (University of Oslo)H-Index: 2

#2Magdalena Ivanovska (University of Oslo)H-Index: 5

In this paper we study the problem of making predictions using multiple structural causal models defined by different agents, under the constraint that the prediction satisfies the criterion of counterfactual fairness. Relying on the frameworks of causality, fairness and opinion pooling, we build upon and extend previous work focusing on the qualitative aggregation of causal Bayesian networks and causal models. In order to complement previous qualitative results, we devise a method based on Mont...

#1Fabio Massimo Zennaro (University of Oslo)H-Index: 2

#2Magdalena Ivanovska (University of Oslo)H-Index: 5

In this paper we study the problem of making predictions using multiple structural casual models defined by different agents, under the constraint that the prediction satisfies the criterion of counterfactual fairness. Relying on the frameworks of causality, fairness and opinion pooling, we build upon and extend previous work focusing on the qualitative aggregation of causal Bayesian networks and causal models. In order to complement previous qualitative results, we devise a method based on Mont...

#1Fabio Massimo ZennaroH-Index: 2

#2Magdalena IvanovskaH-Index: 5

In this paper we consider the problem of combining multiple probabilistic causal models, provided by different experts, under the requirement that the aggregated model satisfy the criterion of counterfactual fairness. We build upon the work on causal models and fairness in machine learning, and we express the problem of combining multiple models within the framework of opinion pooling. We propose two simple algorithms, grounded in the theory of counterfactual fairness and causal judgment aggrega...

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