Publications (267)
ARTICLE
Optimization and comparison of Deep Learning architectures for multi class classification of DDoS attacks in enterprise networks
Yacouba OUATTARA, Yaya TRAORE and Yves SAVADOGO
This article presents an in-depth study aimed at optimizing and comparing several deep learning architectures for multi-class classification of DDoS attacks in enterprise
networks, using the CIC-DDoS2019 dataset. The methodological approach includes rigorous data preprocessing (normalization, encoding, balancing, stratified split) as well as(...)
DDoS, intrusion detection, multi- class classification, Deep Learning, CNN-1D, CNN-LSTM, CNN-BiLSTM, CIC-DDoS2019.
ARTICLE
African digital health strategic plans analysis: key weaknesses in contextualization, intervention focus, and technological foresight
Bry Sylla; Ansiouonèkou Pascal Somda; Jean Noel Nikiema; Leon Gueswende Blaise Savadogo; Gayo Diallo; Nicolas Meda
Digital health strategies are increasingly being adopted in Africa, but their consistency with best practice planning is poorly documented. 54 countries were screened; 48 had a plan in the Global Digital Health Monitor, and 11 recent plans met the inclusion criteria. Using the “Ready, Extract, Analyze, Distill” methodology and a customized gri(...)
Health policy, Public health, Digital health
COMMUNICATION
A Metamodel for Simplifying Infrastructure Deployment Using Vagrant
Flavien Hervé Somda; Arnold Stéphane Kabore; Boureima Zerbo
We propose a metamodel–driven framework that replaces hand-written Ruby Vagrantfiles with validated EMF models capturing core Vagrant concepts (virtual machines, providers, networks, shares, plugins) and provisioners including Shell, Puppet, and Ansible. The framework raises the level of abstraction by allowing users to specify infrastructures(...)
Metamodeling , Infrastructure as Code , Vagrant , Virtualization , Code Generation , DevOps
ARTICLE
Deep learning models for binary ddos attack detection in enterprise environments
Yacouba OUATTARA Yaya TRAORE Yves SAVADOGO
This paper proposes an experimental methodology
based on the comparison of several deep learning models for the
detection of distributed denial of service (DDoS) attacks in
enterprise network environments. The CIC-DDoS2019 dataset,
recognized for the richness and realism of its attack scenarios,
served as a basis for the preparation, trai(...)
Cybersecurity, Deep Learning, Binary Classification, Intrusion Detection System (IDS), DDoS, CIC- DDoS2019 dataset
ARTICLE
A Hybrid Optimization Framework for Emergency Resource Allocation in Low-Resource Settings: Application to Burkina Faso
Tougma Manegaouindé Roland, Zerbo Boureima, Guel Désiré, Traore Salah Idriss Seif, Napon Salifou
The overuse of hospitals in Burkina Faso, especially during emergencies, is largely due to chronic underfunding and weak coordination among health centers. These challenges create critical bottlenecks in emergency care, where resources are limited and demand is often unpredictable. Motivated by the need for improved management of emergency pat(...)
Adaptation Models, Uncertainty, Hospitals, Decision Making, Urban Areas, Artificial Neural Networks, Linear Programming, Mathematical Models, Resource Management, Particle Swarm Optimization, Linear Programming, Particle Swarm Optimization, Artificial Neural Networks, Emergency Resource Allocation, Healthcare Optimization, Burkina Faso
ARTICLE
Network Optimization for Data Flow Control in a Security-Challenged Country: The Case of Burkina Faso
Yamba Dabone, Pengwendé Zongo & Tounwendyam Frédéric Ouedraogo
The Internet plays a crucial role in everything these days—economic development, education, and civic engagement—but it is also exploited by cybercriminals and terrorists, particularly via propaganda on social networks. In Burkina, where the terrorist threat has been present since 2016, cyber-activists sometimes relay unverified information, w(...)
Internet, IXP, Cyber-terrorism, Social network, Architecture.
ARTICLE
Deep Learning Approach for Optimized DDoS Detection in SDN
Rolph Abraham Yao, Ferdinand Tonguim Guinko
The rise of Software-Defined Network (SDN) offers great flexibility in network infrastructure management. However, this flexibility also introduces critical security challenges, in particular vulnerability to Distributed Denial of Service attacks (DDoS). Traditional detection methods are often ineffective in the face of evolving attack strateg(...)
Software-Defined Network , Distributed Denial of Service , Deep Learning , models , specific dataset , performance
ARTICLE
Literature review on classification model for human skin disease
T. Ferdinand Guinko, B. Tanguy Kaboré
The rising global burden of skin diseases requires precise and efficient diagnostic tools to improve patient care and treatment outcomes. Recent advances in artificial intelligence, particularly deep learning, have significantly contributed to the automation of the classification of skin diseases. This review presents a detailed analysis of va(...)
Skin diseases; Classification model; Artificial intelligence; Deep learning; Convolutional neural network
ARTICLE
Improving Web User Tracking Systems Through Browser Fingerprinting
Goya Alama Désiré Dao, Abdoul Kader Kaboré, Aminata Sabané, Rodrique Kafando, Tegawendé F. Bissyandé
Browser fingerprinting is a technique that involves collecting unique information from a user’s web browser to create a distinct profile. This method can be used for identification, targeted advertising, or enhancing web security. This article proposes an approach to improve browser fingerprinting for web tracking systems. We developed a JavaS(...)
Web browser, Web navigation, Client-side scripting, Web application, The Internet, Tracking system, Web page
ARTICLE
Optimizing Real-Time Video Analytics for Resource-Constrained Environments
Rodrique Kafando, Aminata Sabané, Tegawendé F. Bissyandé
Developing countries face a growing demand for video analytics, yet often lack sufficient computational resources. This paper addresses this challenge by proposing and evaluating optimization techniques for efficient video stream processing on resource-constrained devices, including edge systems. We introduce and evaluate several techniques, i(...)
Computer science, Analytics, Resource (disambiguation), Real-time computing, Data science, Computer network
ARTICLE
Text Mining for Thematic Keyword Extraction: Enriching a French Lexicon on Food Security
Rabiatou Zampaligre, Aminata Sabané, Rodrique Kafando, Abdoul Kader Kaboré, Tegawendé F. Bissyandé
Food security is a major concern in many countries in West Africa, particularly Burkina Faso. Early warning systems for food security and famines rely primarily on numerical data for analysis, while textual data, which is more complex to process, is seldom used. In this paper, we propose a textual analysis approach using text mining techniques(...)
Lexicon, Terminology, Food security, Thematic map, Process (computing), Domain (mathematical analysis), Named-entity recognition
ARTICLE
Text-to-OWL: Automated Ontology Construction for Tuberculosis Treatment Recommendation Using Generative AI
Zonabo Ouédraogo, Lydie Simone Tapsoba, Aminata Sabané, Rodrique Kafando, Abdoul Kader Kaboré, Tegawendé F. Bissyandé
This paper presents an automated approach for building ontologies to improve treatment recommendations for tuberculosis (TB), in particular multidrug-resistant tuberculosis (MDR-TB) cases in Burkina Faso, using generative language models such as GPT-3. The aim is to facilitate the personalization of treatments according to the patient profile(...)
Ontology, Generative grammar, Tuberculosis, Generative model, Semantics (computer science)
ARTICLE
Detection of Malicious Android Applications Based on Verification of Indicators of Compromise and Machine Learning Techniques
Theodore Dama, Aminata Sabané, Abdoul Kader Kaboré, Tegawendé F. Bissyandé
Android is the operating system with the largest share of the global smartphone market. The system’s popularity makes it an attractive target for malware because of its users’ data. Despite the security measures used by Google and certain researchers to combat malicious applications, some still slip through the net. In this article, we propose(...)
Android (operating system), Compromise, Support vector machine, Training set, Robustness (evolution)
COMMUNICATION
5G-NR PUSCH Receiver Optimization in Context of Intra/Inter Cell Interference
Désiré Guel, Flavien Hervé Somda, P. Justin Kouraogo, Boureima Zerbo
As 5G New Radio (5G-NR) networks evolve, managing intracell and inter-cell interference, especially during uplink transmissions on the Physical Uplink Shared Channel (PUSCH) becomes increasingly challenging. This paper presents a novel approach to optimizing User Equipment (UE) and cell configuration parameters to mitigate interference and imp(...)
5G-NR PUSCH, Intra/Inter-Cell Interference, Quality of Service (QoS), Block Error Rate (BLER)
COMMUNICATION
A Structured Metamodel for Architecture Debt Management
Flavien Hervé SOMDA, Desire GUEL
Architecture debt in information systems poses significant challenges, leading to increased maintenance costs, reduced performance, and compromised quality. Effective management of architecture debt is crucial for the sustainability and efficiency of software systems. This paper proposes a comprehensive metamodel to support managing architectu(...)
Architecture Debt, Metamodel, Impact Analysis, Traceability, Model-Driven Engineering.