Publications (280)
ARTICLE
Towards a Semantic Architecture for Data Lakes
Sione, A., Traore, Y., Thiombiano, J.
Data lakes are a storage system for large volumes of raw heterogeneous data, adopting an area-based architecture. The main challenge of this architecture is the extraction and storage of raw data without any content monitoring, making data processing and access difficult. In this paper, we propose the integration of an ontology into this archi(...)
Data lake , Architecture, Ontology Area Semantic
ARTICLE
Data Search in Smart GIS Database Using Map Reduce Pattern and Bayesian Probability
Moubaric Kabore, Abdoulaye Sere and Vini Yves Bernadin Loyara
This paper deals with Bayesian approach in Data Research in GIS database through artificial Intelligence (AI) modules, reading the best bayesian probability before returning the data requested, denoted AI4DB. The proposed method combines meshing techniques and the map-reduce algorithm with Bayesian approach to obtain a smart GIS database to re(...)
MapReduce, GIS, Bayesian Probability
ARTICLE
You Got Phished! Analyzing How to Provide Useful Feedback in Anti-Phishing Training with LLM Teacher Models
Tailia Malloy, Laura Bernardy, Omar El Bachyr, Fred Philippy, Jordan Samhi, Jacques Klein, Tegawendé F. Bissyandé
Training users to correctly identify potential security threats like social engineering attacks such as phishing emails is a crucial aspect of cybersecurity. One challenge in this training is providing useful educational feedback to maximize student learning outcomes. Large Language Models (LLMs) have recently been applied to wider and wider a(...)
cybersecurity, phishing, large language models, education, embeddings
ARTICLE
Large Language Models Adaptation for Web Applications Attacks Detection
Nana Sidwendluian Romaric, Bassolé Didier, Guel Désiré, Sié Oumarou
Large Language Models (LLMs) represent a major advance in the field of deep learning. Their ability to understand long-term dependencies between words in a sentence has completely revolutionized natural language processing. Based on the architecture of transformers, LLMs are trained to solve common linguistic problems, such as text classificat(...)
Adaptation models, Accuracy, Large language models, Text categorization, Bidirectional control, Transformers, Encoding, Service-oriented architecture, Computer security, Payloads, Fine-tuning;Large Language Model, Web application attacks, Detection
ARTICLE
Optimizing DDoS attack detection in SDN using machine learning
Rolph Abraham Yao, Ferdinand Tonguim Guinko
Distributed Denial of Service attacks are a major threat to network security, particularly for Software-Defined Networks. Despite their centralized and flexible management, they are particularly vulnerable to Distributed Denial of Service attacks. In this paper, an effective approach to identifying Distributed Denial of Service attacks based o(...)
Distributed Denial of Service , Software-Defined Network , model , Machine Learning , dataset , performance
ARTICLE
Exploring the Role of Artificial Intelligence in Enhancing Security Operations: A Systematic Review
Despoina Giarimpampa, Roland Meier, Tegawendé F. Bissyande, Vincent Lenders, Jacques Klein
Artificial intelligence (AI) is reshaping Security Operations Centers (SOCs). This systematic literature review analyses AI’s transformative impact across the NIST Cybersecurity Framework. The analysis of 189 papers related to AI use-cases for SOCs shows widespread application of AI for detection, with 65% of studies focusing on it. Yet, it al(...)
A Systematic Review
ARTICLE
Software Engineering for OpenHarmony: A Research Roadmap
Li Li, Xiang Gao, Hailong Sun, Chunming Hu, Carolyn Sun, Haoyu Wang, Haipeng Cai, Ting Su, Xiapu Luo, Tegawendé Bissyande, Jacques Klein, John Grundy, Tao Xie, Haibo Chen, Huaimin Wang
Mobile software engineering has been a hot research topic for decades. Our fellow researchers have proposed various approaches (with over 7,000 publications for Android alone) in this field that essentially contributed to the great success of the current mobile ecosystem. Existing research efforts mainly focus on popular mobile platforms, name(...)
Software Engineering
ARTICLE
Health centers network analysis with Gephi and ForceAtlas2 approach: Case of Burkina Faso
Saan-nonnan Olivier Dabire, Désiré Guel, Boureima Zerbo
Burkina Faso, like many developing countries, faces significant challenges in public health, particularly regarding healthcare access and infrastructure distribution. Healthcare centers are unevenly distributed across regions, resulting in disparities in access to care. This study aims to analyze the structure and efficiency of the healthcare(...)
Force Atlas2, Graph theory, Modularity, Density, Health network, Burkina Faso
COMMUNICATION
Do you have 5 min? Improving Call Graph Analysis with Runtime Information
Jordan Samhi, Marc Miltenberger, Marco Alecci, Steven Arzt, Tegawendé Bissyandé, Jacques Klein
Constructing precise and sound call graphs is fundamental for effective static analysis, yet it remains a significant challenge in today's software. Traditionally, researchers have developed sophisticated algorithms to address this issue, often resulting in increased computational costs. But what if we could provide a simple, cost-effective wa(...)
Improving Call Graph
ARTICLE
Artificial intelligence for IoT threat detection: Case of DDoS attacks
Dr. Yacouba OUATTARA Dr. Yaya TRAORE D. Moumine Arthur OUEDRAOGO
With the rapid expansion of connected objects in our daily lives, the risks of cyberattacks, particularly by distributed denial of service (DDoS), have increased considerably. IoT devices, often designed with few resources and little protection, are easy targets for cybercriminals. In this context, our study explores the role of artificial int(...)
IoT, DDoS, Machine Learning, Deep Learning, K-Means, Isolation Forest, Random Forest
ARTICLE
DDoS Attacks Simulation on a Storage Cloud: Impacts and Appropriate Security Mechanisms
Almissi Amed Kindo, Didier Bassole, Gouayon Koala, Oumarou Sié
Safety, availability, confidentiality and integrity are essentials characteristics for any user of cloud services. Distributed Denial of Service (DDoS) attacks are one of the most widespread threats on cloud computing. In this paper, we simulate DDoS attacks on a storage cloud and we try to assess the impact on the integrity, availability and(...)
DDoS, Cloud Computing, Storage cloud, LOIC, XerXes
ARTICLE
ChatGPT Does Not Understand Moore
Aminata Sabané, Tegawendé F. Bissyandé
The advent of Large Language Models (LLMs) likely constitutes a turning point in the history of mankind. LLMs have the potential to revolutionize the way we interact with computers and each other. While current state of the art LLMs mostly support western languages, such as English, it is paramount that we consider the opportunity of integrati(...)
Computer science
ARTICLE
You Don’t Have to Say Where to Edit! jLED—Joint Learning to Localize and Edit Source Code
Weiguo Pian, Yinghua Li, Haoye Tian, Tiezhu Sun, Yewei Song, Xunzhu Tang, Andrew Habib, Jacques Klein, Tegawendé F. Bissyandé
Learning to edit code automatically is becoming more and more feasible. Thanks to recent advances in Neural Machine Translation (NMT), various case studies are being investigated where patches are automatically produced and assessed either automatically (using test suites) or by developers themselves. An appealing setting remains when the deve(...)
Learning
ARTICLE
Advancements in Deep Learning for Malaria Detection: A Comprehensive Overview
Kiswendsida Kisito Kabore and Desire Guel and Flavien Herve Somda
Malaria remains a critical global health issue, with millions of cases reported annually, particularly in resource-limited regions. Timely and accurate diagnosis is vital to ensure effective treatment, reduce complications, and control transmission. Conventional diagnostic methods, including microscopy and Rapid Diagnostic Tests (RDTs), face c(...)
Malaria detection, Deep Learning, Convolutional Neural Networks (CNN), Medical Imaging, Automated diagnostics
ARTICLE
AudioTest: Prioritizing Audio Test Cases
Yinghua Li, Xueqi Dang, Wendkûuni C. Ouédraogo, Jacques Klein, Tegawendé F. Bissyandé
Audio classification systems, powered by deep neural networks (DNNs), are integral to various applications that impact daily lives, like voice-activated assistants. Ensuring the accuracy of these systems is crucial since inaccuracies can lead to significant security issues and user mistrust. However, testing audio classifiers presents a signif(...)
Prioritizing Audio Test Cases