Résumé

Predicting Gestational Diabetes Mellitus (GDM) is crucial for
pregnant women to enable regular monitoring of their blood sugar levels and
adherence to a healthy diet. Early intervention can significantly lower the risk
of developing this condition. To assess this risk, Machine Learning (ML) and
Deep Learning techniques are employed. However, traditional ML models often
face challenges in accurately predicting GDM risk due to the complex
processing required to optimize their hyperparameters for the best performance.
This study presents a feature selection for GDM prediction using AutoML-XAI
techniques (Automatic Machine Learning – eXplainable Artificial Intelligence
techniques) approach, which aims to automatically predict GDM risk as
accurate as possible while providing meaningful interpretations of the
predictive results used in feature selection. The AutoML models generated
utilize a Kaggle dataset and several combinations of features selected based on
their scores of importance determinated with XAI techniques such as SHAP

(Shapley Additive Explanations) and LIME (Local Interpretable Model-
agnostic Explanations). The proposed approach of autoML and features

selection with XAI techniques leads to the creation of a precise, efficient, and
easily interpretable model which surpasses other machine learning models in
predicting GDM risk without the need for human intervention. The scores of
importance of features are involved in the feature selection process and
multiple AutoML models are generated and assessed, with the optimal AutoML
model being established automatically.

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Intelligent Transportation Systems (ITS) are a promising research area that offers a variety of applications. The objective of these applications is to enhance road safety, to optimize traffic efficiency, and to provide a better driving experience. Yet, the efficiency of ITS applications, such as safety and driver assistance applications, relies essentially on the exchanged data between different entities of the network. Accordingly, trust management models are used to guarantee the quality of the data and to eliminate malicious and selfish nodes to secure vehicular communications. In this paper, we pay a special attention to the requirements of trust management models used in the context of ITS applications. We also dissected the trust model to extract the mechanisms used in the literature to fulfil the identified requirements. Furthermore, we present the most known simulators and evaluation metrics that are used to validate the proposed models. The aim of this study is to provide a global overview of the mechanisms that may be used to fulfil the crucial requirements of trust management models. For this purpose, we employed a systematic mapping study, through which we carefully analysed 60 selected articles. Through our analysis, five main requirements were identified: scalability, accuracy, robustness, privacy preservation, appropriate response time. Different mechanisms and techniques were applied to meet with the identified requirements. Two main findings are reported: (1) The accuracy and robustness requirements are the most considered requirements. On the other hand, the privacy requirement is the least covered by the publications, (2) the majority of the reviewed papers focus on addressing two or three requirements at most. A little number of publications covered all the requirements. Based on the identified research gaps, we highlight some future directions that may be investigated. We provide general recommendations that may serve as a guideline for researchers who want to design trust models that fulfil certain requirements.

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The Vehicle Routing Problem (VRP) is a well-known optimization problem in which we aim traditionally to minimize transportation costs while satisfying customer demands. In fact, most logistics companies use a heterogeneous fleet with varying capacities and costs, presenting a more complex variant known as Rich VRP (RVRP). In this paper, we present a mathematical formulation of the RVRP, considering both hard time windows and dynamically changing requests to be as close as possible to real-life logistics scenarios. To solve this challenging problem, we propose a Hybrid Genetic Algorithm (HGA). The experimental study highlights the out-performance of our proposal when evaluated alongside other algorithms on the same benchmark problems. Additionally, we conduct a sensitivity analysis to illustrate how resilient the algorithm is when problem parameters are altered.

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The continuous development of Internet infrastructures and the evolution of digital electronics, particularly Nano-computers, are making the Internet of Things (IoT) emergent. Despite the progress, these IoT objects suffer from a crucial problem which is their limited power supply. IoT objects are often deployed as an ad-hoc network. To minimize their consumption of electrical energy, clustering techniques are used. In this paper, a centralized clustering algorithm with single-hop routing based on a genetic algorithm and Improved k-means is proposed. The proposed approach is compared with the LEACH, K-means and OK-means algorithms. Simulation results show that the proposed algorithm performs well in terms of network lifetime and energy consumption.

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Gestational Diabetes Mellitus (GDM) is a type of diabetes that develops during pregnancy. It is important for pregnant women to monitor their blood sugar levels regularly and follow a healthy diet. However, early intervention can greatly reduce risk of this type of diabetes. Machine Learning and Deep Learning techniques are utilized to predict this risk based on an individual's symptoms, lifestyle, and medical history. By identifying key features such as age, insulin, body mass index, and glucose levels, machine learning models such as Random Forest and XGBoost are used in this research work to classify patients at risk of a gestational diabetes. In addition, we propose an explainable feature selection approach to improve the accuracy of machine learning models for GDM prediction. This method involves iteratively eliminating features that exhibit a negative contribution as determined by the SHAP (Shapley Additive explanations) feature attribution explanations for the model’s predictions

Résumé

Gestational Diabetes Mellitus (GDM) is a type of diabetes that develops during pregnancy. It is important for pregnant women to monitor their blood sugar levels regularly and follow a healthy diet. However, early intervention can greatly reduce risk of this type of diabetes. Machine Learning and Deep Learning techniques are utilized to predict this risk based on an individual's symptoms, lifestyle, and medical history. By identifying key features such as age, insulin, body mass index, and glucose levels, machine learning models such as Random Forest and XGBoost are used in this research work to classify patients at risk of a gestational diabetes. In addition, we propose an explainable feature selection approach to improve the accuracy of machine learning models for GDM prediction. This method involves iteratively eliminating features that exhibit a negative contribution as determined by the SHAP (Shapley Additive explanations) feature attribution explanations for the model’s predictions.

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Elderly fatigue, a critical issue affecting the health and well-being of the aging population worldwide, presents as a substantial decline in physical and mental activity levels. This widespread condition reduces the quality of life and introduces significant hazards, such as increased accidents and cognitive deterioration. Therefore, this study proposed a model to detect fatigue in the elderly with satisfactory accuracy. In our contribution, we use video and image processing through a video in order to detect the elderly’s face recognition in each frame. The model identifies facial landmarks on the detected face and calculates the Eye Aspect Ratio (EAR), Eye Fixation, Eye Gaze Direction, Mouth Aspect Ratio (MAR), and 3D head pose. Among the various methods evaluated in our study, the Extra Trees algorithm outperformed all others machine learning methods, achieving the highest results with a sensitivity of 98.24%, specificity of 98.35%, and an accuracy of 98.29%.

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The integration of social networking concepts into the IoT environment has led to the Social Internet of Things
(SIoT) paradigm which enables connected devices and people to facilitate information sharing, interact, and
enable a variety of attractive applications. However, with this emerging paradigm, people feel cautious and
wary. They worry about violating their privacy and revealing their data. Without trustworthy mechanisms to
guarantee the reliability of user’s communications and interactions, the SIoT will not reach enough popularity
to be considered as a cutting-edge technology. Accordingly, trust management becomes a major challenge
to improve security and provide qualified services. Therefore, we overcome these issues through proposing
an adaptive trust management model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to
estimate the trust level of objects in the Social Internet of Things. We formalized and implemented a new trust
management model built ANFIS, to analyze different trust parameters, estimate the trust level of objects and
distinguish malicious behavior from benign behaviors. Experimentation made on a real data set proves the
performance and the resilience of our trust management model.

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The cold start problem has always been a major challenge for recommender systems. It arises when the system lacks rating records for new users or items. Addressing the challenge of providing personalized recommendations in the cold start scenario is crucial. This research proposes a new hybrid recommender system named CoDFi-DL which combines demographic and enhanced collaborative filtering. The demographic filtering is performed through a deep neural network (DNN) and used to solve the new user cold start problem. The enhanced collaborative filtering component of our model focuses on delivering personalized recommendations through a neighborhood-based method. The major contribution in this research is the DNN-based demographic filtering which overcomes the new user cold start problem and enhances the collaborative filtering process. Our system significantly improves the relevancy of the recommendation task and thus provides personalized recommended items to cold users. To evaluate the effectiveness of our approach, we conducted experiments on real multi-label datasets, 1M and 100K MovieLens. CoDFi-DL recommender system showed higher performance in comparison with baseline methods, achieving lower RMSE rates of 0.5710 on the 1M MovieLens dataset and 0.6127 on the 100K MovieLens dataset.

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Internet users are overwhelmed by the vast number of services and products to choose from. This data deluge has led to the need for recommender systems. Simultaneously, the explosion of interactions on social networks is constantly increasing. These interactions produce a large amount of content that incites organizations and individuals to exploit it as a support for decision making. In our research, we propose, Twit-CoFiD, a hybrid recommender system based on tweet sentiment analysis which performs a demographic filtering to use its outputs in an enhanced collaborative filtering enriched with a sentiment analysis component. The demographic filtering, based on a Deep Neural Network (DNN), allows to overcome the cold start problem. The sentiment analysis of Twitter data combined with the enhanced collaborative filtering makes recommendations more relevant and personalized. Experiments were conducted on 1M and 100K Movielens datasets. Our system was compared to other existing methods in terms of predictive accuracy, assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics. It yielded improved results, achieving lower RMSE and MAE rates of 0.4474 and 0.3186 on 100K Movielens dataset and of 0.3609 and 0.3315 on 1M Movielens dataset.

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The rapid expansion of contextual information from smartphones and Internet of Things (IoT) devices paved the way for Context-Aware Recommendation Systems (CARS). This abundance of contextual data heralds a transformative era for traditional recommendation systems. In alignment with this trend, we propose a novel model which provides personalized recommendations based on context. Our approach uses K-means algorithm to cluster users based on contextual features. Then, the model performs collaborative filtering based on matrix factorization with enhanced contextual biases to provide relevant recommendations. We demonstrated the performance of our method through experiments conducted on the movie recommender dataset LDOS-CoMoDa. The experimental results showed the effective performance of our proposal compared to reference methods, achieving an RMSE of 0.7416 and an MAE of 0.6183.

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Electrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algorithm (DTGA) algorithm, a type of genetic algorithm that is used for optimization problems and discusses its use in the context of feature selection. This research reveals the success of DTGA in selecting relevant ECG features that ultimately enhance accuracy and efficiency in the diagnosis of CVD. This work also proves the benefits of employing DTGA in clinical practice, including a reduction in the amount of time spent diagnosing patients and an increase in the precision with which individuals who are at risk of CVD can be identified.

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Protecting patient data has become a top priority for healthcare providers in the digital age. ECG steganography is a technique for concealing electrocardiogram (ECG) signals during Internet transmission along with other medical data. This strategy aims to recover all embedded patient data while minimizing degradation of the cover signal caused by embedding. Quantization techniques make it possible to include patient information in the ECG signal, and it has been discovered that multiple scaling factors (MSFs) provide a superior trade-off than uniform single scaling factors. In this paper, we present a novel contribution to the field: a discrete wavelet transforms and singular value decomposition-based dynamic Thresholding GA (DTGA)-based ECG steganography scheme. Using the MITIH database, we demonstrate the efficacy of this method, and our findings corroborate that DTGA significantly improves data security.

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In recent years, the negative impacts of neglecting the environment, particularly global warming caused by greenhouse gases, have gained attention. Many countries and organizations are taking steps to reduce their greenhouse gas emissions and promote sustainable practices. In this paper, we aim to address the gap in the classical Vehicle Routing Problem (VRP) by taking into consideration the environmental effects of vehicles. To find a balance between cost-efficiency and environmental impact, we propose a Hybrid Genetic Algorithm (HGA) to address the Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) and a heterogeneous fleet, taking into account new orders that arrive dynamically during the routing process. This approach takes into consideration the environmental effects of the solutions by optimizing the number and type/size of vehicles used to fulfill both static and dynamic orders. The goal is to provide a solution that is both cost-effective and environmentally friendly, addressing the issue of over-exploitation of energy and atmospheric pollution that threaten our ecological environment. Computational results prove that the hybridization of a genetic algorithm with a greedy algorithm can find high-quality solutions in a reasonable run time.

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In the recent year, Internet of Things (IoT) has been adopted in several real-world applications such as smart transportation, smart city, retail, agriculture, smart factory, etc. to make human life more reliable. The integration of social networking concepts into the IoT led to the rise of a new paradigm: the Social Internet of Things (SIoT). In the SIoT environment, the objects are capable of establishing in an autonomous way many social relationships anywhere and anytime with other trusted objects. However, in such environment, objects may provide dishonest recommendations due to malicious reasons such as bad mouthing, ballot stuffing, random opinion, etc. In order to cater these challenges, we propose a new fuzzy logic-based model to filter dishonest recommendations and estimate their trust level based on (1) their values and sending time and the place coordinates and (2) the social relationship parameters of the recommenders. Results prove that our proposed approach is able to detect 100% of the fake Sybil attack and achieves 100% of Recognition Proportion, Sensitivity, Specificity, Accuracy and F1 score and gets 0% of False Negative and False Positive Proportions in presence of up to 90% dishonest recommendations.

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Information security mechanisms are crucial for Vehicular Ad-hoc NETworks (VANET) applications in order to preserve their robustness. The efficiency of these applications relies on the reliability of the used information, especially the vehicle-related information such as location and speed. Trust management models are crucial to evaluate the quality of the used information. Accordingly, we focus in this paper on the assessment of speed information to detect the malicious vehicles using a fuzzy-based model (SP-TRUST). The proposed model relies on two fuzzy inference systems. The first one evaluates the speed trust based on traffic rules (inter-vehicle distance) while considering the road topology (angle deviation) and the traffic state (density). The correlation between these parameters and the speed value is assessed. The second inference system assesses the speed trust based on the behavior of neighbor vehicles using the median speed of vehicles. Simulations were carried out to evaluate the robustness and the scalability of SP-TRUST model. The results of the experimental studies proved that the model performs well in detecting different behaviors of malicious vehicles in different scenarios, especially when the percentage of malicious vehicles is lower than 50%.

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The paradigm of Social Internet of Things (SIoT) incorporates the concepts of social networking in the Internet of Things (IoT). The idea of SIoT is to allow objects to autonomously establish social relationships, which may facilitate network navigability and the discovery of information and services. The characteristics of the IoT such as the heterogeneity and the dynamicity of the network and the social relationships between devices lead to several challenges including how to build a reliable network. In this paper, we propose an adaptive trust management model that helps nodes seeking trusted service providers. The trustworthiness of a service provider is assessed on the basis of its past experiences with the requestor and of the recommendations of the requester’s neighbors. In our trust model, the trust parameters evolve dynamically in response to the change of the network context, the type of the demanded service and the nature of the relationships between the different nodes. The experiment results show that our proposed model achieves high accuracy and it is proved to be resilient against common attacks.

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Workflow management is a core component of modern Enterprise Information Systems (EISs) infrastructure that automates the execution of critical business processes. One of the particular interests of the security community is how to ensure the completion of the workflow execution in the presence of authorisation constraints. These constraints present some restrictions on the users or the roles that are authorised to execute the workflow tasks. The goal is to enforce the legal assignments of access privileges to the executors of the workflow tasks. Despite the variety of approaches proposed in this context, an approach dedicated to the inter-organisational workflows is still missing. In this paper, we take a step towards this goal by proposing a multi-agent-based model, named RPMInter-Work (task-Role assignment Planning Model for Inter-organisational Workflow). Our approach aims to perform the planning of the task-role assignments in inter-organisational workflow in presence of authorisation constraints that are related to task-role assignments. In our research work, this planning problem is formulated as a DisCSP (Distributed Constraint Satisfaction Problem). Our proposed contribution is based on the requirements of inter-organisational workflows, in particular, the autonomy of the participating organisations and the respect of their privacy. A prototype of RPMInter-Work is implemented using JADE (Java Agent DEvelopment) platform and some evaluation results of this prototype are exposed in this paper.

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Workflow management is a core component of modern Enterprise Information Systems (EISs) infrastructure that automates the execution of critical business processes. One of the particular interests of the security community is how to ensure the completion of the workflow execution in the presence of authorisation constraints. These constraints present some restrictions on the users or the roles that are authorised to execute the workflow tasks. The goal is to enforce the legal assignments of access privileges to the executors of the workflow tasks. Despite the variety of approaches proposed in this context, an approach dedicated to the inter-organisational workflows is still missing. In this paper, we take a step towards this goal by proposing a multi-agent-based model, named RPMInter-Work (task-Role assignment Planning Model for Inter-organisational Workflow). Our approach aims to perform the planning of the task-role assignments in inter-organisational workflow in presence of authorisation constraints that are related to task-role assignments. In our research work, this planning problem is formulated as a DisCSP (Distributed Constraint Satisfaction Problem). Our proposed contribution is based on the requirements of inter-organisational workflows, in particular, the autonomy of the participating organisations and the respect of their privacy. A prototype of RPMInter-Work is implemented using JADE (Java Agent DEvelopment) platform and some evaluation results of this prototype are exposed in this paper.

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Workflow management is a core component of modern Enterprise Information Systems (EISs) infrastructure that automates the execution of critical business processes. One of the particular interests of the security community is how to ensure the completion of the workflow execution in the presence of authorisation constraints. These constraints present some restrictions on the users or the roles that are authorised to execute the workflow tasks. The goal is to enforce the legal assignments of access privileges to the executors of the workflow tasks. Despite the variety of approaches proposed in this context, an approach dedicated to the inter-organisational workflows is still missing. In this paper, we take a step towards this goal by proposing a multi-agent-based model, named RPMInter-Work (task-Role assignment Planning Model for Inter-organisational Workflow). Our approach aims to perform the planning of the task-role assignments in inter-organisational workflow in presence of authorisation constraints that are related to task-role assignments. In our research work, this planning problem is formulated as a DisCSP (Distributed Constraint Satisfaction Problem). Our proposed contribution is based on the requirements of inter-organisational workflows, in particular, the autonomy of the participating organisations and the respect of their privacy. A prototype of RPMInter-Work is implemented using JADE (Java Agent DEvelopment) platform and some evaluation results of this prototype are exposed in this paper.

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Workflow management is a core component of modern Enterprise Information Systems (EISs) infrastructure that automates the execution of critical business processes. One of the particular interests of the security community is how to ensure the completion of the workflow execution in the presence of authorisation constraints. These constraints present some restrictions on the users or the roles that are authorised to execute the workflow tasks. The goal is to enforce the legal assignments of access privileges to the executors of the workflow tasks. Despite the variety of approaches proposed in this context, an approach dedicated to the inter-organisational workflows is still missing. In this paper, we take a step towards this goal by proposing a multi-agent-based model, named RPMInter-Work (task-Role assignment Planning Model for Inter-organisational Workflow). Our approach aims to perform the planning of the task-role assignments in inter-organisational workflow in presence of authorisation constraints that are related to task-role assignments. In our research work, this planning problem is formulated as a DisCSP (Distributed Constraint Satisfaction Problem). Our proposed contribution is based on the requirements of inter-organisational workflows, in particular, the autonomy of the participating organisations and the respect of their privacy. A prototype of RPMInter-Work is implemented using JADE (Java Agent DEvelopment) platform and some evaluation results of this prototype are exposed in this paper.

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The Internet of Things (IoT) integrates a large amount of everyday life devices from heterogeneous network environments, bringing a great challenge into security and reliability management. In order to cope with certain challenges posed by device capacity and the nature of IoT networks, a lightweight access control model is needed to resolve security and privacy issues. In this paper, we present Fuzzy logic based Trust-ABAC model, an access control model for the Internet of Things. Our model for the IoT is an improvement of our previous work Trust-ABAC by a new Fuzzy logic-based model in which we consider an evaluation of trust based on recommendations and social relationship that can deal effectively with certain types of malicious behavior that intend to mislead other nodes. Results prove the performance of the proposed model and its capabilities to detect the collision and singular attacks with high precision.

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In order to cope with certain challenges posed by IoT environment and device capacity, a Self-Adaptive access control model is needed to resolve security and privacy issues. The use of complex encryption algorithms is infeasible due to the volatile nature of IoT environment and pervasive devices with limited resources. In this paper, we propose an access control middleware for the Internet of Things. The latter is an extension of the ABAC model in order to take into account the subject behavior and the trust value in the decision making process. In this work, we introduce a dynamic adaptation process of access control rules based on the risk value, the policies and rule sets which can effectively improve the security of IoT applications and produce more efficient access control mechanisms for the Internet of Things.

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In order to cope with certain challenges posed by device capacity and the nature of IoT networks, a lightweight access control model is needed to resolve security and privacy issues. The use of complex encryption algorithms is infeasible due to the volatile nature of IoT environment and pervasive devices with limited resources. In this paper, we present the Trust-ABAC, an access control model for the Internet of Things, in which a coupling between the access control based on attributes and the trust concept is done. We evaluated the performance of Trust-ABAC through an experiment based on a simulation. We used the OMNeT++ simulator to show the efficiency of our model in terms of power consumption, response time and the average number of messages generated by an access request. The obtained results of simulation prove the good scalability of our Trust-ABAC model.

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The workflow satisfiability problem has been studied by researchers in the security community using various approaches. The goal is to ensure that the user/role is authorised to execute the current task and that this permission doesn't prevent the remaining tasks in the workflow instance to be achieved. A valid authorisation plan consists in affecting authorised roles and users to workflow tasks in such a way that all the authorisation constraints are satisfied. Previous works are interested in workflow satisfiability problem by considering intra-instance constraints, i.e. constraints which are applied to a single instance. However, inter-instance constraints which are specified over multiple workflow instances are also paramount to mitigate the security frauds. In this paper, we present how ECA (Event-Condition-Action) paradigm and agent technology can be exploited to control authorisation plan in order to meet dynamic constraints, namely intra-instance and inter-instance constraints. We present a specification of a set of ECA rules that aim to achieve this goal. A prototype implementation of our proposed approach is also provided in this paper.

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In this paper we present a specification of the most common static and dynamic workflow authorisation constraints. We propose an authorisation model that includes a planning phase, an execution phase and an adjustment phase. In addition, we focus on how the problems of role-task assignment and user-task assignment are respectively translated into CSP (Constraint Satisfaction Problem) and DyCSP (Dynamic constraint Satisfaction Problem) and solved using the explanation concept. In case of an inconsistent assignment problem, we propose to restore problem consistency based upon inconsistency explanation.

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Role-Based Access Control (RBAC) model has been developed as an alternative to traditional approaches to handle access control in workflow systems. Accordingly, authorisation constraints must be defined to enforce the legal assignment of access privileges to roles and roles to users. The authorisation planning ensures that there is at least one way to complete the workflow instance without breaching any of the authorisation constraints. Authorisation planning with considering intra-instance constraints has been discussed in the research literature. However, the inter-instance constraints also need to be considered to mitigate the security fraud. In this paper, a novel authorisation system that incorporates intra-instance and inter-instance constraints is proposed. It includes the planning phase, the execution phase, and the adjustment phase. It is in charge of generating user/role assignment plans, verifying them and eventually updating them to take into account the dynamic (intra-instance and inter-instance) constraints. Besides, grounded upon agent technology and publish-subscribe communication model, a mechanism for the consideration of dynamic constraints (intra-instance and inter-intance) to generate valid assignment plans is demonstrated.

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Web services are vulnerable to different types of security attacks. The problem of secure access to web-based
applications is becoming increasingly complex. Management complexity arises because of the scalability
considerations such as the large number of web services users and their invocations and the fact that the
access control system should take into account the context. In this paper we describe the architecture of
our TDRBAC (Trust and Dynamic Role Based Access Control) model which is implemented using agent
technology. In fact, this technology fulfills several requirements of web service’s access control by providing
both context awareness and scalability. In order to verify the scalability of the proposed solution, we expose
some experimental results from a prototype implemented using JADE (Java Agent DEvelopment) platform.
The performance tests show that our TDRBAC multi-agent based system meets the scaling requirements of
large distributed services.

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The need of interoperable e-government services is addressed through the use of web services where sensitive services need to be granted to only authorized subjects from different organizations. In this paper, we propose a Trust and Dynamic Role Based Access Control model (TDRBAC) which deals with the specific requirements of e-government services. It effectively enhances the access control level since it is based on the trust level notion. The trust level evaluation is based on contextual attributes to assign to user role the appropriate view during the active session. The TDRBAC model is sensitive to the internal or external arisen events and it incorporates them in the access decision which makes it suitable for e-government dynamic environment.

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To evaluate the impact of peer-to-peer (P2P) applications on traffic in wide area networks, we analyze measurements from a high speed IP backbone link carrying TCP traffic towards several ADSL areas. The first observations are that the prevalent part of traffic is due to P2P applications (almost 80% of total traffic) and that the usage of network becomes symmetric in the sense that customers are not only clients but also servers. This latter point is observed by the significant proportion of long flows mainly composed of ACK segments. When analyzing the bit rate created by long flows, it turns out that the TCP connections due to P2P applications have a rather small bit rate and that there is no evidence for long range dependence. These facts are intimately related to the way P2P protocols are running. We separately analyze signaling traffic and data traffic. It turns out that by adopting a suitable level of aggregation, global traffic can be described by means of usual tele-traffic models based on M/G/∞ queues with Weibullian service times.

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Measurements from an Internet backbone link carrying TCP traf
f
ic towards different ADSL areas are analyzed in this paper. For
traffic analysis, we adopt a flow-based approach and the popular
mice/elephants dichotomy. The originality of the experimental
data reported in this paper, when compared with previous mea
surements from very high speed backbone links, is in that com
mercial traffic comprises a significant percentage due to peer-to
peer applications. This kind of traffic exhibits some remarkable
properties in terms of mice, elephants and bit rates, which are
thoroughly described in this paper. Mice due to p2p protocols and
mice due to classical Internet applications such as HTTP, ftp, etc.
are analyzed separately. It turns out that by adopting a suitable
level of aggregation, global traffic can be described by means of
usual tele-traffic models based on M/G/∞ queues with Weibul
lian service times. The global bit rate can be approximated by the
superposition of Gaussian processes perturbed by a white noise
and does not exhibit long range dependence.

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By distinguishing long and short TCP flows, we address in this paper the problem of efficiently computing the characteristics of long flows. Instead of using time consuming off-line flow classification procedures, we investigate how flow characteristics could directly be inferred from traffic measurements by means of digital signal processing techniques. The proposed approach consists of classifying on the fly packets according to their size in order to construct two signals, one associated with short flows and the other with long flows. Since these two signals have intertwined spectral characteristics, we use a blind source separation technique in order to reconstruct the original spectral densities of short and long flow sources. The method is applied to a real traffic trace captured on a link of the France Telecom IP backbone network and proves efficient to recover the characteristics of long and short flows.

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Measurements from an Internet backbone link carrying TCP traffic towards different ADSL areas are analyzed. For traffic analysis, we adopt a flow based approach and the popular mice/elephants dichotomy, where mice refer to short traffic transfers and elephants to long transfers. The originality of the reported experimental data, when compared with previous measurements from very high speed backbone links, is that the commercial traffic includes a significant part generated by peer-to-peer applications. This kind of traffic exhibits some remarkable properties in terms of mice and elephants, as we describe. It turns out that by adopting a suitable level of aggregation, the bit rate of mice can be described by means of a Gaussian process. The bit rate of elephants is smoother than that of mice and can also be well approximated by a Gaussian process.