Publications Scientifiques

Understanding how infectious diseases spread requires models that integrate both human contact structure and environmental factors. We propose SEIRW-VN, a generalized epidemic framework that combines network heterogeneity, indirect transmission via environmental reservoirs, and vaccination. Using COVID-19 data from several European countries, the model outperforms classical homogeneous approaches, capturing more realistic epidemic peaks and timing. Our simulations reveal that highly connected individuals disproportionately sustain outbreaks, while environmental transmission can account for up to one quarter of infections, prolonging epidemic duration. Intervention analysis shows that non-pharmaceutical measures delay peaks and vaccination reduces long-term incidence, but only their combination yields strong synergistic effects, lowering both peak size and overall burden. These findings highlight the importance of integrating contact structure, environmental persistence, and vaccination into epidemic models to design robust and effective public health strategies.

The Zika virus exhibits complex transmission dynamics involving vector-borne, sexual, and vertical pathways, which complicates the design of effective control strategies. In this work, we develop an integrated SICR–SI mathematical model coupling human and mosquito populations, incorporating Wolbachia-based biocontrol, sexual prevention, and vector control as intervention strategies. The model parameters are calibrated using epidemiological time-series data from seven Pacific island archipelagos, and the basic reproduction number R0 is analytically derived. Our results show that Wolbachia substantially reduces the vector component of transmission but cannot eliminate Zika in the presence of sexual transmission, which maintains a residual epidemic risk. We demonstrate that combining Wolbachia releases with sexual prevention yields a synergistic reduction in incidence greater than the sum of their individual effects. Using optimal control theory, we identify time-dependent intervention strategies that minimize both infection burden and implementation costs. A global sensitivity analysis (Sobol indices) highlights the dominant influence of mosquito-related parameters and Wolbachia coverage on epidemic outcomes. Overall, this study underscores the necessity of multitarget and coordinated intervention strategies to effectively reduce Zika transmission and its severe neurological complications.

Many real-world networks exhibit multi-scale organization, yet identifying nested structural patterns remains challenging. We introduce a recursive framework to uncover the hierarchical organization of networks by extracting and evaluating densely connected subgraphs, called local components. At each level, components are assessed based on modularity, stability, and minimum size before being further decomposed. We apply this method to 24 real-world networks from social, biological, and technological domains. The results show diverse hierarchical depths (from 1 to 5), with social networks typically exhibiting deeper and more complex structures. Our analysis highlights the relevance of mesoscopic decomposition for characterizing the internal architecture of networks and enables cross-domain comparisons of structural depth.

The Intelligent Reflecting Surface (IRS) is an enabling technology for beyond 5G and 6G. IRSs are passive reflective surfaces that can modify signals in a controlled manner. They are utilized to enhance coverage and spectrum with high energy efficiency and, therefore, are referred to as passive relays. This energy efficiency makes them a promising technology. The reflective nature of these surfaces allows for a line-of-sight path through the IRS between a transmitter and receiver when the direct path is obstructed with low-energy consumption. However, this requires careful positioning. Proper IRS positioning is crucial for optimizing network performance. Most studies consider a fixed placement of the IRS, but the IRS can also be mobile to better adapt to the dynamics of certain networks. In this paper, we propose to compare the fixed and mobile placements of several IRSs. The problem is modeled using an IRS activation strategy for each period, employing different comparison criteria.

Assessing public health intervention strategies is crucial for effectively managing dengue. While numerous studies have explored the impact of dengue interventions on its transmission dynamics, limited research has focused on the combined effects of implementing multiple therapeutic interventions for disease control. This study presents an epidemic model for understanding dengue transmission dynamics, incorporating two critical therapeutic measures: vaccination and treatment of infected individuals. The model is characterized by ordinary differential equations involving seven-state variables. The investigation encompasses both disease-free and endemic equilibria of the model. The findings reveal that the disease-free equilibrium (only) is globally stable when the basic reproduction number is below one. Interestingly, when the vaccine’s effectiveness is low, treatment emerges as a more successful approach in reducing dengue cases than vaccination. In contrast, a highly effective vaccine alone significantly curtails dengue occurrences. Moreover, the study introduces an optimal control problem, featuring an objective function integrating two control mechanisms: vaccination and treatment. The analysis strongly suggests that implementing two control strategies outweighs the efficacy of a single approach in effectively mitigating the spread of the disease.

The correlation of security alerts is crucial for effective threat detection and mitigation in modern cybersecurity landscapes. With the exponential growth in the volume of generated alerts, there arises an imperative to continually develop new, efficient methods for alert correlation. The use of Large Language Models (LLMs) presents new avenues for enhancing real-time threat detection in the field of cybersecurity. In this paper, we introduce MCM-Llama, a fine-tuned language model aimed at improving security event correlation for real-time threat detection. Building upon our prior research, we augment the current framework by integrating MCM-Llama and introducing a novel architecture to practically implement our proposition. In doing so, we replace the correlation module previously based on Named Entity Recognition (NER) and semantic similarity with a fine-tuned Large Language Model (LLM). This transition allows us to capitalize on the advanced capabilities of LLM in comprehending and correlating security events dynamically. Through this enhancement, we aim to significantly improve the accuracy and efficiency of our threat detection system while leveraging the full potential of LLM technology in cybersecurity. Our experimental results validate the efficacy of the proposed approach by significantly improving the correlation rate, thereby reinforcing the pivotal role of Large Language Models (LLMs) in advancing security event correlation and threat detection methodologies.

Modern Security Operations Centers (SOCs) struggle with an overwhelming influx of security alerts from diverse sources, leading to alert fatigue and delayed incident response times. Conventional Security Information and Event Management (SIEM) systems typically process alerts in isolation, missing complex attack patterns that unfold across multiple detection points. This research introduces a comprehensive machine learning framework for intelligent alert correlation using three classification approaches: binary correlation detection, correlation type classification, and incident classification. We evaluated eight machine learning algorithms—Logistic Regression, Random Forest, XGBoost, LightGBM, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), CatBoost, and Support Vector Machines (SVM)—using 50,000 realistic security alerts generated from 2.30 million network flows in the CIC-IDS-2017 dataset. Our multi-classification framework incorporates temporal relationships, network topology, attack progression patterns, and severity indicators to enhance correlation precision. The experimental results reveal remarkable performance across all classification tasks. CatBoost achieved the highest overall performance with 99.27% average accuracy across all tasks, closely followed by Random Forest (99.17%) and LightGBM (99.06%). For multiclass correlation type classification, six out of eight models achieved perfect 100% accuracy, with only XGBoost and SVM showing decreases to 96.30%. Binary correlation detection demonstrated exceptional performance with three models (Random Forest, LightGBM, CatBoost) achieving 99.96% accuracy. The proposed framework demonstrates exceptional capability in reducing alert noise while maintaining optimal detection rates, significantly enhancing SOC operational efficiency and enabling more targeted threat response strategies.

The intelligent reflective surface (IRS) is a new technology that can reduce the number of base stations and improve signal quality. Whereas in the literature, IRS is often considered in one-hop scenarios, we propose to address two-hop IRS in a vehicular context. On the one hand, two-hop IRS should improve connectivity by enabling blind devices in blind areas to be connected to the network, and on the other, connections may be more volatile due to a higher number of intermediate devices. In this work, their impact on network performance is investigated through different performance criteria. Our approach is validated using real vehicular traces in the city of Roma, Italy.

The automation of agriculture is increasingly incorporating advanced technologies like Internet of Things (IoT) and artificial intelligence to optimize productivity. This study focuses on a specific innovation: forecasting post-harvest tomato shelf life using electronic noses and deep learning. The proposed approach provides accurate and continuous monitoring of tomato conditions, addressing the industry’s demand for enhanced efficiency and quality. The model architecture includes four layers: two stacked recurrent layers (either long short-term memory (LSTM) or gated recurrent unit (GRU)), a dropout regularization layer, and a dense output layer. The LSTM based model achieved a mean squared error (MSE) of 0.000586, a mean absolute error (MAE) of 0.01502, and R-squared values of 0.817 (global) and 0.9870 (remaining days). Similarly, the GRU based model demonstrated superior performance with an MSE of 0.000505, an MAE of 0.01392, and R-squared values of 0.832 (global) and 0.9928 (remaining days). By effectively capturing temporal dependencies and minimizing overfitting, these models offer a robust solution for predicting tomato shelf life. This contribution advances agricultural automation, supporting sustainable practices and ensuring the delivery of fresh, high-quality produce to consumers.

The automation of agriculture is increasingly incorporating advanced technologies like Internet of Things (IoT) and artificial intelligence to optimize productivity. This study focuses on a specific innovation: forecasting post-harvest tomato shelf life using electronic noses and deep learning. The proposed approach provides accurate and continuous monitoring of tomato conditions, addressing the industry’s demand for enhanced efficiency and quality. The model architecture includes four layers: two stacked recurrent layers (either long short-term memory (LSTM) or gated recurrent unit (GRU)), a dropout regularization layer, and a dense output layer. The LSTM based model achieved a mean squared error (MSE) of 0.000586, a mean absolute error (MAE) of 0.01502, and R-squared values of 0.817 (global) and 0.9870 (remaining days). Similarly, the GRU based model demonstrated superior performance with an MSE of …

Intelligent Reflecting Surfaces or IRSs are energy-efficient technologies used as “passive” relays to increase coverage. Often fixed, they enable connectivity to users in positions that are difficult for the base station to access, or that are blind. Most studies propose fixed IRS positioning, with the disadvantage of covering only a fixed zone. Therefore, in the vehicular environment, it would be interesting to see the feasibility of placing IRSs following traffic. We, therefore, propose to study the mobile placement of IRSs in a vehicular context first by using an optimizer and then by relying on heuristics. In the first part, we compare fixed and mobile IRS positioning. Then, for the heuristic part, we present different IRS election strategies, which we have compared. Performances are compared between fixed and mobile placement in the first part, and between one and two hops in the second part, all while analyzing the impact of …

Predicting the next phases of an ongoing cyberattack is critical for proactive cybersecurity, enabling defenders to anticipate and mitigate threats effectively. This paper presents a context-aware meta-model framework designed to dynamically select the most suitable prediction model for projecting the next phase of an attack. Depending on real-time situational parameters, the meta-model determines whether to employ a Graph Attention Network (GAT) or a Deep Q-Network (DQN). The GAT model captures complex, graph-based relationships in attack patterns, while the DQN model excels in sequential decision-making for evolving attack scenarios. Experimental evaluations using the CTU-13 dataset demonstrate the effectiveness of this approach: GAT achieves 99.20% accuracy, and DQN achieves 100% in predicting the second phase of an attack based on an alert set representing the first phase. These results …

In this publication, we present a model that simulates the spread of monkeypox virus. The model takes into account the effect of interactions between human and rodent populations, as well as certain realistic factors such as the complex network with a heterogeneous mean field and certain disease control measures such as vaccination and disease therapy. Although vaccines do not offer total protection against the disease, they can reduce the transmission of monkeypox, which could help reduce the spread of the virus in a population, as our results show. So to develop efficient strategies for epidemic control and prevention, we studied global sensitivity analysis to determine the parameters most influential on disease spread. The existence and overall stability of the disease‐free equilibrium are also discussed. The basic reproduction number R0 of the model is calculated. The results show that if R0 < 1, the disease …

In many domains, including social network analysis, biology, and beyond, community structure discovery in networks is a basic topic. To find highly connected node groups—often referred to as communities—in these networks, researchers develop a wide range of techniques. While the field evolves rapidly, many comparative studies are carried out to assess the strengths and weaknesses of different algorithms on various types of networks and scenarios. This study attempts to provide a thorough comparative analysis involving both established techniques like Louvain, Label Propagation, Infomap, Girvan-Newman, and Walktrap, as well as newly proposed methods like PercoMVC, Walkscan, and Paris. This is due to the constantly changing landscape of community detection algorithms and the critical importance of understanding their strengths and weaknesses. We carry out a thorough empirical assessment of these eight community recognition methods on a variety of real-world networks, such as biological, social, and collaborative networks, among others with different topologies. A wide range of internal and external quality criteria, including conductance, density-based measurements, modularity, cut ratio, and the Friedman rank test, are incorporated into our analysis. Louvain consistently performs well on dense networks and is the best at optimizing modularity. Large, sparse networks work best with label propagation, effectively reducing the number of intercommunity links. PercoMVC shows high overall resilience and adaptability across a variety of network configurations. Infomap is good at capturing complex community structures and works well on various network types. Walktrap provides a solid balance between accuracy and efficiency and demonstrated high performance on congested networks. Our study provides insights to guide practitioners in selecting the most suitable community detection technique based on the network characteristics at hand, highlighting the complementary strengths of algorithms like Louvain and Label Propagation on dense and sparse networks respectively, and PercoMVC’s versatility.

Network analysis is a crucial tool for understanding complex systems, offering insights into patterns, optimization, and behavior prediction across various fields. Traditional mesoscopic representations like community and core-periphery structures often face ambiguities. We propose a novel component structure framework that distinguishes between dense local components and sparse global components, offering clear definitions, compatibility with standard network analysis techniques, and the ability to integrate hierarchical relationships for a more nuanced and comprehensive network analysis.

Assessing strategies for managing dengue is crucial. While many studies have examined interventions' impact on transmission dynamics, few focus on combining multiple therapeutic approaches. This study presents an epidemic model incorporating vaccination and treatment for infected individuals. It employs ordinary differential equations with seven-state variables, analyzing disease-free and endemic equilibria. Results show the disease-free equilibrium is globally stable when the basic reproduction number is below one. When vaccine effectiveness is low, treatment proves more effective in reducing dengue cases. However, a highly effective vaccine alone significantly reduces dengue occurrences. Additionally, the study introduces an optimal control problem, combining vaccination and treatment. The analysis suggests implementing both strategies is more effective than a single approach in mitigating disease spread.

This paper introduces a component-aware ranking strategy to identify critical nodes in modular networks. It overcomes the limitations of traditional centrality-based methods that fail to account for mesoscopic complex network structuration. The component structure decomposes networks into local (densely connected areas) and global (bridges between these areas) components allowing for the flexible use of different centrality measures in each component to better identify key nodes. We evaluate the method using the Susceptible-Infected-Recovered (SIR) model on LFR synthetic networks that simulate varying levels of community strength, ranging from tightly knit to highly mixed communities. The experimental results demonstrate that the community-aware strategy significantly improves diffusion containment in networks with strong community structures compared to classical centrality ranking methods. Its performance is less pronounced in moderately modular networks, and in weakly modular networks, its benefits are minimal. Additionally, we explore the impact of combining centrality measures (e.g., Betweenness for global and Degree for local components), finding marginal improvements in moderately mixed networks but no substantial advantages in highly mixed ones. Our findings suggest that the component-aware strategy is particularly suited for networks with pronounced community structures, offering a robust tool for information-spreading applications where targeting influential nodes is crucial.

This paper presents a mathematical model to explore the interplay between vaccination and treatment strategies to control dengue fever, a significant public health concern in tropical and subtropical regions. The study employs an SVITR compartmental model (Susceptible, Vaccination, Infectious, Treatment, Recovered) to analyze dengue transmission dynamics and assess the effectiveness of various intervention strategies. Key findings indicate that while a highly effective vaccine can significantly reduce dengue cases, treatment becomes more critical when the vaccine's efficacy is low. The results show the advantages of combining vaccination and treatment to optimize disease control, providing valuable information for public health policy.

Identifying influential nodes in networks is a crucial task with many applications across various domains. Traditional centrality measures, while insightful, often fail to capture the true influence of nodes, especially in complex networks. Machine learning techniques can potentially incorporate diverse node features, but their effectiveness relies heavily on feature engineering. In this study, we propose a hybrid methodology that synergistically combines well-established centrality measures as topological features and employs a powerful Random Forest classifier. Our approach extracts degree, betweenness, closeness, eigenvector centrality, PageRank, and clustering coefficients for each node, which are then used as input features to the Random Forest model. We evaluate our method on three real-world networks: the Cora dataset, the CA-HepTh dataset, and the Facebook dataset. The results demonstrate the …