Publications

2024

1. NSDI ’25

   Mutant: Learning Congestion Control from Existing Transport Protocols

   Lorenzo Pappone, Alessio Sacco, and Flavio Esposito

   22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2024

   Abs Bib

   Learning how to control congestion remains a challenge despite years of progress. Existing congestion control protocols have demonstrated efficacy within specific network conditions, inevitably behaving suboptimally or poorly in others. Machine learning solutions to congestion control have been proposed, though relying on extensive training and specific network configurations. In this paper, we loosen such dependencies by proposing Mutant, an online reinforcement learning algorithm for congestion control that adapts to the behavior of the best-performing schemes, outperforming them in most network conditions. Design challenges included determining the best protocols to learn from, given a network scenario, and creating a system able to evolve to accommodate future protocols with minimal changes. Our evaluation on real-world and emulated scenarios shows that Mutant achieves lower delays and higher throughput than prior learning-based schemes while maintaining fairness by exhibiting negligible harm to competing flows, making it robust across diverse and dynamic network conditions.

   @article{mutant,
     title = {Mutant: Learning Congestion Control from Existing Transport Protocols},
     author = {Pappone, Lorenzo and Sacco, Alessio and Esposito, Flavio},
     journal = {22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI)},
     year = {2024},
     pages = {},
     organization = {},
     doi = {},
   }

2. CNSM ’24

   ResCue: Inferring Fine-Grained Traffic Matrices via Distributed Deep Residual Networks

   Lorenzo Pappone, Cristian Zilli, Alessio Sacco, and 1 more author

   20th International Conference on Network and Service Management (CNSM), 2024

   Abs Bib PDF

   Network measurement and telemetry techniques are central to the management of modern computer networks. Internet traffic matrix estimation is a popular technique employed for network management and telemetry to reconstruct missing information. Existing approaches use statistical methods, which often make impractical assumptions about the structure of the Internet traffic matrix. Data-driven methods, instead, heavily rely on the assumption of full knowledge of network topology data, that may be unavailable or impractical to collect. In this work, we propose ResCue, a deep residual networks technique to infer fine-grained Internet network traffic starting from spatial coarse-grained measurements. To address scenarios with network visibility constraints, we design a federated learning approach for fine-grained traffic prediction with partial network knowledge. Our evaluation across real-world traffic data shows that our proposed approach outperforms existing interpolation techniques and that our federated learning design achieves similar accuracy with respect to its centralized counterpart while requiring only partial knowledge of the network

   @article{super_resolution,
     author = {Pappone, Lorenzo and Zilli, Cristian and Sacco, Alessio and Esposito, Flavio},
     title = {ResCue: Inferring Fine-Grained Traffic Matrices via Distributed Deep Residual Networks},
     journal = {20th International Conference on Network and Service Management (CNSM)},
     year = {2024},
   }

3. CNSM ’24

   Addressing Data Security in IoT: Minimum Sample Size and Denoising Diffusion Models for Improved Malware Detection

   Chiara Camerota, Lorenzo Pappone, Tommaso Pecorella, and 1 more author

   20th International Conference on Network and Service Management (CNSM), 2024

   Abs Bib PDF

   Machine learning (ML) has emerged as a compelling approach to identify attacks in network traffic security. Existing malware detection strategies often concentrate on specific facets, such as efficient data collection, particular types of malware, or handling data scarcity. While valid, these strategies typically overlook the potential for minimizing sample size, focusing instead on data augmentation. This work introduces a novel method to determine the minimum sample size necessary to achieve a specified accuracy level, measured by the F1 score derived from the confusion matrix. We focus on TCP header traffic data transformed into images through flow-splitting techniques for multi-class traffic classification. In addition, we introduce a diffusion model to generate new synthetic traffic images and show that our method outperforms existing techniques in terms of stability and predictability. This study also compares the effectiveness of synthetic image augmentation using Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPM) in improving image recognition and classification accuracy.

   @article{gan_malware,
     title = {Addressing Data Security in IoT: Minimum Sample Size and Denoising Diffusion Models for Improved Malware Detection},
     author = {Camerota, Chiara and Pappone, Lorenzo and Pecorella, Tommaso and Esposito, Flavio},
     journal = {20th International Conference on Network and Service Management (CNSM)},
     year = {2024},
   }

4. TNSM

   On Traffic Matrix Estimation via Super-Resolution and Federated Learning

   Lorenzo Pappone, Alessio Sacco, and Flavio Esposito

   IEEE Transactions on Network and Service Management, 2024

   Abs Bib PDF

   Network traffic telemetry plays a crucial role in the management of modern networks. Estimation of the network traffic matrix is a widely recognized problem whose solutions can span a diverse set of applications. Current approaches to traffic matrix inference through statistical methods often rely on assumptions about the matrix structure, which may be invalid in certain scenarios. Data-driven methods, instead, often use detailed information about the network topology that may be unavailable or impractical to collect. To overcome these challenges, we propose a super-resolution technique for traffic matrix inference that leverages coarser measurements to predict fine-grained network traffic. Furthermore, we devise a distributed learning procedure and adapt our model to scenarios of partial network visibility. Our experiments on real network traces demonstrate that the proposed approach can infer fine-grained network traffic with high precision. Moreover, we prove that our distributed approach improves the inference accuracy with respect to its centralized counterpart, significantly lowering the training time, even in scenarios with partial network knowledge.

   @article{super_resolution_journal,
     author = {Pappone, Lorenzo and Sacco, Alessio and Esposito, Flavio},
     title = {On Traffic Matrix Estimation via Super-Resolution and Federated Learning},
     journal = {IEEE Transactions on Network and Service Management},
     year = {2024},
   }

2023

1. TNSM

   Dealing with Changes: Resilient Routing via Graph Neural Networks and Multi-Agent Deep Reinforcement Learning

   Sai Shreyas Bhavanasi, Lorenzo Pappone, and Flavio Esposito

   IEEE Transactions on Network and Service Management, 2023

   Abs Bib HTML PDF

   The computer networking community has been steadily increasing investigations into machine learning to help solve tasks such as routing, traffic prediction, and resource management. The traditional best-effort nature of Internet connections allows a single link to be shared among multiple flows competing for network resources, often without consideration of in-network states. In particular, due to the recent successes in other applications, Reinforcement Learning has seen steady growth in network management and, more recently, routing. However, if there are changes in the network topology, retraining is often required to avoid significant performance losses. This restriction has chiefly prevented the deployment of Reinforcement Learning-based routing in real environments. In this paper, we approach routing as a reinforcement learning problem with two novel twists: minimize flow set collisions, and construct a reinforcement learning policy capable of routing in dynamic network conditions without retraining. We compare this approach to other routing protocols, including multi-agent learning, with respect to various Quality-of-Service metrics, and we report our lesson learned.

   @article{routing-tnsm,
     author = {Bhavanasi, Sai Shreyas and Pappone, Lorenzo and Esposito, Flavio},
     journal = {IEEE Transactions on Network and Service Management},
     title = {Dealing with Changes: Resilient Routing via Graph Neural Networks and Multi-Agent Deep Reinforcement Learning},
     year = {2023},
     volume = {},
     number = {},
     pages = {1-1},
     keywords = {},
     url = {https://www.comsoc.org/publications/journals/ieee-tnsm},
     issn = {1932-4537},
     month = {},
   }

2. CCNC ’23

   A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques

   Roberto Amoroso, Lorenzo Pappone, and Flavio Esposito

   2023

   Abs Bib HTML PDF

   Network measurement and telemetry techniques are central to the management of modern computer networks. Traffic matrix estimation is a popular technique that supports several applications. Existing approaches use statistical methods, which often make invalid assumptions about the structure of the traffic matrix. Data-driven methods, instead, leverage detailed information about the network topology that may be unavailable or impractical to collect. In this work, we propose a super-resolution technique for traffic matrix estimation that can infer fine-grained network traffic. In our experiment, we demonstrate that the proposed approach with high precision outperforms existing data interpolation techniques. We also expand our design by employing a federated learning model to address scalability and improve performance. We find that our model increases the accuracy of the inference with respect to its centralized counterpart.

   @article{federated,
     author = {Amoroso, Roberto and Pappone, Lorenzo and Esposito, Flavio},
     booktitle = {2023 IEEE Consumer Communications & Networking Conference (CCNC)},
     title = {A Federated Learning Approach to Traffic Matrix Estimation using Super-resolution Techniques},
     year = {2023},
     volume = {},
     number = {},
     pages = {},
     doi = {10.1109/CCNC51644.2023.10060210},
     url = {https://ccnc2023.ieee-ccnc.org},
   }

2022

1. NFV-SDN ’22

   Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning

   Sai Shreyas Bhavanasi, Lorenzo Pappone, and Flavio Esposito

   2022

   Abs Bib HTML PDF

   The computer networking community has been steadily increasing investigations into machine learning to help solve tasks such as routing, traffic prediction, and resource management. In particular, due to the recent successes in other applications, Reinforcement Learning (RL) has seen steady growth in network management and, more recently, in routing. However, changes in the network topology prevent RL-based routing approaches from being employed in real environments due to the need for retraining. In this paper, we approach routing as an RL problem with two novel twists: minimizing flow set collisions and dealing with routing in dynamic network conditions without retraining. We compare this approach to other routing protocols, including multi-agent learning, to various Quality-of-Service metrics, and we report our lesson learned.

   @article{routing,
     author = {Bhavanasi, Sai Shreyas and Pappone, Lorenzo and Esposito, Flavio},
     booktitle = {2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)},
     title = {Routing with Graph Convolutional Networks and Multi-Agent Deep Reinforcement Learning},
     year = {2022},
     volume = {},
     number = {},
     pages = {72-77},
     url = {https://nfvsdn2022.ieee-nfvsdn.org},
   }

2. IFIP ’22

   Prediction of Mobile-App Network-Video-Traffic Aggregates using Multi-task Deep Learning

   Lorenzo Pappone, Francesco Cerasuolo, Valerio Persico, and 3 more authors

   2022

   Abs Bib HTML PDF

   Traffic prediction has proven to be useful for several network management domains and represents one of the main enablers for instilling intelligence within future networks. Recent solutions have focused on predicting the behavior of traffic aggregates. Nonetheless, minimal attempts have tackled the prediction of mobile network traffic generated by different video application categories. To this end, in this work we apply Multi-task Deep Learning to predict network traffic aggregates generated by mobile video applications over short-term time scales. We investigate our approach leveraging state-of-art prediction models such as Convolutional Neural Networks, Gated Recurrent Unit, and Random Forest Regressor, showing some surprising results (e.g. NRMSE < 0.075 for upstream packet count prediction while NRMSE < 0.15 for the downstream counterpart), including some variability in prediction performance among the examined video application categories. Furthermore, we show that using smaller time intervals when predicting traffic aggregates may achieve better performances for specific traffic profiles.

   @article{mobileprediction,
     author = {Pappone, Lorenzo and Cerasuolo, Francesco and Persico, Valerio and Ciuonzo, Domenico and Pescapé, Antonio and Esposito, Flavio},
     booktitle = {2022 IFIP Networking Conference (IFIP Networking)},
     title = {Prediction of Mobile-App Network-Video-Traffic Aggregates using Multi-task Deep Learning},
     year = {2022},
     volume = {},
     number = {},
     pages = {1-6},
     doi = {10.23919/IFIPNetworking55013.2022.9829800},
     url = {https://networking.ifip.org/2022/},
   }