A Petri Net and LSTM Hybrid Approach for Intrusion Detection Systems in Enterprise Networks
Gaetano Volpe, Marco Fiore, Annabella la Grasta, Francesca Albano, Sergio Stefanizzi, Marina Mongiello, Agostino Marcello Mangini, Răzvan Gabriel Boboc, Ali Vatankhah Barenji · 2024
Read A Petri Net and LSTM Hybrid Approach for Intrusion Detection Systems in Enterprise Networks by Gaetano Volpe, Marco Fiore, Annabella la Grasta, Francesca Albano, Sergio Stefanizzi, Marina Mongiello, Agostino Marcello Mangini, Răzvan Gabriel Boboc, Ali Vatankhah Barenji (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11679887.txt.
Description
Gaetano Volpe and co-authors fuse a memory neural network with a Petri net model to spot and block intrusions on company networks in real time, in a 2024 paper. Learning-based detectors flag attacks well but often stop at flagging, without steering the traffic itself. Their network reads packets in flowing groups, while the formal net model routes or drops traffic based on its verdicts.
The hybrid reached 99.71 percent detection accuracy, up from about 97 percent for the learning model alone. The authors present the pairing as a practical design: machine learning for judgment, formal modeling for enforcement, inside one firewall-ready system.
Catalog record
| CREATOR | Gaetano Volpe, Marco Fiore, Annabella la Grasta, Francesca Albano, Sergio Stefanizzi, Marina Mongiello, Agostino Marcello Mangini, Răzvan Gabriel Boboc, Ali Vatankhah Barenji |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 78 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |