Publications Scientifiques

Mobility is a critical element for understanding human contact networks. In many studies, the researchers use random processes to model human mobility. However, people do not move randomly in their environment. Their interactions do not depend only on spatial constraints but on their temporal, social, economic, and cultural activities. The topological structure of the physical and/or proximity contact networks depends, therefore, entirely on the mobility patterns. This paper performs an extensive comparative analysis of real-world temporal contact networks and synthetic networks based on influential mobility models. Results show that the various topological properties of most of the synthetic datasets depart from those observed in real-world contact networks because the randomness of some mobility parameters tends to move away from human contact properties. However, it appears that data generated using Spatio-Temporal Parametric Stepping (STEPS) mobility model reveals similarities with real temporal contact networks such as heavy-tailed distribution of contact duration, frequency of pairs of contacts, and the bursty phenomenon. These results pave the way for further improvement of mobility models to generate meaningful artificial contact networks.

he main objective of Zika transmission studies is to work out the simplest approach to scale back human mortality and morbidity caused by the disease. Therefore, it is essential to spot the relative importance of the various factors contributing to the transmission and prevalence of the disease. Many mathematical models have been formulated incorporating vector-to-human transmission or human-to-human transmission. However, they do not take into consideration the mixture of both sorts of transmission. It raises the question of the impact of both sorts of transmission on the disease dynamics. We develop a mathematical model of Zika with the vertical transmission in the vector population and human-to-human transmission to answer this question. It includes the immature phase of mosquitoes (eggs), adult mosquitoes (susceptible, exposed, and infectious), and human hosts (susceptible, exposed, infectious, and recovered). Results show that neglecting sexual transmission results in an understatement of the proportion of the infected population. Furthermore, it reduces the speed of disease spreading. On the other hand, vertical transmission in mosquitoes has a negligible effect on the dynamics of disease spread. We perform a sensitivity analysis of the reproductive number R0 to raise to understand the parameters driving the dynamics of the disease. It appears that the most sensitive parameters in decreasing order are as follows: the adult mosquito death rate, the sting rate, the transmission probability of mosquito to human, and, therefore, the transmission probability of human to mosquito. Furthermore, the proportion at the equilibrium of infected humans is extremely sensitive to the transition rate from the immature vector stage to the adult stage, the human-to-human transmission rate, and, therefore, the human recovery rate. These results confirm that control policies targeting the vector population and, therefore, the recovery rate of people are pretty effective solutions. To validate the model and estimate the important parameters of the model and the prediction of the disease, we consider the real cases in Colombia from 2016. In a series of graphic maps, we presented the comparative study to estimate the disease scenarios and to predict the time limit of the epidemic control measure.

The Internet of Things (IoT) has finally made its way into people’s daily lives. Multiple connected objects are present in many markets. Their rapid adoption is due to the fact that IoT applications are developing rapidly and are easily deployed. However, these applications present several security challenges, although they are based on existing network technologies, which are already more or less secure. These challenges can be broadly classified as identification, authentication, jamming, cloning, privacy, etc. To face these challenges and in the perspective of ensuring end-to-end IoT networks security, several encryption mechanisms have been adapted and widely used including public key infrastructure (PKI), AES standard and ECC (Elliptic Curve Cryptography). To correct the weaknesses and vulnerabilities that are still present, other technologies such as DLTs (Distributed Ledger Technology) have been recently adopted by the Internet of Things. This adoption is still in its infancy as there are no standards nor methods yet indicating how to manage the security of the IoT through DLTs. In this article, we review the IoT-DLT paradigm in order to identify the benefits and major challenges of the convergence of these two technologies.

The air transportation network is a critical infrastructure in our connected world. Therefore, understanding its robustness to targeted attacks is essential. Extensive research has investigated how removing a particular class of nodes impacts connectivity, efficiency, and security. However, the impact of its mesoscopic structuration remains largely unexplored. To fill this gap, we investigate how targeted attacks on the global weighted air transport network impact its components in this study. Indeed, the weighted world air transportation network includes five local components covering different regions (North America-Caribbean, Europe-Russia, East and Southeast Asia-Oceania, Africa-Middle East-South Asia, South America) and a global component scattered around the world. We study and compare two prevalent attacks (weighted Closeness and weighted Eigenvector). The results show that as the percentage of airports removed increases, the local components progressively separate from the global air transportation network. One needs to remove a lower fraction of top Weighted Closeness airports to isolate regions compared to the Weighted Eigenvector attack. Furthermore, traveling in areas separated by the Weighted Closeness attack is still quite effective. In contrast, the Weighted Eigenvector attack is more damaging for regional transportation. This study opens new perspectives for better understanding the global air transportation network resilience.

As the cyber threat landscape expands, attacks are becoming stealthier, faster and smarter. Traditional security techniques therefore become ineffective against polymorphic threats and zero-day attacks. Thus, research is increasingly oriented towards AI. Machine Learning (ML) quickly showed its limits due to the amount of data and the high dimensionality imposed by the Big Data era, and the workload on manual feature extraction. IDS based on ML has thus shown poor performance and Deep IDS based on ML has thus shown poor performance and Deep we propose traffic classification by a one-dimensional CNN and we propose traffic classification by a one-dimensional CNN and anomaly detection by a deep/stacked autoencoder (DAE). The evaluation of the proposed models show that the false alarm rate (FAR) and the false negative rate (FNR) are very low. Additionally, the DAE model works well against almost any attack. Finally, both models show high performance.

Attacks in IoT networks are increasingly numerous and frequent. Therefore, knowing how to extract abnormal traffic in order to identify potential attacks seems to be a task of primary importance. Here, we first seek to detect anomalies in smart home IoT network traffic through binary classification. Then we try to identify the attacks contained in the anomalous traffic by using learning models based on multiclass classification. In each of these two cases, we used two different datasets to study and compare four learning models based on Decision-Tree (Adaboost), Random Forest, KNN (K-Nearest Neighbors) and a Neural Network model. The results show that our models based on Decision-Tree and Random Forest are more efficient both in the detection of anomalies and in the identification of attacks. This is also the case both in predictions and in behavior with new data.

Security remains one of the biggest challenges in the IoT field. This is why several machine learning and deep learning techniques are used to set up models capable of monitoring network traffic and detecting security incidents. In this paper, we develop two classifiers for anomaly detection in IoT networks. One of them is binary and the other is multi-class. After the training phase, we test them on different datasets. Firstly, we test them on samples of the same nature as the training data. Secondly, we test models on new data containing unknown attacks. For a good comparison of the two models, we transform the multi-class classifier confusion matrix into a 2 × 2 matrix. Thus, with two confusion matrices having the same shape, we calculate the different metrics and compare the models’ performance. On data with known types of anomalies (attacks), both models give good performance but the multi-class classifier has a slight advantage. classifier. Conversely, on data containing unknown anomalies (attacks), performance drops on both sides. However, this time the binary classifier proves to be more efficient.

Targeted attacks aim to disintegrate a network by removing nodes or links. Classically, in node attacks, one removes nodes in descending order of a given centrality measure. Although most real-world networks exhibit a mesoscopic structure, strategies proposed in the literature do not exploit this ubiquitous property. We introduce an attack strategy based on the network component structure to overcome these drawbacks. The component structure of a network consists of local components and global components. The local components are the dense parts of the network, and the global components are the nodes and links connecting them. We investigate this attack on the world air transportation network using Degree and Betweenness centrality measures. Results show that the degree-based attack on the global component is more effective than the classical attack on the entire network. In contrast, the classical Betweenness attack slightly outperforms the Betweenness attack on the global component. However, the latter is more efficient.

Perturbations of the air transport network have a tremendous impact on many sectors of activity. Therefore, a better understanding of its robustness to targeted attacks is essential. The literature reports numerous investigations at different levels (world, regional, airline) considering various targeted attack strategies. However, few works consider the mesoscopic organization of the network. To fill this gap, we rely on the component structure recently introduced in the network literature. Indeed, the world air transportation network possesses seven local components capturing the regional flights in localized areas. Its global component, distributed worldwide, capture the interregional routes. We investigate the impact of two influential attacks (Degree, Betweenness) on the world air transportation network at the regional and inter-regional levels. Results show that the seven regions are isolated one after the other from the world air transportation network. Additionally, although the Betweenness attack effectively splits the network, its impact on regional routes is less pronounced.

Chaque jour, des millions de passagers et de marchandises transitent par le transport aérien. Cette infrastructure est essentielle dans la vie des gens à des fins économiques, sociales et sanitaires. Une perturbation peut avoir des conséquences importantes dans différents secteurs d'activité. Il est donc primordial d'étudier sa perturbation pour en limiter les dégâts. Le paradigme des réseaux complexes tente d'apporter des solutions dans ce sens. Notre travail concerne les interactions entre le réseau mondial non pondéré du transport aérien et ses composantes régionales. Dans des travaux antérieurs, nous avons introduit une décomposition de réseau appelée structure en composantes d'un réseau [1]. Elle décompose un réseau en ses composantes locales et ses composantes globales. Les composantes locales sont des zones denses localisées du réseau originel. Les liens reliant les composantes locales et leurs nœuds associés forment les composantes globales. Sur la base de cette représentation, le transport aérien mondial comprend plusieurs composantes régionales correspondant à des zones géographiques et culturelles naturelles. Les composantes interrégionales révèlent les principaux aéroports et routes entre ces régions. Sur la base de cette représentation, nous explorons l'impact des attaques ciblées sur le réseau mondial du transport aérien sur ses composantes régionales et interrégionales. Nous considérons les mesures de centralité du Degree et de la Betweenness pour éliminer les nœuds par ordre décroissant[2]. Ces expériences donnent un nouvel aperçu des interactions entre les routes internationales et régionales exposées aux perturbations

Some existing methods for authentication of IoT nodes are based on blockchain technology which has limitations that must be taken into account. In this paper, our contribution consists in proposing a new authentication protocol, named bisAUTH, for IoT objects inspired on certain characteristics of the blockchain technology. Our main objective is to propose a mechanism allowing neighboring IoT nodes of a network to authenticate in a decentralized and secure way. Evaluation of our proposed protocol against several criteria, its resistance to various attacks and its comparison with recent protocols, show that it brings significant improvements compared to the existing ones.

Te robustness of a system indicates its ability to withstand disturbances while maintaining its properties, performance, and efciency. Tere are plenty of studies on the robustness of air transport networks in the literature. However, few works consider its mesoscopic organization. Building on the recently introduced component structure, we explore the impact of targeted attacks on the weighted world air transportation network on its components. Indeed, it contains fve local components covering diferent regions (North America•Caribbean, Europe•Russia, East and Southeast Asia•Oceania, Africa•Middle East•Southern Asia, and South America) and one global component linking these regions. We investigate targeted attacks based on infuential weighted centrality measures (strength, betweenness, and PageRank). Results show that the local components gradually separate from the world air transportation network as the fraction of removed airports grows. Te weighted betweenness attack removes fewer top airports to isolate the regions compared to its alternatives. Furthermore, it is still convenient to travel locally in the separate areas. In contrast, strength and PageRank attacks need to target more airports to split the network. However, they are more disruptive. Indeed, the size of the isolated local components reduces drastically, so it becomes more challenging to travel locally. Looking at the world air transportation network through its component structure reveals a new viewpoint on its resilence. It opens new perspectives to design more efcient attacks.

Air transportation plays an essential role in the global economy. Therefore, there is a great deal of work to understand better the complex network formed by the links between the origins and destinations of flights. Some investigations show that the world air transportation network exhibits a community and a core-periphery structure. Although precious, these representations do not distinguish the inter-regional (global) web of connections from the regional (local) one. Therefore, we propose a new mesoscopic model called the component structure that decomposes the network into local and global components. Local components are the dense areas of the network, and global components are the nodes and links bridging the local components. As a case study, we consider the unweighted and undirected world air transportation network. Experiments show that it contains seven large local components and multiple small ones spatially well-defined. Moreover, it has a main global component covering the world. We perform an extensive comparative analysis of the structure of the components. Results demonstrate the non-homogeneous nature of the world air transportation network. The local components structure highlights regional differences, and the global component organization captures the efficiency of inter-regional travel. Centrality analysis of the components allows distinguishing airports centered on regional destinations from those focused on inter-regional exchanges. Core analysis is more accurate in the components than in the whole network where Europe dominates, blurring the rest of the world. Besides the world air transportation network, this paper demonstrates the potential of the component decomposition for modeling and analyzing the mesoscale structure of networks.

Applying deep learning to IoT data classification would yield deeper and more useful insights. IoT field is very wide and has several applications. In this paper we focus on smart home, especially human activity recognition within a house equipped with ambient sensors. Firstly, we describe ARAS Human Activity Dataset that are used in models training and testing. Secondly, we apply three deep learning models to it in order to classify the activities carried out by residents within the house. The deep learning models that we use in our experiments are : a Multilayer Perceptron (MLP), a Recurrent Neural Network(RNN) and Long short-term memory(LSTM). The results show best performances with MLP followed by RNN. In addition, it should be noted that there is a strong correlation between the frequency of activities and their recognition rate.

Today, thanks to artificial neural networks, the field of computer vision is undergoing an unprecedented revolution. The rise of deep learning and the advent of big data have both facilitated the construction of neural networks, not only deep, but also increasingly efficient. This has resulted in the automation of Internet of Things (IoT) technologies and devices. In addition, there are more and more disasters across the world where it is sometimes difficult to find victims in disaster areas. The contribution of IoT technologies is very important in this field. Thus, we propose in this work an intelligent system for detecting people from images and videos provided by IoT devices. Thus, we initially list some open source datasets used for object detection tasks. Then, we propose four person detection models based on the YOLO architectures. After evaluation, we obtained good performance results in terms of Average Accuracy.

Security is one of the major challenges of the Internet of Things (IoT). In an IoT network, data are processed and exchanged without human intervention. Because of this autonomy, objects must authenticate each other as well as to ensure the integrity of the exchanged data. An efficient authentication scheme allows to protect the network against several attacks. Several IoT authentication schemes have already been proposed but they are mostly ineffective and sometimes have limitations. This work proposes a new mutual authentication scheme for IoT based on digital signatures, the Physical Unclonable Functions (PUF) and the blockchain technology. The Global Assessment and analyses show that our new protocol gives more resistance to different types of attacks, and that it also provides a better performance in terms of computing load although it requires fewer storage resources.

Air transportation networks have been extensively studied in the network science literature. Researchers focus on airlines networks, national, regional, continental and worldwide networks using monoplex or multiplex approaches. Inspired by recent results on communityaware centrality measures [1], in this work, an extensive analysis of the topological properties of the world air transportation network is performed. Based on the community structure uncovered by the Louvain algorithm, the original network is split into local components and global components. The local components are made of the communities by removing the intercommunity links. Removing all the intra-community links and subsequent isolated nodes forms the global components. Results reported in figure 1 shows that the main local components are geographically distributed over seven areas (North America, Europe, East & Southeast Asia, Africa-West and South Asia Oceania, South America, Russia). The main global component is distributed among all these areas (figure 2). A comparative analysis of the macroscopic properties of these various networks reveal some consistencies. Indeed, density of all the components is low. However local components are all denser than the overall network. Transitivity is low and more particularly for the global component. All the networks are disassortative. Except for the Russia component, the hub dominance is relatively small. The study also reveals some dissimilarity. For example, the diameter of the local components ranges from 4 to 9 as compared to a value of 13 for the original network. The highest k-core subnetworks (k= 31 and k …