Domenico Ciuonzo
Domenico Ciuonzo is a Tenure-Track Professor at DIETI, University of Naples, Federico II, Italy. He received the B.Sc. and M.Sc. (summa) degrees in computer engineering and the Ph.D. degree from the University of Campania "L. Vanvitelli", Aversa, Italy, in 2007, 2009, and 2013, respectively.
Since 2011, he has held several visiting appointments: NATO CMRE, IT (2011); ECE Department, University of Connecticut, US (2012); Department of Electronics and Telecommunications, NTNU, Trondheim, NOR (2015, 2016 and 2022); Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Castelldefels, ES (2018).
Since ‘14 he has served as Associate and Area/Senior Editor for several IET, Elsevier and IEEE journals. In 2022 he has served as Lead Guest Editor for a Special issue on the IEEE Internet of Things Magazine. Currently, he is an Executive Editor for the IEEE Communications Letters. His reviewing and editorial activities were recognized by the IEEE Communications Letters, IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, and MDPI, which nominated him Exemplary Reviewer and Editor, respectively.
He is the recipient of two Best Paper awards (IEEE ICCCS 2019 and Elsevier Computer Networks 2020), the 2019 Exceptional Service award from IEEE AESS, the 2020 Early-Career Technical Achievement award from IEEE SENSORS COUNCIL for sensor networks/systems and the 2021 Early-Career Award from IEEE AESS for contributions to decentralized inference and sensor fusion in networked sensor systems.
His research interests fall within the areas of data fusion, network traffic analysis, statistical signal processing, IoT & wireless sensor networks, and machine learning. He has co-authored 120+ journal and conference publications to top-notch venues. He is co-author of the book “Data Fusion in Wireless Sensor Networks: A Statistical Signal Processing Perspective”, published by IET in 2019. Since 2016 he is an IEEE Senior Member. D. Ciuonzo has served and serves as independent reviewer/evaluator of research and implementation projects and project proposals co-funded by many EU and non-EU parties.
Address: Department of Electrical and Information Technologies (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples (IT).
Since 2011, he has held several visiting appointments: NATO CMRE, IT (2011); ECE Department, University of Connecticut, US (2012); Department of Electronics and Telecommunications, NTNU, Trondheim, NOR (2015, 2016 and 2022); Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Castelldefels, ES (2018).
Since ‘14 he has served as Associate and Area/Senior Editor for several IET, Elsevier and IEEE journals. In 2022 he has served as Lead Guest Editor for a Special issue on the IEEE Internet of Things Magazine. Currently, he is an Executive Editor for the IEEE Communications Letters. His reviewing and editorial activities were recognized by the IEEE Communications Letters, IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, and MDPI, which nominated him Exemplary Reviewer and Editor, respectively.
He is the recipient of two Best Paper awards (IEEE ICCCS 2019 and Elsevier Computer Networks 2020), the 2019 Exceptional Service award from IEEE AESS, the 2020 Early-Career Technical Achievement award from IEEE SENSORS COUNCIL for sensor networks/systems and the 2021 Early-Career Award from IEEE AESS for contributions to decentralized inference and sensor fusion in networked sensor systems.
His research interests fall within the areas of data fusion, network traffic analysis, statistical signal processing, IoT & wireless sensor networks, and machine learning. He has co-authored 120+ journal and conference publications to top-notch venues. He is co-author of the book “Data Fusion in Wireless Sensor Networks: A Statistical Signal Processing Perspective”, published by IET in 2019. Since 2016 he is an IEEE Senior Member. D. Ciuonzo has served and serves as independent reviewer/evaluator of research and implementation projects and project proposals co-funded by many EU and non-EU parties.
Address: Department of Electrical and Information Technologies (DIETI), University of Naples Federico II, Via Claudio 21, 80125 Naples (IT).
less
Related Authors
InterestsView All (36)
Uploads
Journal papers by Domenico Ciuonzo
networks, the demand for transparent and interpretable Artificial
Intelligence (AI) models has surged. This survey comprehensively
reviews the current state of eXplainable Artificial Intelligence
(XAI) methodologies in the context of Network Traffic Analysis
(NTA) (including tasks such as traffic classification, intrusion detection,
attack classification, and traffic prediction), encompassing
various aspects such as techniques, applications, requirements,
challenges, and ongoing projects. It explores the vital role of XAI
in enhancing network security, performance optimization, and
reliability. Additionally, this survey underscores the importance
of understanding why AI-driven decisions are made, emphasizing
the need for explainability in critical network environments. By
providing a holistic perspective on XAI for Internet NTA, this
survey aims to guide researchers and practitioners in harnessing
the potential of transparent AI models to address the intricate
challenges of modern network management and security.
networks, the demand for transparent and interpretable Artificial
Intelligence (AI) models has surged. This survey comprehensively
reviews the current state of eXplainable Artificial Intelligence
(XAI) methodologies in the context of Network Traffic Analysis
(NTA) (including tasks such as traffic classification, intrusion detection,
attack classification, and traffic prediction), encompassing
various aspects such as techniques, applications, requirements,
challenges, and ongoing projects. It explores the vital role of XAI
in enhancing network security, performance optimization, and
reliability. Additionally, this survey underscores the importance
of understanding why AI-driven decisions are made, emphasizing
the need for explainability in critical network environments. By
providing a holistic perspective on XAI for Internet NTA, this
survey aims to guide researchers and practitioners in harnessing
the potential of transparent AI models to address the intricate
challenges of modern network management and security.
by massive MIMO systems and reconfigurable intelligent surfaces
(RIS). By integrating both, we aim to improve goal-oriented (fusion)
performance despite the unique propagation challenges introduced.
Specifically, we investigate traditional favorable propagation properties
in the context of RIS-aided Massive MIMO decision fusion. The
above analysis is then leveraged (i) to design three sub-optimal simple
fusion rules suited for the large-array regime and (ii) to devise an
optimization criterion for RIS reflection coefficients based on longterm
channel statistics. Simulation results confirm the appeal of the
presented design.
TC comes with its own challenges and requirements that are even exacerbated in a mobile-traffic context, such as: (a) the adoption of encrypted protocols (b) a large number of apps to discriminate from, (c) the dynamic nature of network traffic and, more importantly, (d) the lack of a satisfactory flow-level Ground Truth (GT) to train the classification algorithms on, and test and compare them against.
For this reason, this work proposes a novel self-supervised TC architecture composed of two main blocks: (i) an automatic GT generation tool and (ii) a Multi-Classifier System (MCS). The first block automatically produces a corpus of traffic traces with flow-level labeling, the label being the package name and version (uniquely identifying the mobile app); this is exploited to rapidly train (or re-train), in a supervised way, the proposed MCS, which is then employed on classification of true (human-generated) mobile traffic.
In more detail, in the first block of the proposed system each app package of interest is automatically installed and run on a (physical- or virtual-) device connected to a network where all traffic generated or received by the device can be captured. Then the Graphical User Interface (GUI) of the app is explored, generating events as taps and keystrokes, causing the generation of network traffic. The GUI explorer is based on Android GUI Ripper, a tool implementing both Random and Active Learning techniques. The device is instrumented with a logger that records all network-related system calls originated by the exercised app to properly associate traffic flows with originating process names, thus avoiding mislabeling traffic from other apps or from the operating system. The traffic generated by the device is captured on a host (wireless access point) from which the device can also be controlled (e.g. via USB).
The second block is represented by a MCS which intelligently-combines decisions from state-of-the-art (base) classifiers specifically devised for mobile- and encrypted-traffic classification. The MCS is intended to overcome the deficiencies of each single classifier (not improvable over a certain bound, despite efforts in “tuning”) and provide improved performance with respect to any of the base classifiers. The proposed MCS is not restricted to a specific set of classification algorithms and also allows for modularity of classifiers' selection in the pool. Additionally, the MCS can adopt several types of combiners (based on both hard and soft approaches) developed in the literature constituting a wide spectrum of achievable performance, operational complexity, and training set requirements.
Preliminary results show that our system is able to: (i) automatically run mobile apps making them generate sufficient traffic to train a MCS; (ii) obtain promising results in terms of classification accuracy of new mobile apps traffic.