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Author details

Domenico Ciuonzo

Also published under: D. Ciuonzo

Publications

115

Citations

4,191

Publications by Year

20072025

Co-Authors:

Giuseppe AcetoSaleh O. Al-JazzarSami A. AldalahmehRashed AlsakarnahMohammad Hossein Anisi

Show All Co-Authors (98)

Domenico Ciuonzo

Also published under: D. Ciuonzo

Affiliation

University of Napoli Federico II

Publication Topics

Deep Learning,
Explainable Artificial Intelligence,
False Discovery Rate,
Inter-arrival Time,
Wireless Sensor Networks,
Deep Learning Models,
Detection Performance,
Fusion Center,
Internet Of Things,
SHapley Additive exPlanations,
Shapley Value,
Traffic Classification

Biography

Domenico Ciuonzo [s'11, M'14, Sm'16], co-Lead GE, is a tenure-track assistant professor at Dept. Electrical Engineering and Information Technologies (DIETI) of University of Naples, Federico II, Italy, since 2022. He was born in Aversa, Italy, on June 29, 1985. He received the B.Sc. and M.Sc. (summa cum laude) degrees in computer engineering and the Ph.D. degree from University of Campania “L. Vanvitelli” (optimum evaluation), Aversa, Italy, in 2007, 2009, and 2013, respectively. In 2011, he was involved in the Visiting Researcher Programme of NATO CMRE, La Spezia, Italy. In 2012, he was a Visiting Scholar with the Dept. Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA. In 2015–16 and 2018, he held visiting ap...

Publications

115

Citations

4,191

Publications by Year

20072025

Co-Authors:

Giuseppe Aceto
Saleh O. Al-Jazzar
Sami A. Aldalahmeh
Rashed Alsakarnah
Mohammad Hossein Anisi

Show All Co-Authors (98)

Author's Published Works

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Showing 1-25 of 115 results

Journals (57)

Conferences (54)

Early Access Articles (2)

Magazines (2)

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Domenico Ciuonzo(105)
Pierluigi Salvo Rossi(44)
Giuseppe Aceto(23)
Antonio Pescapé(23)
Antonio Montieri(22)
Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway(11)
Department of Gas Technology, SINTEF Energy Research, Trondheim, Norway(7)
Department of Electronics and Telecommunications, Norwegian University of Science and Technology, Trondheim, Norway(6)
Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy(5)
University of Napoli “Federico II”, Italy(5)
IEEE Transactions on Signal Processing(10)
IEEE Signal Processing Letters(8)
IEEE Transactions on Aerospace and Electronic Systems(6)
IEEE Transactions on Network and Service Management(6)
IEEE Transactions on Signal and Information Processing over Networks(6)
IEEE(115)
Media(5)
Datasets(2)
A Coruna, Spain(3)
Aachen, Germany(3)
Rome, Italy(3)
Chicago, IL, USA(2)
Naples, Italy(2)
Wireless Sensor Networks(44)
Fusion Rule(35)
Detection Probability(32)
Fusion Center(31)
Decision Fusion(24)

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Explainable Predictive Analysis of the Economic and Financial Performance of Italian Publicly Owned Companies

Idio Guarino;Antonio Montieri;Domenico Ciuonzo;Antonio Pescapè

2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

Year: 2025 | Conference Paper |

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This study examines the prediction of key economic and financial indicators for publicly owned Italian companies using historical time-series data. Four machine learning regression models-Linear Regression, Decision Tree, Random Forest, and XGBoost-are implemented with a sliding window approach to uncover patterns while addressing challenges like missing data and optimal window size. Performance i...Show More

Explainable Predictive Analysis of the Economic and Financial Performance of Italian Publicly Owned Companies

Idio Guarino;Antonio Montieri;Domenico Ciuonzo;Antonio Pescapè

2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

Year: 2025 | Conference Paper |

Massive MIMO Channel-aware Decision Fusion Aided by Reconfigurable Intelligent Surfaces

Domenico Ciuonzo;Alessio Zappone;Marco Di Renzo;Linlong Wu

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

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This paper investigates channel-aware decision fusion empowered 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 a...Show More

Massive MIMO Channel-aware Decision Fusion Aided by Reconfigurable Intelligent Surfaces

Domenico Ciuonzo;Alessio Zappone;Marco Di Renzo;Linlong Wu

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2025 | Conference Paper |

Mapping the Landscape of Generative AI in Network Monitoring and Management

Giampaolo Bovenzi;Francesco Cerasuolo;Domenico Ciuonzo;Davide Di Monda;Idio Guarino;Antonio Montieri;Valerio Persico;Antonio Pescapé

IEEE Transactions on Network and Service Management

Year: 2025 | Early Access Article |

Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification,...Show More

Mapping the Landscape of Generative AI in Network Monitoring and Management

Giampaolo Bovenzi;Francesco Cerasuolo;Domenico Ciuonzo;Davide Di Monda;Idio Guarino;Antonio Montieri;Valerio Persico;Antonio Pescapé

IEEE Transactions on Network and Service Management

Year: 2025 | Early Access Article |

Explainable Few-Shot Class Incremental Learning for Mobile Network Traffic Classification

Francesco Cerasuolo;Giampaolo Bovenzi;Vincenzo Spadari;Domenico Ciuonzo;Antonio Pescapè

GLOBECOM 2024 - 2024 IEEE Global Communications Conference

Year: 2024 | Conference Paper |

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Mobile Traffic Classification (TC) increasingly relies on Machine Learning (ML) and Deep Learning (DL) to enhance network management. Yet, these methods face challenges in (i) classifying new apps, (ii) handling data scarcity from frequent app releases/updates, and (iii) explaining their decisions due to their opaqueness. Class Incremental Learning (CIL) and Few-Shot Learning (FSL) enable to quick...Show More

Explainable Few-Shot Class Incremental Learning for Mobile Network Traffic Classification

Francesco Cerasuolo;Giampaolo Bovenzi;Vincenzo Spadari;Domenico Ciuonzo;Antonio Pescapè

GLOBECOM 2024 - 2024 IEEE Global Communications Conference

Year: 2024 | Conference Paper |

A Comparison Between Classical and Quantum Machine Learning for Mobile App Traffic Classification

Vincenzo Spadari;Idio Guarino;Domenico Ciuonzo;Antonio Pescapé

2024 IEEE/ACM Symposium on Edge Computing (SEC)

Year: 2024 | Conference Paper |

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Network traffic analysis is essential for modern communication systems, focusing on tasks like traffic classification, prediction, and anomaly detection. While classical Machine Learning (ML) and Deep Learning (DL) methods have proven effective, their scalability and real-time performance can be limited by evolving traffic patterns and computational demands. Quantum Machine-Learning (QML) offers a...Show More

A Comparison Between Classical and Quantum Machine Learning for Mobile App Traffic Classification

Vincenzo Spadari;Idio Guarino;Domenico Ciuonzo;Antonio Pescapé

2024 IEEE/ACM Symposium on Edge Computing (SEC)

Year: 2024 | Conference Paper |

Mirage-App×Act-2024: A Novel Dataset for Mobile App and Activity Traffic Analysis

Idio Guarino;Domenico Ciuonzo;Antonio Montieri;Antonio Pescapè

2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

Year: 2024 | Conference Paper |

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This paper presents MIRAGE-App×AcT-2024, a novel dataset originating from the efforts of the MIRAGE project, which collects traffic and corresponding ground-truth data from human-generated mobile-app interactions. By providing detailed insights into traffic patterns, the dataset supports advancements in mobile network optimization, security, and application performance evaluation. To this aim, we ...Show More

Mirage-App×Act-2024: A Novel Dataset for Mobile App and Activity Traffic Analysis

Idio Guarino;Domenico Ciuonzo;Antonio Montieri;Antonio Pescapè

2024 20th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

Year: 2024 | Conference Paper |

A Survey on Explainable Artificial Intelligence for Internet Traffic Classification and Prediction, and Intrusion Detection

Alfredo Nascita;Giuseppe Aceto;Domenico Ciuonzo;Antonio Montieri;Valerio Persico;Antonio Pescapé

IEEE Communications Surveys & Tutorials

Year: 2024 | Early Access Article |

With the increasing complexity and scale of modern 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 classif...Show More

A Survey on Explainable Artificial Intelligence for Internet Traffic Classification and Prediction, and Intrusion Detection

Alfredo Nascita;Giuseppe Aceto;Domenico Ciuonzo;Antonio Montieri;Valerio Persico;Antonio Pescapé

IEEE Communications Surveys & Tutorials

Year: 2024 | Early Access Article |

Suppressing Interrupted Sampling Repeater Jamming by Transceiver Design of Fully Polarimetric Wideband Radars

Xu Cheng;Mengliang Li;Domenico Ciuonzo;Fulai Wang;Pengcheng Gong;Yuntao Wu

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 24 | Journal Article |

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The interrupted sampling repeater jamming (ISRJ) is adept at generating multiple false targets with high fidelity at radar receivers through subsampling, leading to significant challenges in detecting actual targets. This article presents a novel approach to mitigate such jamming by jointly designing the transmit waveform and receive filter of a fully polarimetric wideband radar system. In this st...Show More

Suppressing Interrupted Sampling Repeater Jamming by Transceiver Design of Fully Polarimetric Wideband Radars

Xu Cheng;Mengliang Li;Domenico Ciuonzo;Fulai Wang;Pengcheng Gong;Yuntao Wu

IEEE Sensors Journal

Year: 2024 | Volume: 24, Issue: 24 | Journal Article |

Explainable Deep-Learning Approaches for Packet-Level Traffic Prediction of Collaboration and Communication Mobile Apps

Idio Guarino;Giuseppe Aceto;Domenico Ciuonzo;Antonio Montieri;Valerio Persico;Antonio Pescapè

IEEE Open Journal of the Communications Society

Year: 2024 | Volume: 5 | Journal Article |

Cited by: Papers (10)

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Significant in lifestyle have reshaped the Internet landscape, resulting in notable shifts in both the magnitude of Internet traffic and the diversity of apps utilized. The increased adoption of communication-and-collaboration apps, also fueled by lockdowns in the COVID pandemic years, has heavily impacted the management of network infrastructures and their traffic. A notable characteristic of the...Show More

Explainable Deep-Learning Approaches for Packet-Level Traffic Prediction of Collaboration and Communication Mobile Apps

Idio Guarino;Giuseppe Aceto;Domenico Ciuonzo;Antonio Montieri;Valerio Persico;Antonio Pescapè

IEEE Open Journal of the Communications Society

Year: 2024 | Volume: 5 | Journal Article |

Bayesian Fault Detection and Localization Through Wireless Sensor Networks in Industrial Plants

Gianluca Tabella;Domenico Ciuonzo;Nicola Paltrinieri;Pierluigi Salvo Rossi

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 8 | Journal Article |

Cited by: Papers (7)

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This work proposes a data fusion approach for quickest fault detection and localization within industrial plants via wireless sensor networks. Two approaches are proposed, each exploiting different network architectures. In the first approach, multiple sensors monitor a plant section and individually report their local decisions to a fusion center (FC). The FC provides a global decision after spat...Show More

Bayesian Fault Detection and Localization Through Wireless Sensor Networks in Industrial Plants

Gianluca Tabella;Domenico Ciuonzo;Nicola Paltrinieri;Pierluigi Salvo Rossi

IEEE Internet of Things Journal

Year: 2024 | Volume: 11, Issue: 8 | Journal Article |

AI-Powered Internet Traffic Classification: Past, Present, and Future

Giuseppe Aceto;Domenico Ciuonzo;Antonio Montieri;Valerio Persico;Antonio Pescapé

IEEE Communications Magazine

Year: 2024 | Volume: 62, Issue: 9 | Magazine Article |

Cited by: Papers (6)

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Traffic classification (TC) is pivotal for network traffic management and security. Over time, TC solutions leveraging artificial intelligence (AI) have undergone significant advancements, primarily fueled by machine learning (ML). This article analyzes the history and current state of AI-powered TC on the Internet, highlighting unresolved research questions. Indeed, despite extensive research, ke...Show More

AI-Powered Internet Traffic Classification: Past, Present, and Future

Giuseppe Aceto;Domenico Ciuonzo;Antonio Montieri;Valerio Persico;Antonio Pescapé

IEEE Communications Magazine

Year: 2024 | Volume: 62, Issue: 9 | Magazine Article |

Benchmarking Class Incremental Learning in Deep Learning Traffic Classification

Giampaolo Bovenzi;Alfredo Nascita;Lixuan Yang;Alessandro Finamore;Giuseppe Aceto;Domenico Ciuonzo;Antonio Pescapé;Dario Rossi

IEEE Transactions on Network and Service Management

Year: 2024 | Volume: 21, Issue: 1 | Journal Article |

Cited by: Papers (12)

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Traffic Classification (TC) is experiencing a renewed interest, fostered by the growing popularity of Deep Learning (DL) approaches. In exchange for their proved effectiveness, DL models are characterized by a computationally-intensive training procedure that badly matches the fast-paced release of new (mobile) applications, resulting in significantly limited efficiency of model updates. To addres...Show More

Benchmarking Class Incremental Learning in Deep Learning Traffic Classification

Giampaolo Bovenzi;Alfredo Nascita;Lixuan Yang;Alessandro Finamore;Giuseppe Aceto;Domenico Ciuonzo;Antonio Pescapé;Dario Rossi

IEEE Transactions on Network and Service Management

Year: 2024 | Volume: 21, Issue: 1 | Journal Article |

Adaptive Intrusion Detection Systems: Class Incremental Learning for IoT Emerging Threats

Francesco Cerasuolo;Giampaolo Bovenzi;Christian Marescalco;Francesco Cirillo;Domenico Ciuonzo;Antonio Pescapè

2023 IEEE International Conference on Big Data (BigData)

Year: 2023 | Conference Paper |

Cited by: Papers (5)

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In the evolving landscape of Internet of Things (IoT) security, the need for continuous adaptation of defenses is critical. Class Incremental Learning (CIL) can provide a viable solution by enabling Machine Learning (ML) and Deep Learning (DL) models to $( i)$ learn and adapt to new attack types (0-day attacks), $( ii)$ retain their ability to detect known threats, (iii) safeguard computational ef...Show More

Adaptive Intrusion Detection Systems: Class Incremental Learning for IoT Emerging Threats

Francesco Cerasuolo;Giampaolo Bovenzi;Christian Marescalco;Francesco Cirillo;Domenico Ciuonzo;Antonio Pescapè

2023 IEEE International Conference on Big Data (BigData)

Year: 2023 | Conference Paper |

Deep Recurrent Graph Convolutional Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

Hossein Darvishi;Domenico Ciuonzo;Pierluigi Salvo Rossi

IEEE Sensors Journal

Year: 2023 | Volume: 23, Issue: 23 | Journal Article |

Cited by: Papers (12)

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The rapid adoption of Internet-of-Things (IoT) and digital twins (DTs) technologies within industrial environments has highlighted diverse critical issues related to safety and security. Sensor failure is one of the major threats compromising DTs operations. In this article, for the first time, we address the problem of sensor fault detection, isolation, and accommodation (SFDIA) in large-size net...Show More

Deep Recurrent Graph Convolutional Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

Hossein Darvishi;Domenico Ciuonzo;Pierluigi Salvo Rossi

IEEE Sensors Journal

Year: 2023 | Volume: 23, Issue: 23 | Journal Article |

Fine-Grained Traffic Prediction of Communication-and-Collaboration Apps Via Deep-Learning: A First Look at Explainability

Idio Guarino;Giuseppe Aceto;Domenico Ciuonzo;Antonio Montieri;Valerio Persico;Antonio Pescapé

ICC 2023 - IEEE International Conference on Communications

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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The lifestyle change originated from the COVID-19 pandemic has caused a measurable impact on Internet traffic in terms of volume and application mix, with a sudden increase in usage of communication-and-collaboration apps. In this work, we focus on four of these apps (Skype, Teams, Webex, and Zoom), whose traffic we collect, reliably label at fine (i.e. per-activity) granularity, and analyze from ...Show More

Fine-Grained Traffic Prediction of Communication-and-Collaboration Apps Via Deep-Learning: A First Look at Explainability

Idio Guarino;Giuseppe Aceto;Domenico Ciuonzo;Antonio Montieri;Valerio Persico;Antonio Pescapé

ICC 2023 - IEEE International Conference on Communications

Year: 2023 | Conference Paper |

Time-Aware Distributed Sequential Detection of Gas Dispersion via Wireless Sensor Networks

Gianluca Tabella;Domenico Ciuonzo;Yasin Yilmaz;Xiaodong Wang;Pierluigi Salvo Rossi

IEEE Transactions on Signal and Information Processing over Networks

Year: 2023 | Volume: 9 | Journal Article |

Cited by: Papers (5)

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This work addresses the problem of detecting gas dispersions through concentration sensors with wireless transmission capabilities organized as a distributed Wireless Sensor Network (WSN). The concentration sensors in the WSN perform local sequential detection (SD) and transmit their individual decisions to the Fusion Center (FC) according to a transmission rule designed to meet the low-energy req...Show More

Time-Aware Distributed Sequential Detection of Gas Dispersion via Wireless Sensor Networks

Gianluca Tabella;Domenico Ciuonzo;Yasin Yilmaz;Xiaodong Wang;Pierluigi Salvo Rossi

IEEE Transactions on Signal and Information Processing over Networks

Year: 2023 | Volume: 9 | Journal Article |

Optimization Based Sensor Placement for Multi-Target Localization With Coupling Sensor Clusters

Linlong Wu;Nitesh Sahu;Sheng Xu;Prabhu Babu;Domenico Ciuonzo

IEEE Transactions on Signal and Information Processing over Networks

Year: 2023 | Volume: 9 | Journal Article |

Cited by: Papers (1)

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Since the Cramér-Rao lower bounds (CRLB) of target localization depends on the sensor geometry explicitly, sensor placement becomes a crucial issue in many target or source localization applications. In the context of simultaneous time-of-arrival (TOA) based multi-target localization, we consider the sensor placement for multiple sensor clusters in the presence of shared sensors. To minimize the m...Show More

Optimization Based Sensor Placement for Multi-Target Localization With Coupling Sensor Clusters

Linlong Wu;Nitesh Sahu;Sheng Xu;Prabhu Babu;Domenico Ciuonzo

IEEE Transactions on Signal and Information Processing over Networks

Year: 2023 | Volume: 9 | Journal Article |

Non-cooperative Distributed Detection via Federated Sensor Networks

Domenico Ciuonzo;Apoorva Chawla;Pierluigi Salvo Rossi

2023 IEEE Radar Conference (RadarConf23)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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In this study, we address the challenge of non-cooperative target detection by federating two wireless sensor networks. The objective is to capitalize on the diversity achievable from both sensing and reporting phases. The target's presence results in an unknown signal that is influenced by unknown distances between the sensors and target, as well as by symmetrical and single-peaked noise. The fus...Show More

Non-cooperative Distributed Detection via Federated Sensor Networks

Domenico Ciuonzo;Apoorva Chawla;Pierluigi Salvo Rossi

2023 IEEE Radar Conference (RadarConf23)

Year: 2023 | Conference Paper |

Constant-Modulus Waveform Design With Polarization-Adaptive Power Allocation in Polarimetric Radar

Linlong Wu;Xu Cheng;Huiping Huang;Domenico Ciuonzo;Bhavani Shankar;Björn Ottersten

IEEE Transactions on Signal Processing

Year: 2023 | Volume: 71 | Journal Article |

Cited by: Papers (13)

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In polarimetric radars, corresponding to the polarized antennas, exploiting waveform diversity along the polarization dimension becomes accessible. In this article, we aim to maximize the signal-to-interference plus noise ratio (SINR) of a polarimetric radar by optimizing the transmit polarimetric waveform, the power allocation on its horizontal and vertical polarization segments, and the receivin...Show More

Constant-Modulus Waveform Design With Polarization-Adaptive Power Allocation in Polarimetric Radar

Linlong Wu;Xu Cheng;Huiping Huang;Domenico Ciuonzo;Bhavani Shankar;Björn Ottersten

IEEE Transactions on Signal Processing

Year: 2023 | Volume: 71 | Journal Article |

Sparse Bayesian Learning Assisted Decision Fusion in Millimeter Wave Massive MIMO Sensor Networks

Apoorva Chawla;Domenico Ciuonzo;Pierluigi Salvo Rossi

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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This paper investigates decision fusion in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless sensor network (WSNs), where the sparse Bayesian learning (SBL) algorithm is employed to estimate the channel between the sensors and the fusion center (FC). We present low-complexity fusion rules based on the hybrid combining architecture for the considered framework. Further...Show More

Sparse Bayesian Learning Assisted Decision Fusion in Millimeter Wave Massive MIMO Sensor Networks

Apoorva Chawla;Domenico Ciuonzo;Pierluigi Salvo Rossi

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Year: 2023 | Conference Paper |

Improving Performance, Reliability, and Feasibility in Multimodal Multitask Traffic Classification with XAI

Alfredo Nascita;Antonio Montieri;Giuseppe Aceto;Domenico Ciuonzo;Valerio Persico;Antonio Pescapé

IEEE Transactions on Network and Service Management

Year: 2023 | Volume: 20, Issue: 2 | Journal Article |

Cited by: Papers (25)

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The promise of Deep Learning (DL) in solving hard problems such as network Traffic Classification (TC) is being held back by the severe lack of transparency and explainability of this kind of approaches. To cope with this strongly felt issue, the field of eXplainable Artificial Intelligence (XAI) has been recently founded, and is providing effective techniques and approaches. Accordingly, in this ...Show More

Improving Performance, Reliability, and Feasibility in Multimodal Multitask Traffic Classification with XAI

Alfredo Nascita;Antonio Montieri;Giuseppe Aceto;Domenico Ciuonzo;Valerio Persico;Antonio Pescapé

IEEE Transactions on Network and Service Management

Year: 2023 | Volume: 20, Issue: 2 | Journal Article |

A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

Hossein Darvishi;Domenico Ciuonzo;Pierluigi Salvo Rossi

IEEE Sensors Journal

Year: 2023 | Volume: 23, Issue: 3 | Journal Article |

Cited by: Papers (49)

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Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomalies instantaneously in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus pavin...Show More

A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

Hossein Darvishi;Domenico Ciuonzo;Pierluigi Salvo Rossi

IEEE Sensors Journal

Year: 2023 | Volume: 23, Issue: 3 | Journal Article |

Joint Design of Horizontal and Vertical Polarization Waveforms for Polarimetric Radar via SINR Maximization

Xu Cheng;Linlong Wu;Domenico Ciuonzo;Wei Wang

IEEE Transactions on Aerospace and Electronic Systems

Year: 2023 | Volume: 59, Issue: 3 | Journal Article |

Cited by: Papers (15)

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For an extended target with different polarimetric responses, one way of improving the detection performance is to exploit waveform diversity on the dimension of polarization. In this article, we focus on the joint design of transmit signal and receive filter for polarimetric radars with local waveform constraints. Considering the signal-to-interference-plus-noise ratio (SINR) as the figure of mer...Show More

Joint Design of Horizontal and Vertical Polarization Waveforms for Polarimetric Radar via SINR Maximization

Xu Cheng;Linlong Wu;Domenico Ciuonzo;Wei Wang

IEEE Transactions on Aerospace and Electronic Systems

Year: 2023 | Volume: 59, Issue: 3 | Journal Article |

Exploring a Modular Architecture for Sensor Validation in Digital Twins

Hossein Darvishi;Domenico Ciuonzo;Pierluigi Salvo Rossi

2022 IEEE Sensors

Year: 2022 | Conference Paper |

Cited by: Papers (3)

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Decision-support systems rely on data exchange between digital twins (DTs) and physical twins (PTs). Faulty sensors (e.g, due to hardware/software failures) deliver unreliable data and potentially generate critical damages. Prompt sensor fault detection, isolation and accommodation (SFDIA) plays a crucial role in DT design. In this respect, data-driven approaches to SFDIA have recently shown to be...Show More

Exploring a Modular Architecture for Sensor Validation in Digital Twins

Hossein Darvishi;Domenico Ciuonzo;Pierluigi Salvo Rossi

2022 IEEE Sensors

Year: 2022 | Conference Paper |

On the use of Machine Learning Approaches for the Early Classification in Network Intrusion Detection

Idio Guarino;Giampaolo Bovenzi;Davide Di Monda;Giuseppe Aceto;Domenico Ciuonzo;Antonio Pescapé

2022 IEEE International Symposium on Measurements & Networking (M&N)

Year: 2022 | Conference Paper |

Cited by: Papers (14)

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Current intrusion detection techniques cannot keep up with the increasing amount and complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to apply deep packet inspection approaches. In recent years, Machine Learning techniques have been proposed for post-mortem detection of network attacks, and many datasets have been shared by research groups and organizations...Show More

On the use of Machine Learning Approaches for the Early Classification in Network Intrusion Detection

Idio Guarino;Giampaolo Bovenzi;Davide Di Monda;Giuseppe Aceto;Domenico Ciuonzo;Antonio Pescapé

2022 IEEE International Symposium on Measurements & Networking (M&N)

Year: 2022 | Conference Paper |

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