Alessandro Finamore

Paris, FRANCE ·

I am a researcher working in Internet measurements at the intersection between Deep Learning, BigData and data-plane programming. Currently, I am a Principal Engineer working in AI4NET Datacom lab lead by Dario Rossi, at the Huawei Research center in Paris (France), and I focus on the integration of Deep Learning into traffic monitoring systems, targeting continuous learning and network automation.

Previously, I was an associate researcher at Telefonica Research, and a Principal Engineer at Telefonica UK/O2, where I designed and deployed an ML product to predict daily customer satisfaction for 30M+ O2 customers using a variety of live network logs.

Research Papers

  • A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification PDF details
    G. Bovenzi, L. Yang, A. Finamore, G. Aceto, D. Ciuonzo, A. Pescapè, D. Rossi
    IEEE/IFIP Traffic Measurement and Analysis (TMA)
    The recent popularity growth of Deep Learning (DL) re-ignited the interest towards traffic classification, with several studies demonstrating the accuracy of DL-based classifiers to identify Internet applications' traffic. Even with the aid of hardware accelerators (GPUs, TPUs), DL model training remains expensive, and limits the ability to operate frequent model updates necessary to fit to the ever evolving nature of Internet traffic, and mobile traffic in particular. To address this pain point, in this work we explore Incremental Learning (IL) techniques to add new classes to models without a full retraining, hence speeding up model's updates cycle. We consider iCarl, a state of the art IL method, and MIRAGE-2019, a public dataset with traffic from 40 Android apps, aiming to understand "if there is a case for incremental learning in traffic classification". By dissecting iCarl internals, we discuss ways to improve its design, contributing a revised version, namely iCarl+. Despite our analysis reveals their infancy, IL techniques are a promising research area on the roadmap towards automated DL-based traffic analysis systems.

    @inproceedings{AF:CORR-21c, title={A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification}, author=, year={2021}, booktitle=Traffic Measurement and Analysis (TMA), location={Virtual Event}, doi=, howpublished="" }
  • FENXI: Deep-learning Traffic Analytics at the Edge PDF details
    M. Gallo, A. Finamore, G. Simon, D. Rossi
    ACM/IEEE Symposium on Edge Computing (SEC)
    Live traffic analysis at the first aggregation point in the ISP network enables the implementation of complex traffic engineering policies but is limited by the scarce processing capabilities, especially for Deep Learning (DL) based analytics. The introduction of specialized hardware accelerators i.e., Tensor Processing Unit (TPU), offers the opportunity to enhance the processing capabilities of network devices at the edge. Yet, to date, no packet processing pipeline is capable of offering DL-based analysis capabilities in the data-plane, without interfering with network operations. In this paper, we present FENXI, a system to run complex analytics by leveraging TPU. The design of FENXI decouples forwarding operations and traffic analytics which operates at different granularities i.e., packet and flow levels. We conceive two independent modules that asynchronously communicate to exchange network data and analytics results, and design data structures to extract flow level statistics without impacting per-packet processing. We prototyped and evaluated FENXI on general-purpose servers considering both adversarial and realistic network conditions. Our analysis shows that FENXI can sustain 100 Gbps line rate traffic processing requiring only limited resources, while also dynamically adapting to variable network conditions.

    @inproceedings{AF:CORR-21b, title={FENXI: Deep-learning Traffic Analytics at the Edge}, author={M. {Gallo} and A. {Finamore} and G. {Simon} and D. {Rossi}, year={2021}, booktitle={Symposium on Edge Computing (SEC)}, location={Virtual Event, USA}, doi=, howpublished="" }
  • Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications PDF details
    L. Yang, A. Finamore, F. Jun, D. Rossi
    The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification. While classification of known traffic is a well investigated subject with supervised classification tools (such as ML and DL models) are known to provide satisfactory performance, detection of unknown (or zero-day) traffic is more challenging and typically handled by unsupervised techniques (such as clustering algorithms). In this paper, we share our experience on a commercial-grade DL traffic classification engine that is able to (i) identify known applications from encrypted traffic, as well as (ii) handle unknown zero-day applications. In particular, our contribution for (i) is to perform a thorough assessment of state of the art traffic classifiers in commercial-grade settings comprising few thousands of very fine grained application labels, as opposite to the few tens of classes generally targeted in academic evaluations. Additionally, we contribute to the problem of (ii) detection of zero-day applications by proposing a novel technique, tailored for DL models, that is significantly more accurate and light-weight than the state of the art. Summarizing our main findings, we gather that (i) while ML and DL models are both equally able to provide satisfactory solution for classification of known traffic, however (ii) the non-linear feature extraction process of the DL backbone provides sizeable advantages for the detection of unknown classes.

    @article{AF:CORR-21a, title={Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications}, author={L. {Yang} and A. {Finamore} and F. {Jun} and D. {Rossi}}, year={2021}, journal={CoRR}, doi=, howpublished="" }
  • Are we breaking bubbles as we move? Using a large sample to explore the relationship between urban mobility and segregation PDF details
    S. Park, T. Oshan, A. El Ali, A. Finamore
    Journal of Computers, Environment and Urban Systems (CEUS)
    Segregation often dismantles common activity spaces and isolates people of different backgrounds, leading to irreconcilable inequalities that disfavour the poor and minorities and intensifies societal fragmentation. Therefore, segregation has become an increasing concern and topic of research with studies typically concentrating on the residential communities of a particular racial or socioeconomic group. This paper enhances the residential view of segregation and examines the topic in the context of urban mobility. Specifically, it expands upon prior research by employing large-sample, seamless telecommunication logs of London, UK to provide a holistic view of mobility across the entire socioeconomic spectrum. A method is developed to transform the data to flows between geographic areas with different socioeconomic statuses. Spatial interaction models are then calibrated to examine the impact of both geographical distance and socioeconomic distance on the deterrence of flows and the analysis is extended to analyze the interaction of the two factors. Overall, socioeconomic distance is found to have a subtle effect compared to geographical distance. However, different effects are observed depending on the socioeconomic distance between flows and the deterrence of mobility tends to be the greatest when both physical and socioeconomic distance are high, suggesting that both factors may play a role creating and maintaining segregation.

    @article{AF:CEUS-21, title=Are we breaking bubbles as we move? Using a large sample to explore the relationship between urban mobility and segregation, author={S. {Park} and T. {Oshan} and A. {El Ali} and A. {Finamore}}, year=2021, journal=Computers, Environment and Urban Systems (CEUS), volume=86, pages=101585, doi=10.1016/j.compenvurbsys.2020.101585, howpublished="" }
  • Where Things Roam: Uncovering Cellular IoT/M2M Connectivity PDF details
    A. Lutu, B. Jun, A. Finamore, F. Bustamante, D. Perino
    ACM Internet Measurement Conference (IMC)
    Support for "things" roaming internationally has become critical for Internet of Things (IoT) verticals, from connected cars to smart meters and wearables, and explains the commercial success of Machine-to-Machine (M2M) platforms. We analyze IoT verticals operating with connectivity via IoT SIMs, and present the first large-scale study of commercially deployed IoT SIMs for energy meters. We also present the first characterization of an operational M2M platform and the first analysis of the rather opaque associated ecosystem. For operators, the exponential growth of IoT has meant increased stress on the infrastructure shared with traditional roaming traffic. Our analysis quantifies the adoption of roaming by M2M platforms and the impact they have on the underlying visited Mobile Network Operators (MNOs). To manage the impact of massive deployments of device operating with an IoT SIM, operators must be able to distinguish between the latter and traditional inbound roamers. We build a comprehensive dataset capturing the device population of a large European MNO over three weeks. With this, we propose and validate a classification approach that can allow operators to distinguish inbound roaming IoT devices.

    @inproceedings{AF:IMC-20, title={Where Things Roam: Uncovering Cellular IoT/M2M Connectivity}, author={A. {Lutu} and B. {Jun} and A. {Finamore} and F. {Bustamante} and D. {Perino}}, year={2020}, booktitle={Internet Measurement Conference (IMC)}, location={Virtual Event, USA}, doi={10.1145/3419394.3423661}, howpublished="" }
  • Opening the Deep Pandora Box: Explainable Traffic Classification (poster) PDF details
    C. Beliard, A. Finamore, D. Rossi
    IEEE International Conference on Computer Communications (INFOCOM)
    Fostered by the tremendous success in the image recognition field, recently there has been a strong push for the adoption of Convolutional Neural Networks (CNN) in networks, especially at the edge, assisted by low-power hardware equipment (known as “tensor processing units”) for the acceleration of CNN-related computations. The availability of such hardware has reignited the interest for traffic classification approaches that are based on Deep Learning. However, unlike tree-based approaches that are easy to interpret, CNNs are in essence represented by a large number of weights, whose interpretation is particularly obscure for the human operators. Since human operators will need to deal, troubleshoot, and maintain these automatically learned models, that will replace the more easily human-readable heuristic rules of DPI classification engine, there is a clear need to open the “deep pandora box”, and make it easily accessible for network domain experts. In this demonstration, we shed light in the inference process of a commercial-grade classification engine dealing with hundreds of classes, enriching the classification workflow with tools to enable better understanding of the inner mechanics of both the traffic and the models.

    @INPROCEEDINGS{AF:INFOCOM-20, title={Opening the Deep Pandora Box: Explainable Traffic Classification}, author={C. {Beliard} and A. {Finamore} and D. {Rossi}}, year={2020}, booktitle={IEEE International Conference on Computer Communications (INFOCOM)}, location={Virtual conference}, doi={10.1109/INFOCOMWKSHPS50562.2020.9162704}, howpublished="" }
  • Real-time Deep Learning based Traffic Analytics (poster) PDF details
    M. Gallo, A. Finamore, G. Simon, D. Rossi
    ACM Special Interest Group on Data Communications (SIGCOMM)
    The increased interest towards Deep Learning (DL) tech- nologies has led to the development of a new generation of specialized hardware accelerator [4] such as Graphic Process- ing Unit (GPU) and Tensor Processing Unit (TPU) [1, 2]. The integration of such components in network routers is how- ever not trivial. Indeed, routers typically aim to minimize the overhead of per-packet processing (e.g., Ethernet switching, IP forwarding, telemetry) and design choices (e.g., power, memory consumption) to integrate a new accelerator need to factor in these key requirements. The literature and bench- marks on DL hardware accelerators have overlooked specific router constraints (e.g., strict latency) and focused instead on cloud deployment [3] and image processing. Likewise, there is limited literature regarding DL application on traffic processing at line-rate. Among all hardware accelerators, we are interested in edge TPUs [1, 2]. Since their design focuses on DL inference, edge TPUs matches the vision of operators, who consider running pre-trained DL models in routers with low power drain. Edge TPUs are expected to limit the amount of com- putational resources for inference and to yield a higher ratio of operations per watt footprint than GPUs. This demo aims to investigate the operational points at which edge TPUs become a viable option, using traffic classi- fication as a use case. We sketch the design of a real-time DL traffic classification system, and compare inference speed (i.e., number of classifications per second) of a state-of-the- art Convolutional Neural Network (CNN) model running on different hardware (Central Processing Unit (CPU), GPU, TPU). To constrast their performance, we run stress tests based on synthetic traffic and under different conditions. We collect the results into a dashboard which enables network operators and system designers to both explore the stress test results with regards to their considered operational points, as well as triggering synthetic live tests on top of Ascend 310 TPUs [1].

    @INPROCEEDINGS{AF:SIGCOMM-20, title={Real-time Deep Learning based Traffic Analytics}, author={M. {Gallo} and A. {Finamore} and G. {Simon} and D. {Rossi}}, year={2020}, booktitle={ACM Special Interest Group on Data Communications (SIGCOMM)}, location={Virtual conference}, doi=, howpublished="" }
  • Back in control -- An extensible middle-box on your phone PDF details
    J. Newman, A. Razaghpanah, N. Vallina-Rodriguez, F. Bustamante, M. Allman, D. Perino, A. Finamore

    @article{AF:CORR-20, title={Back in control -- An extensible middle-box on your phone}, author={J. {Newman} and A. {Razaghpanah} and N. {Vallina-Rodriguez} and F. {Bustamante} and M. {Allman} and D. {Perino} and A. {Finamore}, year={2020}, journal={CoRR}, doi=, howpublished="" }
full list


Principal Engineer

HUAWEI · Paris, France

I currently work in the AI4NET group at the Datacom department of Huawei Research in Paris. We work focuses on the integration of DL into network monitoring and distributed telemetry systems, specifically touching few-shot learning, and continuous learning.

September 2019 - Present

Principal Engineer

Telefonica UK / O2 · London, UK

Started as research experiment and later graduated to product, I lead a project aim to create a ML model to predict the satisfaction of the 30M+ O2 customers. The system was based on a combination of Apache Spark analytics and sklearn modeling, and deployed on a 250+ nodes hadoop cluster generating daily predictions using a wide variety of network data feeds from the mobile network packet core. The prediction have been succesfully used to drive operations for both marketing campains and radio teams planning at O2/Telefonica UK.

January 2018 - September 2020

Associate Researcher

Telefonica Research · Barcelona, Spain

I split my time between doing Internet measurements research, and support different Telefonica operational business (OBs) needs for analytics based on network data.

December 2014 - January 2018

Research assistant (PostDoc) / Contractor

CNIT · Politecnico di Torino / Narus Inc · Sunnyvale, CA, U.S

I worked on different research projects related to network measurements, and spent almost 1 year at Narus Inc (now part of Symantec) focusing on cyber security based on network traffic analytics.

January 2012 - December 2014


Politecnico di Torino

Ph.D. in Electronics and Telecommunication Engineering.

2008 - 2012

Politecnico di Torino

M.S. in Computer Engineering

2005 - 2008