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Tesi etd-01302020-132017


Thesis type
Tesi di laurea magistrale
Author
CASTIGLIONE, FRANCESCO
URN
etd-01302020-132017
Title
Binary and multi-class classification in network anomaly detection using deep neural networks
Struttura
INFORMATICA
Corso di studi
INFORMATICA E NETWORKING
Supervisors
relatore Pagano, Michele
supervisore Callegari, Christian
Parole chiave
  • intrusion detection system
  • anomaly detection
  • deep neural networks
Data inizio appello
06/03/2020;
Consultabilità
Secretata d'ufficio
Data di rilascio
06/03/2090
Riassunto analitico
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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