Publications Scientifiques

This study leverages game theory to examine attack and defense strategies in the global air transportation network. We focus on zero-sum games where attackers and defenders use centrality-based strategies. The research highlights the vulnerability of networks to different attack strategies and the corresponding defense mechanisms that can mitigate these attacks. Results show that when the attack and defense resources are equal, no single defense strategy proves uniformly effective, but strategies based on betweenness centrality show significant resilience. Analyzing Nash equilibrium in the global air network reveals diverse equilibrium strategies, emphasizing the need for varied defense approaches as attack ranges increase.

Evaluating public health intervention techniques is essential to manage dengue successfully. This study proposes an epidemic model that includes two essential therapeutic interventions: vaccination and treating infected individuals to better understand dengue transmission dynamics. We introduce a seven-state model and investigate the model's endemic and disease-free equilibria. Results show that when vaccination efficacy is low, therapy is a more effective strategy for lowering dengue cases, whereas a highly effective vaccine significantly reduces dengue cases. The study also formulates an optimal control problem, suggesting that combined strategies are more effective in mitigating the disease spread.

We introduce the "component structure," a mesoscopic representation that differentiates between a network's dense and sparse global components. Unlike its alternatives (community structure, core-periphery), the representation offers a straightforward definition, captures a broader spectrum of mesoscopic features, and supports hierarchical analysis, allowing for a multilevel network examination of networks' organizational principles and functional behaviors.

The proliferation of IoT is evident in numerous domains today. Its applications are expanding rapidly, offering significant benefits. Nonetheless, it is plagued by numerous security flaws that can hinder its adoption in critical areas. This paper addresses the development of an intrusion detection system (IDS) for IoT networks. Specifically, we implement an autoencoder, an unsupervised deep learning model frequently employed in anomaly detection. The model is trained using the MQTTSet dataset, which contains both normal MQTT traffic and attack data. Training focused exclusively on legitimate data. Testing was conducted on both benign and malicious data to assess the model's effectiveness. The results indicate detection rates of 99.86% and 98.56% for normal and attack data, respectively.

This study explores the robustness of the world air transportation network by analyzing its component structure. The robustness analysis assesses the impact of targeted attacks on key airports based on three centrality measures: strength, betweenness, and PageRank. Our findings reveal that removing airports with high betweenness centrality is the most effective strategy for disrupting the network, isolating regional components from the global network.

Dengue fever is a major health concern in tropical and subtropical regions, affecting millions annually. This study develops an epidemic model to evaluate the combined effects of vaccination and treatment on dengue transmission. The model uses ordinary differential equations to examine the dynamics of susceptible, vaccinated, infected, treated and recovered individuals in human and mosquito populations. Key findings indicate that a highly effective vaccine can significantly reduce dengue incidence, but optimal control is achieved through a combined approach of vaccination and treatment.

This study explores the strategic dynamics within interconnected systems by integrating game theory with complex networks. It presents a static zero-sum game model to analyze attack and defense strategies in such networks. Investigating three strategies for attackers and defenders—random, degree centrality, and betweenness centrality—the study examines Nash equilibrium under equal resource assumptions. Analyzing the payoff matrix and players’ responses identify the dominant strategy as combining random attacks and betweenness-based defenses.

The topological structure of the world air transportation network has been the subject of much research. However, to better understand the reality of air networks, one can consider the traffic, the number of passengers, or the distance between flights. This paper studies the weighted world air transportation network through the component structure, recently introduced in the network literature, by using the number of flights. The component structure is based on the community or multiple core-periphery structures and splits the network into local and global components. The local components capture the regional flights of these two mesoscopic structures (dense parts). The global components capture the inter-regional flights (links between the dense parts). We perform a comparative analysis of the world air transportation network and its components with their weighted counterparts. Moreover, we explore the strength and the s-core of these networks. Results display fewer local components well delimited and more global components covering the world than the unweighted world air transportation network. Centrality analysis reveals the difference between the top airports with high traffic and the top airports with high degrees. This difference is more pronounced in the global air network and the largest global component. Core analysis shows similitude between the s-core and the k-core for the local and global components, even though the latter includes more airports. For the world air network, the North and Central America-Caribbean airports dominate the s-core, whereas the European airports dominate the k-core.

Robustness analysis for targeted attacks is essential, especially for critical infrastructures. Typically, targeted attacks rank the nodes according to a centrality measure and remove top nodes according to a budget. The goal is to exploit the network features efficiently to dismantle them with a minimal budget. Few works are linked to the network mesoscopic properties in the literature, although it is well-admitted that communities or core-periphery are ubiquitous in real-world networks. We propose a network dismantling method based on a mesoscopic representation called the component structure. It performs classical centrality attacks on the network’s global components rather than on the original network. Global components of a network are isolated networks formed by the interactions between its dense parts that one can extract from a community or multiple core-periphery structures. We investigate the proposed strategy using three real-world networks and popular centrality measures (Degree, Betweenness, and PageRank). Results show that the proposed approach is more effective for Degree and PageRank. In contrast, the Betweenness attack on the original network slightly outperforms the attack on the global components but at the price of higher complexity.

The paper describes the design and testing of an electronic nose, which is a device that uses eight metal oxide semiconductor gas sensors and another sensor for temperature and humidity to collect data on various farm products. The electronic nose was tested on a variety of farm products and was able to collect data with gas sensors, temperature, and humidity. The data collected ranged from 0 to 1,023 integer values for gas sensors, from -40ºC to 80ºC degrees Celsius for temperature, and from 0 to 100% for humidity. The experimental electronic nose is then a valid functional device since it really collects data for food quality using machine learning models.

A deeper assessment of the nutritional and organoleptic potential of the items we consume is the first step toward knowing their capacity. Research is becoming more and more interested in developing quick and accurate instruments to assess touch, vision, hearing, and other senses under this impetus. This paper examines a variety of statistical and machine learning algorithms used for food quality applications with electronic nose technology. The latter was discussed, allowing us to say that different data collection devices and data analysis methods were used for each study that was undertaken. Setting up an experimental e-nose based on commercially accessible sensors would be simpler, quicker, and less expensive in a West African setting than buying a commercial e-nose to forecast local crop condition. There is a scarcity of studies that examine the connection between e-nose technologies and food quality in Africa.

Cyber attack scenario reconstruction plays a crucial role in understanding and mitigating security breaches. In this paper, we propose a novel framework that leverages Natural Language Processing (NLP), specifically Named Entity Recognition (NER), and semantic similarity techniques to reconstruct cyber attack scenarios. By analyzing Intrusion Detection alerts, our offline approach identifies relevant entities, detects relationships between them, and measures semantic similarity to uncover hidden patterns and connections. We demonstrate the effectiveness of our framework through experimental evaluations using a public dataset. The results highlight the potential of NLP-based approaches in cyber attack scenario reconstruction.

Crops like tomatoes are an important source of vitamins and other essential nutrients for a healthy diet. How long a tomato will be useful for excellent health can be predicted by early identification of its state. In this paper, we designed an electronic nose for tomato data collection. The proposed electronic device is setup thanks to eight metal oxide semiconductor gas sensors and another sensor for temperature and humidity. The gas sensors detect the volatile organic compounds emitted by the tomato, which can provide insight into its state of ripeness and overall quality. The temperature and humidity sensor provide additional environmental data that can be used to further refine the predictions. We tested the designed electronic nose on two local varieties of tomatoes found in Benin. Data collected ranged from 0 to 1, 023 integer values for gas sensors, from -40ºC to +80ºC for temperature, and from 0 to 100% for humidity. These data will be helpful to train machine learning models to accurately predict the shelf life and overall quality of tomatoes. The obtained results show that our prototype is effective in predicting the shelf life of tomatoes, providing accurate and reliable data that can be used to optimize their consumption.

The goal of this study is to evaluate the feasibility of using an electronic nose for the quality-based classification of two varieties of tomatoes, Tounvi and Akikon, mostly cultivated in Benin. To achieve this, a device consisting of 8 semiconductor metal oxide gas sensors was used to collect olfactory data on five fruits of each tomato variety for 70 days, until each fruit was unfit for consumption. The collected data was then utilized to train Random Forest, k-NN, SVM and MLP for classification. The obtained results showed that, generally, performances obtained were better on an oversampled dataset. The random forest algorithm exhibited the highest performance, achieving accuracies of 0.92, 0.94, and 0.98 on undersampled, normal, and oversampled data, respectively. Findings indicate that utilizing an electronic nose in combination with a supervised learning model can be a successful approach for classifying different post-harvested tomato varieties based on quality. This method offers a valuable solution that can expedite the classification process and enhance decision-making in the tomato industry.

This study explores the mesoscopic intricacies governing the interplay of regional and interregional traffic flows within the world air transportation network. We adopt a network decomposition method, the component structure, to partition the network into local and global components, aligning with geographical and cultural boundaries. We explore the centrality and the core of these unweighted and weighted networks. The analysis of centrality highlights distinctions between airports with high traffic and those with high degrees. This contrast is particularly pronounced in the global air network and its largest component. Additionally, core analysis reveals similarities between the s-core and the k-core for local and global components, despite the latter encompassing more airports. In the world air network, North and Central America-Caribbean airports dominate the s-core, while European airports take precedence in the k-core. Interestingly, airports distant from their component’s core can also rank among the top high-strength airports. This comparative analysis underscores the component structure representation’s vital role in uncovering regional and interregional dynamics within the world air transportation network, providing valuable insights into its mesoscopic structure and implications for global air travel. Moreover, we explore the strength and the s-core of these networks. Results display fewer local components well delimited and more global components covering the world than the unweighted world air transportation network. Centrality analysis reveals the difference between the top airports with high traffic and the top airports with high degrees. This difference is more pronounced in the global air network and the largest global component. Core analysis shows similitude between the s-core and the k-core for the local and global components, even though the latter includes more airports. For the world air network, the North and Central America-Caribbean airports dominate the s-core, whereas the European airports dominate the k-core. Transport network and its intricate hierarchical organization.

In an era of global interconnectedness, transportation networks serve as vital conduits for international relations, trade, and cultural exchange. Previous research has extensively examined the worldwide air transportation network at three distinct levels: global, regional, and national, with contributions from various researchers shedding light on different facets of the network. Focusing on densely connected regions that extend from other dense areas, this study unveils the hierarchical structure of the global air transport network. Results emphasize the multi-level nature of the network, from significant regions to country-based divisions and ultimately to the intricate hub-and-spoke model. One observes increased hub dominance and negative assortativity values as one delves deeper into the network’s hierarchy. This investigation brings a more comprehensive understanding of the global air transport network and its intricate hierarchical organization.

The topological structure of the world air transportation network is the subject of much research. This paper reports a comparative analysis of the weighted and unweighted air transportation network at the mesoscopic level. We use the component structure to isolate regional from interregional traffic and infrastructure. Recently introduced in the network literature, the component structure splits the network into local and global components. The local components are the dense parts of the network. They capture the regional flights. The global components linking the dense parts capture the inter-regional flights. Results display fewer local components well delimited and more global components covering the world than the unweighted world air transportation network. Beyond their structural implications, these components offer practical advantages. They can be a foundation for optimizing transportation routes and schedules, leading to cost savings and reduced travel times. Stakeholders in transportation, including airlines, shipping companies, urban planners, and policymakers, can leverage this knowledge to make informed decisions and strategic plans that promote economic growth and environmental sustainability.

Security stands out as one of the most significant challenges confronting the Internet of Things. Within the existing literature, various solutions have been proposed, with intrusion detection systems playing a prominent role and offering numerous approaches to address security concerns. This paper introduces a three-level intrusion detection system based on deep learning. At the first level, an autoencoder is deployed to detect intrusion presence. Moving to the second level, another autoencoder is utilized to classify the intrusion as either a known or new attack when an intrusion occurs. In the case of a recognized attack, the third level comes into play. At this stage, a Multi-Layer Perceptron (MLP) serves as a multi-class classifier, enabling precise identification of the ongoing attack type. The entire system is trained and evaluated using the MQTTSet dataset. Comprehensive tests are conducted at each of the three levels. The results obtained demonstrate an accuracy of 99%, 99.60%, and almost 100% respectively at level 1, level 2, and level 3.

The evolution of vehicular networks continues with the advent of LPWAN (Low Power Wide Area Network). Thus, several studies currently concern the integration of LPWANs with ITS (Intelligent Transportation Systems). Among these LPWANs, LoRa with LoRaWAN, with its coverage capabilities, support of mobility, and implementation cost, is in the spotlight. However, the latter presents performance problems in highly mobile environments. Therefore, optimizing mobility in LoRaWAN is imperative for its integration into ITS. This requires performance improvements such as loss containment in very dense environments. In this paper, a resource reservation approach is proposed. It is based on device trajectory prediction and traffic differentiation in a dense multi-operator environment. This mechanism aims at reserving resources before the arrival of the device on the predicted antenna, thus reducing the rejections at …

Crops like tomatoes are an important source of vitamins and other essential nutrients for a healthy diet. How long a tomato will be useful for excellent health can be predicted by early identification of its state. In this paper, we designed an electronic nose for tomato data collection. The proposed electronic device is setup thanks to eight metal oxide semiconductor gas sensors and another sensor for temperature and humidity. The gas sensors detect the volatile organic compounds emitted by the tomato, which can provide insight into its state of ripeness and overall quality. The temperature and humidity sensor provide additional environmental data that can be used to further refine the predictions. We tested the designed electronic nose on two local varieties of tomatoes found in Benin. Data collected ranged from 0 to 1,023 integer values for gas sensors, from-40 C to+ 80 C for temperature, and from 0 to 100% for humidity. These data will be helpful to train machine learning models to accurately predict the shelf life and overall quality of tomatoes. The obtained results show that our prototype is effective in predicting the shelf life of tomatoes, providing accurate and reliable data that can be used to optimize their consumption.