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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-01302020-132017


Tipo di tesi
Tesi di laurea magistrale
Autore
CASTIGLIONE, FRANCESCO
URN
etd-01302020-132017
Titolo
Binary and multi-class classification in network anomaly detection using deep neural networks
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA E NETWORKING
Relatori
relatore Pagano, Michele
supervisore Callegari, Christian
Parole chiave
  • anomaly detection
  • deep neural networks
  • intrusion detection system
Data inizio appello
06/03/2020
Consultabilità
Non consultabile
Data di rilascio
06/03/2090
Riassunto
This thesis proposes an inspection of binary and multiclass classification in the case of an anomaly detection-based network intrusion detection system through the use of five different deep neural networks. First of all, a brief description of which is an intrusion detection system, a neural network and what are the types of network used. Then the testbed on which all the performance results are calculated, is presented. The last step is to evaluate the results comparing the performances in the case of binary and multiclass classification.
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