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Domenico Ciuonzo
Researcher at University of Naples Federico II
Publications - 134
Citations - 4994
Domenico Ciuonzo is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Wireless sensor network & MIMO. The author has an hindex of 35, co-authored 116 publications receiving 3404 citations. Previous affiliations of Domenico Ciuonzo include Seconda Università degli Studi di Napoli & Information Technology University.
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Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges
TL;DR: Different state-of-the-art DL techniques from (standard) TC are reproduced, dissected, and set into a systematic framework for comparison, including also a performance evaluation workbench, to propose deep learning classifiers based on automatically extracted features, able to cope with encrypted traffic, and reflecting their complex traffic patterns.
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MIMETIC: Mobile encrypted traffic classification using multimodal deep learning
TL;DR: A novel multimodal DL framework for encrypted TC is proposed, named MIMETIC, able to capitalize traffic data heterogeneity (by learning both intra- and inter-modality dependences), overcome performance limitations of existing (myopic) single- modality DL-based TC proposals, and support the challenging mobile scenario.
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Massive MIMO Channel-Aware Decision Fusion
TL;DR: The aim is the development of (widely) linear fusion rules, as opposed to the unsuitable optimum log-likelihood ratio (LLR), which can effectively benefit from performance improvement via a large array, differently from existing suboptimal alternatives.
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Channel-Aware Decision Fusion in Distributed MIMO Wireless Sensor Networks: Decode-and-Fuse vs. Decode-then-Fuse
TL;DR: This work presents the optimal rule and derive sub-optimal fusion rules, as alternatives with improved numerical stability, reduced complexity and lower system knowledge required, and Simulation results for performances are presented both under Neyman-Pearson and Bayesian frameworks.
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Multi-classification approaches for classifying mobile app traffic
TL;DR: This paper proposes a multi-classification approach, intelligently-combining outputs from state-of-the-art classifiers proposed for mobile and encrypted traffic classification, and demonstrates that classification performance can be improved according to all considered metrics.