Traffic Classification (TC) is pivotal for network management, cybersecurity, and Quality of Experience (QoE) monitoring. However, while Deep Learning (DL) has significantly advanced TC, most existing works assume static, idealized conditions, overlooking key challenges of real-world deployments—such as traffic variability, routing asymmetries, out-of-order packet arrivals, and partial visibility at the Vantage Points (VPs). This motivates the need for robustness evaluations under such scenarios. In this work, we investigate the robustness of state-of-the-art (SOTA) TC models under realistic, yet controlled, perturbation scenarios. Specifically, we introduce novel, model-agnostic traffic perturbations—simulating time jitter, retransmissions, and partial visibility—to reflect conditions commonly encountered in live network traffic. We evaluate our approach on three public datasets—i.e., VPN-16 , MIRAGE-19 , and MIRAGE-24 —and show how Mimetic-Enhanced , a multimodal model, tends to outperform two representative single-modal counterparts both in terms of TC effectiveness on clean traffic and robustness under perturbations. Nonetheless, our analysis also reveals that multimodal models remain vulnerable under specific perturbation settings. To address this limitation, we propose a model-agnostic perturbation-aware training framework based on Supervised Data Augmentation ( Aug ) and Contrastive Learning ( CL )—considering both self-supervised and supervised variants. Unlike architecture-specific solutions, our approach operates at the learning strategy level , allowing it to be seamlessly applied to diverse classifiers without requiring structural modifications. Adopting Mimetic-Enhanced as a primary multimodal case study, we integrate the proposed strategies into its two-stage training pipeline. Experimental results demonstrate that perturbation-aware training not only improves TC effectiveness on clean (i.e., unperturbed) traffic—particularly when applied across both training stages—but also significantly strengthens the model’s robustness under diverse and realistic perturbation scenarios. Furthermore, we investigate Out-of-Distribution (OOD) detection, model calibration, and TC effectiveness in low-data regimes. Finally, we explicitly demonstrate the framework’s generalizability by validating it on other SOTA architectures, spanning both single- and multi-modal approaches.

Ensuring network security is increasingly challenging, especially in the Internet of Things (IoT) domain, where threats are diverse, rapidly evolving, and often device-specific. Hence, Network Intrusion Detection Systems (NIDSs) require(i)being trained on network traffic gathered in different collection points to cover the attack traffic heterogeneity,(ii)continuously learning emerging threats (viz., 0-day attacks), and(iii)be able to take attack countermeasures as soon as possible. In this work, we aim to improve Artificial Intelligence (AI)-based NIDS design & maintenance by integrating Federated Learning (FL) and Class Incremental Learning (CIL). Specifically, we devise a Federated Class Incremental Learning (FCIL) framework–suited for early-detection settings—that supports decentralized and continual model updates, investigating the non-trivial intersection of FL algorithms with state-of-the-art CIL techniques to enable scalable, privacy-preserving training in highly non-IID environments. We evaluate FCIL on three IoT datasets across different client scenarios to assess its ability to learn new threats and retain prior knowledge. The experiments assess potential key challenges in generalization and few-sample training, and compare NIDS performance to monolithic and centralized baselines.

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 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 long-term channel statistics. Simulation results confirm the appeal of the presented design.

Network traffic has experienced substantial growth in recent years, requiring the implementation of more advanced techniques for effective management. In this context, Traffic Classification (TC) helps in successfully handling the network by identifying what is flowing through it. Nowadays, data-driven approaches—viz., Machine Learning (ML) and Deep Learning (DL)—are widely employed to address this task. However, these approaches struggle to keep pace with the ever-changing nature of traffic due to the introduction of new or updated services/apps and exhibit a decision-making process not interpretable. Furthermore, network traffic can vary significantly by geographic area , requiring a decentralized privacy-preserving approach to update classifiers collaboratively. In this work, we propose a Federated Class Incremental Learning (FCIL) framework that integrates Class Incremental Learning (CIL) and Federated Learning (FL) for network TC while incorporating a comprehensive eXplainable Artificial Intelligence (XAI) methodology, tackling the challenges of updating traffic classifiers, managing the geographic diversity of traffic along with data privacy, and interpreting the decision-making process, respectively. To assess our proposal, we leverage two publicly available encrypted network traffic datasets. Our findings uncover that, in small networks, fewer synchronizations facilitate retaining old knowledge, while larger networks reveal an approach-dependent pattern, yet still exhibiting good retention performance. Moreover, in both small and larger networks, frequent updates enhance the assimilation of new information . Notably, B i C + is the most effective approach in small networks (i.e., 2 clients) while i C a R L + performs best in larger networks (i.e., 10 clients), obtaining 82% and 79% F1 on C E S N E T - T L S 2 2 , respectively. Leveraging XAI techniques, we analyze the effect of incorporating a per-client bias correction layer. By integrating sample-based and attribution-based explanations, we provide detailed insights into the decision-making process of FCIL approaches .

The advent of the Internet of Things (IoT) has ushered in an era of unprecedented connectivity and convenience, enabling everyday objects to gather and share data autonomously, revolutionizing industries, and improving quality of life. However, this interconnected landscape poses cybersecurity challenges, as the expanded attack surface exposes vulnerabilities ripe for exploitation by malicious actors. The surge in network attacks targeting IoT devices underscores the urgency for robust and evolving security measures. Class Incremental Learning (CIL) emerges as a dynamic strategy to address these challenges, empowering Machine Learning (ML) and Deep Learning (DL) models to adapt to evolving threats while maintaining proficiency in detecting known ones. In the context of IoT security, characterized by the constant emergence of novel attack types, CIL offers a powerful means to enhance Network Intrusion Detection Systems (NIDS) resilience and network security. This paper aims to investigate how CIL methods can support the evolution of NIDS within IoT networks ( i ) by evaluating both attack detection and classification tasks — optimizing hyperparameters associated with the incremental update or to the traffic input definition—and ( i i ) by addressing also key research questions related to real-world NIDS challenges —such as the explainability of decisions, the robustness to perturbation of traffic inputs, and scenarios with a scarcity of new-attack samples. Leveraging 4 recently-collected and comprehensive IoT attack datasets , the study aims to evaluate the effectiveness of CIL techniques in classifying 0-day attacks.

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs) utilizing channel-aware binary-decision fusion over a shared flat-fading channel. A reconfigurable metasurface, positioned in the near-field of a limited number of receive antennas, is integrated to enable a holographic Decision Fusion (DF) system. This approach minimizes the need for multiple RF chains while leveraging the benefits of a large array. The optimal fusion rule for a fixed metasurface configuration is derived, alongside two suboptimal joint fusion rule and metasurface design strategies. These suboptimal approaches strike a balance between reduced complexity and lower system knowledge requirements, making them practical alternatives. The design objective focuses on effectively conveying the information regarding the phenomenon of interest to the FC while promoting energy-efficient data analytics aligned with the Internet of Things (IoT) paradigm. Simulation results underscore the viability of holographic DF, demonstrating its advantages even with suboptimal designs and highlighting the significant energy-efficiency gains achieved by the proposed system.

Distributed detection of an unknown deterministic signal is studied in a wireless sensor network with low-cost nodes. Sensors apply one-bit quantization to noisy observations and transmit over Rayleigh fading to a Fusion Center (FC). Score tests, including variants using observed Fisher information, are proposed as low-complexity alternatives to the generalized likelihood ratio test (GLRT). The FC performs joint decoding and fusion, optimizing quantizers in a channel-aware manner to improve asymptotic detection performance. Simulations confirm the appeal of score-based tests under realistic wireless conditions.

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, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for large-scale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.

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 quickly update models and learn with very limited data, respectively, while eXplainable AI (XAI) enhances decision transparency. In this work, we merge CIL and FSL to update models with new apps under few sample constraints. First, we introduce SWEET, a CIL-originated approach that flexibly accommodates different few-sample scenarios via adaptive traffic augmentation. Second, we devise an XAI methodology based on visualization-, sample-, and attribution-based techniques to explore practical incremental learning. We evaluate both contributions on the public mobile traffic dataset MIRAGE19.

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 promising alternative by utilizing quantum computing's parallelism. This paper examines QML's application in mobile traffic classification, comparing classical methods such as Multi-layer Perceptron (MLP) and Convolutional Neural Networks (CNNs) with Quantum Neural Networks (QNNs) using different embedding types. Our experiments, conducted on the MIRAGE-COVID-CCMA-2022 dataset, show that QNNs achieve competitive performance, indicating QML's potential for efficient large-scale traffic classification in future networks.

In the ever-changing digital environment, ensuring the ongoing effectiveness of traffic analysis and security measures is crucial. Therefore, Class Incremental Learning (CIL) in encrypted Traffic Classification (TC) is essential for adapting to evolving network behaviors and the rapid development of new applications. However, the application of CIL techniques in the TC domain is not straightforward, usually leading to unsatisfactory performance figures. Specifically, the improvement goal is to reduce forgetting on old apps and increase the capacity in learning new ones, in order to improve overall classification performance— reducing the drop from a model “trained-from-scratch”. The contribution of this work is the design of a novel fine-tuning approach called MEMENTO, which is obtained through the careful design of different building blocks: memory management, model training, and rectification strategies. In detail, we propose the application of traffic biflows augmentation strategies to better capitalize on old apps biflows, we introduce improvements in the distillation stage, and we design a general rectification strategy that includes several existing proposals. To assess our proposal, we leverage two publicly-available encrypted network traffic datasets, i.e., MIRAGE19 and CESNET-TLS22. As a result, on both datasets MEMENTO achieves a significant improvement in classifying new apps (w.r.t. the best-performing alternative, i.e., BiC) while maintaining stable performance on old ones. Equally important, MEMENTO achieves satisfactory overall TC performance, filling the gap toward a trained-from-scratch model and offering a considerable gain in terms of time (up to 10× speed-up) to obtain up-to-date and running classifiers. The experimental evaluation relies on a comprehensive performance evaluation workbench for CIL proposals, which is based on a wider set of metrics (as opposed to the existing literature in TC).

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. The fusion center provides a global decision after spatial aggregation of the local decisions. A post-processing center subsequently processes these global decisions in time, which performs quick detection and localization. Alternatively, the fusion center directly performs a spatio-temporal aggregation directed at quickest detection, together with a possible estimation of the faulty item. Both architectures are provided with a feedback system where the network’s highest hierarchical level transmits parameters to the lower levels. The two proposed approaches model the faults according to a Bayesian criterion and exploit the knowledge of the reliability model of the plant under monitoring. Moreover, adaptations of the well-known Shewhart and CUSUM charts are provided to fit the different architectures and are used for comparison purposes. Finally, the algorithms are tested via simulation on an active Oil and Gas subsea production system, and performances are provided.

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 address this shortcoming, in this work we systematically explore Class Incremental Learning (CIL) techniques, aimed at adding new apps/services to pre-existing DL-based traffic classifiers without a full retraining, hence speeding up the model’s updates cycle. We investigate a large corpus of state-of-the-art CIL approaches for the DL-based TC task, and delve into their working principles to highlight relevant insight, aiming to understand if there is a case for CIL in TC. We evaluate and discuss their performance varying the number of incremental learning episodes, and the number of new apps added for each episode. Our evaluation is based on the publicly available$\mathtt {MIRAGE19}$dataset comprising traffic of 40 popular Android applications, fostering reproducibility. Despite our analysis reveals their infancy, CIL techniques are a promising research area on the roadmap towards automated DL-based traffic analysis systems.

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 efficiency (i.e. no full re-training). In IoT security, where novel attacks frequently emerge, CIL offers an effective tool to enhance Intrusion Detection Systems (IDS) and secure network environments. In this study, we explore how CIL approaches empower DL-based IDS in IoT networks, using the publicly-available IoT-23 dataset. Our evaluation focuses on two essential aspects of an IDS: $( a)$ attack classification and $( b)$ misuse detection. A thorough comparison against a fully-retrained IDS, namely starting from scratch, is carried out. Finally, we place emphasis on interpreting the predictions made by incremental IDS models through eXplainable AI (XAI) tools, offering insights into potential avenues for improvement.

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, a deflection coefficient maximization-based optimization framework is developed to determine the transmit signaling matrix that can improve detection performance. The performance of the proposed fusion rule is presented through simulation results demonstrating the validation of the analytical findings.

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 the viewpoint of traffic prediction. The outcome of this analysis is informative for a number of network management tasks, including monitoring, planning, resource provisioning, and (security) policy enforcement. To this aim, we employ state-of-the-art multitask deep learning approaches to assess to which degree the traffic generated by these apps and their different use cases (i.e. activities: audio-call, video-call, and chat) can be forecast at packet level. The experimental analysis investigates the performance of the considered deep learning architectures, in terms of both traffic-prediction accuracy and complexity, and the related trade-off. Equally important, our work is a first attempt at interpreting the results obtained by these predictors via eXplainable Artificial Intelligence (XAI).

The Internet of Things (IoT) is a key enabler in closing the loop in Cyber-Physical Systems, providing “smartness” and thus additional value to each monitored/controlled physical asset. Unfortunately, these devices are more and more targeted by cyberattacks because of their diffusion and of the usually limited hardware and software resources. This calls for designing and evaluating new effective approaches for protecting IoT systems at the network level (Network Intrusion Detection Systems, NIDSs). These in turn are challenged by the heterogeneity of IoT devices and the growing volume of transmitted data. To tackle this challenge, we select a Deep Learning architecture to perform unsupervised early anomaly detection. With a data-driven approach, we explore in-depth multiple design choices and exploit the appealing structural properties of the selected architecture to enhance its performance. The experimental evaluation is performed on two recent and publicly available IoT datasets (IoT-23 and Kitsune). Finally, we adopt an adversarial approach to investigate the robustness of our solution in the presence of Label Flipping poisoning attacks. The experimental results highlight the improved performance of the proposed architecture, in comparison to both well-known baselines and previous proposals.

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 work we investigate interpretability via XAIbased techniques to understand and improve the behavior of state-of-the-art multimodal and multitask DL traffic classifiers. Using a publicly available security-related dataset (ISCX VPNNONVPN), we explore and exploit XAI techniques to characterize the considered classifiers providing global interpretations (rather than sample-based ones), and define a novel classifier, DISTILLER-EVOLVED, optimized along three objectives: performance, reliability, feasibility. The proposed methodology proves as highly appealing, allowing to much simplify the architecture to get faster training time and shorter classification time, as fewer packets must be collected. This is at the expenses of negligible (or even positive) impact on classification performance, while understanding and controlling the interplay between inputs, model complexity, performance, and reliability.

Traffic Classification (TC), i.e. the collection of procedures for inferring applications and/or services generating network traffic, represents the workhorse for service management and the enabler for valuable profiling information. Sadly, the growing trend toward encrypted protocols (e.g. TLS) and the evolving nature of network traffic make TC design solutions based on payload-inspection and machine learning, respectively, unsuitable. Conversely, Deep Learning (DL) is currently foreseen as a viable means to design traffic classifiers based on automatically-extracted features, reflecting the complex patterns distilled from the multifaceted (encrypted) traffic nature, implicitly carrying information in "multimodal" fashion. To this end, in this paper a novel multimodal DL approach for multitask TC is explored. The latter is able to capitalize traffic data heterogeneity (by learning both intra- and inter-modality dependencies), overcome performance limitations of existing (myopic) single-modality DL-based TC proposals, and solve different traffic categorization problems associated with different providers’ desiderata. Based on a real dataset of encrypted traffic, we report performance gains of our proposal over (a) state-of-art multitask DL architectures and (b) multitask extensions of single-task DL baselines (both based on single-modality philosophy).

In this article, we address the problem of distributed detection of a noncooperative (unknown emitted signal) target with a wireless sensor network. When the target is present, sensors observe a (unknown) deterministic signal with attenuation depending on the unknown distance between the sensor and the target, multiplicative fading, and additive Gaussian noise. To model energy-constrained operations within Internet of Things, one-bit sensor measurement quantization is employed and two strategies for quantization are investigated. The fusion center receives sensor bits via noisy binary symmetric channels and provides a more accurate global inference. Such a model leads to a test with nuisances (i.e., the target positionxT) observable only underH1hypothesis. Davies' framework is exploited herein to design the generalized forms of Rao and locally optimum detection (LOD) tests. For our generalized Rao and LOD approaches, a heuristic approach for threshold optimization is also proposed. The simulation results confirm the promising performance of our proposed approaches.

Modeling network traffic is an endeavor actively carried on since early digital communications, supporting a number of practical applications, that range from network planning and provisioning to security. Accordingly, many theoretical and empirical approaches have been proposed in this long-standing research, most notably, Machine Learning (ML) ones. Indeed, recent interest from network equipment vendors is sparking around the evaluation of solid information-theoretical modeling approaches complementary to ML ones, especially applied to new network traffic profiles stemming from the massive diffusion of mobile apps. To cater to these needs, we analyze mobile-app traffic available in the public dataset MIRAGE-2019 adopting two related modeling approaches based on the well-known methodological toolset of Markov models (namely, Markov Chains and Hidden Markov Models). We propose a novel heuristic to reconstruct application-layer messages in the common case of encrypted traffic. We discuss and experimentally evaluate the suitability of the provided modeling approaches for different tasks: characterization of network traffic (at different granularities, such as application, application category, and application version), and prediction of network traffic at both packet and message level. We also compare the results with several ML approaches, showing performance comparable to a state-of-the-art ML predictor (Random Forest Regressor). Also, with this work we provide a viable and theoretically sound traffic-analysis toolset to help improving ML evaluation (and possibly its design), and a sensible and interpretable baseline.

The increasing diffusion of mobile devices has dramatically changed the network traffic landscape, with Traffic Classification (TC) surging into a fundamental role while facing new and unprecedented challenges. The recent and appealing adoption of Deep Learning (DL) techniques has risen as the solution overcoming the performance of ML techniques based on tedious and time-consuming handcrafted feature design. Still, the black-box nature of DL models prevents its practical and trustful adoption in critical scenarios where the reliability/interpretation of results/policies is of key importance. To cope with these limitations, eXplainable Artificial Intelligence (XAI) techniques have recently acquired the interest of the community. Accordingly, in this work we investigate trustworthiness and interpretability via XAI-based techniques to understand, interpret and improve the behavior of state-of-the-art multimodal DL traffic classifiers. The proposed methodology, as opposed to common results seen in XAI, attempts to provide global interpretation, rather than sample-based ones. Results, based on an open dataset, allow to complement the above findings with domain knowledge.

Internet of Things (IoT) fosters unprecedented network heterogeneity and dynamicity, thus increasing the variety and the amount of related vulnerabilities. Hence, traditional security approaches fall short, also in terms of resulting scalability and privacy. In this paper we propose H2ID, a two-stage hierarchical Network Intrusion Detection approach. H2ID performs (i) anomaly detection via a novel lightweight solution based on a MultiModal Deep AutoEncoder (M2-DAE), and (ii) attack classification, using soft-output classifiers. We validate our proposal using the recently-released Bot-IoT dataset, inferring among four relevant categories of attack (DDoS, DoS, Scan, and Theft) and unknown attacks. Results show gains of the proposed M2-DAE in the case of simple anomaly detection (up to -40% false-positive rate when compared with several baselines at same true positive rate) and for H2ID as a whole when compared to the best-performing misuse detector approach (up to ≈ +5% F1 score). Besides the performance advantages, our system is suitable for distributed and privacy-preserving deployments while limiting re-training necessities, in line with the high efficiency as well as the flexibility required in IoT scenarios.

Traffic Classification (TC) is an important tool for several tasks, applied in different fields (security, management, traffic engineering, R&D). This process is impaired or prevented by privacy-preserving protocols and tools, that encrypt the communication content, and (in case of anonymity tools) additionally hide the source, the destination, and the nature of the communication. In this paper, leveraging a public dataset released in 2017, we provide classification results with the aim of investigating to which degree the specific anonymity tool (and the traffic it hides) can be identified, when compared to the traffic of other considered anonymity tools, using five machine learning classifiers. Initially, flow-based TC is considered, and the effects of feature importance and temporal-related features to the network are investigated. Additionally, the role of finer-grained features, such as the (joint) histogram of packet lengths (and inter-arrival times), is determined. Successively, “early” TC of anonymous networks is analyzed. Results show that the considered anonymity networks (Tor, I2P, JonDonym) can be easily distinguished (with an accuracy of 99.87% and 99.80%, in case of flow-based and early-TC, respectively), telling even the specific application generating the traffic (with an accuracy of 73.99% and 66.76%, in case of flow-based and early-TC, respectively).

In this paper, in order to tackle major challenges of spectrum exploration & allocation in Cognitive Radio (CR) networks, we apply the general framework of Decision Fusion (DF) to wideband collaborative spectrum sensing based on Orthogonal Frequency Division Multiplexing (OFDM) reporting. At the transmitter side, we employ OFDM without Cyclic Prefix (CP) in order to improve overall bandwidth efficiency of the reporting phase in networks with high user density. On the other hand, at the receiver side (of the reporting channel) we device the Time-Reversal Widely Linear (TR-WL), Time-Reversal Maximal Ratio Combining (TR-MRC) and modified TR-MRC (TR-mMRC) rules for DF. The DF Center (DFC) is assumed to be equipped with a large antenna array, serving a number of unauthorized users competing for the spectrum, thereby resulting in a “virtual” massive Multiple-Input Multiple-Output (MIMO) channel. The effectiveness of the proposed TR-based rules in combating (a) inter-symbol and (b) inter-carrier interference over conventional (non-TR) counterparts is then examined, as a function of the Signal-to-Interference-plus-Noise Ratio (SINR). Closed-form performance, in terms of system false-alarm and detection probabilities, is derived for the formulated fusion rules. Finally, the impact of large-scale channel effects on the proposed fusion rules is also investigated, via Monte-Carlo simulations.

With the growing adoption of cloud infrastructures to deliver a variety of IT services, monitoring cloud network performance has become crucial. However, cloud providers only disclose qualitative info about network performance, at most. This hinders efficient cloud adoption, resulting in no performance guarantees, uncertainties about the behavior of hosted services, and sub-optimal deployment choices. In this work, we focus on cloud-to-user latency, i.e. the latency of network paths interconnecting datacenters to worldwide-spread cloud users accessing their services. In detail, we performed a 14-day measurement campaign from 25 vantage points deployed via Planetlab infrastructure (emulating spatially- spread users) and considering services running in distinct locations on the infrastructures of the two most popular public-cloud providers, namely Amazon Web Services and Microsoft Azure. Our experimentation allows to provide an in-depth performance characterization (based on multiple probing methods and fine-grained sampling rate) of such networks as perceived by users spread worldwide. Results show the presence of both spatial and temporal latency trends. Finally, by evaluating the advantages of multi- cloud deployments, our results also provide useful guidelines to cloud customers.

Ambient backscatter (AmBC) communications cap-italize on ambient radio-frequency (RF) signals to enable communications among ultra-low-power devices, thus representing a promising cost-effective solution for wireless sensor networks in the Internet of Things. In this paper, we study the scenario where single-antenna AmBC sensors are employed to perform decision fusion over multiple-access fading channels. Specifically, AmBC sensors detect the presence/absence of a phenomenon of interest and transmit their decisions to a multiple-antenna fusion center reader (FCR), by reflecting part of an incident RF ambient signal. In this scenario, we derive fusion rules at the FCR by considering both the cases of instantaneous and statistical channel state information, as well as their corresponding low-complexity alternatives. Numerical simulation results are provided to compare the proposed fusion rules and highlight the relevant trends.

The massive adoption of hand-held devices has led to the explosion of mobile traffic volumes traversing home and enterprise networks, as well as the Internet. Traffic classification (TC), i.e., the set of procedures for inferring (mobile) applications generating such traffic, has become nowadays the enabler for highly valuable profiling information (with certain privacy downsides), other than being the workhorse for service differentiation/blocking. Nonetheless, the design of accurate classifiers is exacerbated by the raising adoption of encrypted protocols (such as TLS), hindering the suitability of (effective) deep packet inspection approaches. Also, the fast-expanding set of apps and the moving-target nature of mobile traffic makes design solutions with usual machine learning, based on manually and expert-originated features, outdated and unable to keep the pace. For these reasons deep learning (DL) is here proposed, for the first time, as a viable strategy to design practical mobile traffic classifiers based on automatically extracted features, able to cope with encrypted traffic, and reflecting their complex traffic patterns. To this end, different state-of-the-art DL techniques from (standard) TC are here reproduced, dissected (highlighting critical choices), and set into a systematic framework for comparison, including also a performance evaluation workbench. The latter outcome, although declined in the mobile context, has the applicability appeal to the wider umbrella of encrypted TC tasks. Finally, the performance of these DL classifiers is critically investigated based on an exhaustive experimental validation (based on three mobile datasets of real human users' activity), highlighting the related pitfalls, design guidelines, and challenges.

In this letter, we propose novel (semi)blind hard decision fusion rules that use the mean of the secondary user characteristics instead of their actual values. We show that these rules with slight (or no) additional system knowledge achieve better receiver operating characteristics than existing (semi)blind alternatives. These rules also have a low-complexity analytical solution under Neyman-Pearson criterion in some relevant cases. Numerical results are reported in a channel-aware scenario to demonstrate their appeal and to confirm the theoretical findings.

The wide spreading and growing usage of smartphones are deeply changing the kind of traffic that traverses home and enterprise networks and the Internet. Tools that base their functions on the knowledge of the application generating the traffic (performance enhancement proxies, network monitors, policy enforcement devices) imply traffic classification, and are thus limited or impaired when dealing with the daily expanding set of mobile apps. Besides the moving-target nature of mobile apps traffic, the increasing adoption of encrypted protocols (TLS) makes classification even more challenging, defeating established approaches (DPI, statistical classifiers). In this paper we aim to improve the classification performance of mobile apps classifiers adopting a multi-classification approach, intelligently-combining decisions from state-of-art classifiers proposed for mobile and encrypted traffic classification. Based on a dataset of users' activity collected by a mobile solutions provider, our results demonstrate that classification performance can be improved according to all considered metrics, up to +8.1% F-measure score with respect to the best base classifier. Further room for improvements is also evidenced by the ideal combiner performance (oracle).

This letter is focused on the design and analysis of computational wideband time-reversal imaging algorithms, designed to be adaptive with respect to the noise levels pertaining to the frequencies being employed for scene probing. These algorithms are based on the concept of cell-by-cell processing and are obtained as theoretically-founded decision statistics for testing the hypothesis of single-scatterer presence (absence) at a specific location. These statistics are also validated in comparison with the maximal invariant statistic for the proposed problem.

We derive the asymptotic distribution of the null spectrum of the well-known Multiple Signal Classification (MUSIC) in its computational Time-Reversal (TR) form. The result pertains to a single-frequency noncolocated multistatic scenario and several TR-MUSIC variants are investigated here. The analysis builds upon the first-order perturbation of the singular value decomposition and allows a simple characterization of null-spectrum moments (up to the second order). This enables a comparison in terms of spectrums stability. Finally, a numerical analysis is provided to confirm the theoretical findings.

We tackle distributed detection of a noncooperative target with a wireless sensor network. When the target is present, sensors observe an (unknown) deterministic signal with attenuation depending on the distance between the sensor and the (unknown) target positions, embedded in symmetric and unimodal noise. The fusion center receives quantized sensor observations through error-prone binary symmetric channels and is in charge of performing a more-accurate global decision. The resulting problem is a two-sided parameter testing with nuisance parameters (i.e., the target position) present only under the alternative hypothesis. After introducing the generalized likelihood ratio test for the problem, we develop a novel fusion rule corresponding to a generalized Rao test, based on Davies' framework, to reduce the computational complexity. Also, a rationale for threshold-optimization is proposed and confirmed by simulations. Finally, the aforementioned rules are compared in terms of performance and computational complexity.

This letter deals with the problem of adaptive signal detection in partially homogeneous and persymmetric Gaussian disturbance within the framework of invariance theory. First, a suitable group of transformations leaving the problem invariant is introduced and the maximal invariant statistic (MIS) is derived. Then, it is shown that the (two-step) generalized-likelihood ratio test, Rao, and Wald tests can be all expressed in terms of the MIS, thus proving that they all ensure a constant false-alarm rate.

In this paper, we consider a wireless sensor network (WSN) with sensors simultaneously reporting their decision to a fusion center (FC) equipped with multiple antennas. A Gaussian mixture channel model is used to obtain a general fading characterization of the channels ensemble between the sensors and the FC. Energy detection is studied as an appealing low-complexity sub-optimal alternative to the (computationally expensive) optimal test based on log-likelihood ratio. Closed-form theoretical performance is obtained for the energy test and furthermore asymptotic analysis for both tests is derived in order to provide a detailed characterization of large-system scenarios. Finally, the simulation results are provided to confirm the theoretical results and compare performance trends of the two tests.

This letter is focused on the classic problem of testing samples drawn from independent Bernoulli probability mass functions, when the success probability under the alternative hypothesis is not known. The goal is to provide a systematic taxonomy of the viable detectors (designed according to theoretically-founded criteria) which can be used for the specific instance of the problem. Both One-Sided (OS) and Two-Sided (TS) tests are considered, with reference to: (i) identical success probability (a homogeneous scenario) or (ii) different success probabilities (a non-homogeneous scenario) for the observed samples. As a result of the study, a complete summary (in tabular form) of the relevant statistics for the problem is provided, along with a discussion on the existence of the Uniformly Most Powerful (UMP) test. Finally, when the Likelihood Ratio Test (LRT) is not UMP, existence of the UMP detector after reduction by invariance is investigated.

In this letter we study the problem of channel-aware decision fusion when the sensor detection probability is not known at the decision fusion center. Several alternatives proposed in the literature are compared and new fusion rules (namely “ideal sensors” and “locally-optimum detection”) are proposed, showing attractive performance and linear complexity. Simulations are provided to compare the performance of the aforementioned rules.

Iterative detection-and-decoding for multi-input multi-output (MIMO) communication systems require a soft-input soft-output (SISO) detection algorithm, which, in the optimal formulation, is well known to be exponentially complex in the number of transmitting antennas. This paper presents a novel SISO detector for MIMO systems, named SISO king decoder. It is a tree-search branch-and-bound algorithm, which exploits the properties of the channel matrix and the a-priori information on the transmitted bits to reduce the overall computational complexity. The proposed algorithm is compared with the SISO single tree-search sphere decoder [1]. Simulation results are provided to show the complexity reduction without relevant performance loss in terms of bit-error rate.

In this paper the choice of the Bernoulli distribution as biased distribution for importance sampling (IS) Monte-Carlo (MC) simulation of linear block codes over binary symmetric channels (BSCs) is studied. Based on the analytical derivation of the optimal IS Bernoulli distribution, with explicit calculation of the variance of the corresponding IS estimator, two novel algorithms for fast-simulation of linear block codes are proposed. For sufficiently high signal-to-noise ratios (SNRs) one of the proposed algorithm is SNR-invariant, i.e. the IS estimator does not depend on the cross-over probability of the channel. Also, the proposed algorithms are shown to be suitable for the estimation of the error-correcting capability of the code and the decoder. Finally, the effectiveness of the algorithms is confirmed through simulation results in comparison to standard Monte Carlo method.

In this letter, we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.

In this paper we present a theoretical performance analysis of the maximum ratio combining (MRC) rule for channel-aware decision fusion over multiple-input multiple-output (MIMO) channels for (conditionally) dependent and independent local decisions. The system probabilities of false alarm and detection conditioned on the channel realization are derived in closed form and an approximated threshold choice is given. Furthermore, the channel-averaged (CA) performances are evaluated in terms of the CA system probabilities of false alarm and detection and the area under the receiver operating characteristic (ROC) through the closed form of the conditional moment generating function (MGF) of the MRC statistic, along with Gauss-Chebyshev (GC) quadrature rules. Furthermore, we derive the deflection coefficients in closed form, which are used for sensor threshold design. Finally, all the results are confirmed through Monte Carlo simulations.

This paper deals with spectrum sensing for cognitive radio scenarios where the decision fusion center (DFC) exploits array processing. More specifically, we explore the impact of user cooperation and orthogonal transmissions among secondary users (SUs) on the reporting channel. To this aim four protocols are considered: (i) non-orthogonal and non-cooperative; (ii) orthogonal and non-cooperative; (iii) non-orthogonal and cooperative; (iv) orthogonal and cooperative. The DFC employs maximum ratio combining (MRC) rule and performance are evaluated in terms of complementary receiver operating characteristic (CROC). Analytical results, coupled with Monte Carlo simulations, are presented.

We study channel-aware binary-decision fusion over a shared Rayleigh flat-fading channel with multiple antennas at the Decision Fusion Center (DFC). We present the optimal rule and derive sub-optimal fusion rules, as alternatives with improved numerical stability, reduced complexity and lower system knowledge required. The set of rules is derived following both "Decode-and-Fuse" and "Decode-then-Fuse" approaches. Simulation results for performances are presented both under Neyman-Pearson and Bayesian frameworks. The effect of multiple antennas at the DFC for the presented rules is analyzed, showing corresponding benefits and limitations. Also, the effect on performances as a function of the number of sensors is studied under a total power constraint.