371
On the Eectiveness of
Machine and Deep Learning
for Cyber Security
Abstract: Machine learning is adopted in a wide range of domains where it shows its
superiority over traditional rule-based algorithms. These methods are being integrated
in cyber detection systems with the goal of supporting or even replacing the rst level
of security analysts. Although the complete automation of detection and analysis is
an enticing goal, the efcacy of machine learning in cyber security must be evaluated
Giovanni Apruzzese
Department of Engineering
‘Enzo Ferrari’
University of Modena
and Reggio Emilia
Modena, Italy
giovanni.apruzzese@unimore.it
Luca Ferretti
Department of Engineering
‘Enzo Ferrari’
University of Modena
and Reggio Emilia
Modena, Italy
luca.ferretti@unimore.it
Mirco Marchetti
Department of Engineering
‘Enzo Ferrari’
University of Modena
and Reggio Emilia
Modena, Italy
mirco.marchetti@unimore.it
Michele Colajanni
Department of Engineering
‘Enzo Ferrari’
University of Modena
and Reggio Emilia
Modena, Italy
michele.colajanni@unimore.it
Alessandro Guido
Department of Engineering
‘Enzo Ferrari’
University of Modena
and Reggio Emilia
Modena, Italy
alessandro.guido@unimore.it
2018 10th International Conference on Cyber Conict
CyCon X: Maximising Eects
T. Minárik, R. Jakschis, L. Lindström (Eds.)
2018 © NATO CCD COE Publications, Tallinn
Permission to make digital or hard copies of this publication for internal
use within NATO and for personal or educational use when for non-prot or
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requires prior written permission by NATO CCD COE.
