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284 results on '"Korbaa, Ouajdi"'

59. Modified Graph-Based Algorithm for Efficient Hyperspectral Feature Extraction

Author

Fejjari, Asma, Ettabaa, Karim Saheb, Korbaa, Ouajdi, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Czachórski, Tadeusz, editor, Gelenbe, Erol, editor, Grochla, Krzysztof, editor, and Lent, Ricardo, editor

Published
2018
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60. Heuristics for the Hybrid Flow Shop Scheduling Problem with Parallel Machines at the First Stage and Two Dedicated Machines at the Second Stage

Author

Nabli, Zouhour, Khalfallah, Soulef, Korbaa, Ouajdi, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Abraham, Ajith, editor, Muhuri, Pranab Kr., editor, Muda, Azah Kamilah, editor, and Gandhi, Niketa, editor

Published
2018
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71. Active intrusion detection and prediction based on temporal big data analytics.

Author

Jemili, Farah and Korbaa, Ouajdi

Subjects
*INTRUSION detection systems (Computer security), *TEMPORAL databases, *EXPERT systems, *MACHINE learning, *COMPUTER security, *COMPUTER network security, *DECISION trees
Abstract

Computer security consists in protecting access and manipulating system data by several mechanisms. However, conventional protection technologies are ineffective against current attacks. Thus, new tools have appeared, such as the intrusion detection and prediction systems which are important defense elements for network security since they detect the ongoing intrusions and predict the upcoming attacks. Besides, most of conventional protection technologies remain insufficient in terms of actions since they are all passive systems, unable to provide recommendations in order to block or stop the attacks. In this paper, a distributed detection and prediction system, composed of three major parts, is proposed. The first part deals with the detection of intrusions based on the decision tree learning algorithm. The second part deals with intrusions prediction using the chronicle algorithm. The third part proposes an expert system for security recommendations in response to detected intrusions, able to provide appropriate recommendations to stop the attacks. The proposed system gives good results in terms of accuracy and precision in detecting and predicting attacks, and efficiency in proposing the right recommendations to stop the attacks. [ABSTRACT FROM AUTHOR]

Published
2024
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85. Convolutional neural network for intrusion detection using blockchain technology

Author

Aljabri, Ahmed, Jemili, Farah, and Korbaa, Ouajdi

Abstract

Cyber-physical systems (CPS) are becoming increasingly ubiquitous, connecting the physical world with the cyber realm. This convergence has exposed CPS to a growing threat landscape, necessitating robust intrusion detection systems (IDS) to safeguard critical infrastructure. Deep learning (DL) has emerged as a powerful tool for IDS, and convolutional neural networks (CNNs) have demonstrated exceptional performance in this domain. However, traditional IDS models are susceptible to data tampering and manipulation, compromising their integrity and effectiveness. Blockchain technology, with its inherent immutability and tamper-proof nature, offers a promising solution to enhance the security and reliability of IDS models. In this study, we propose a CNN-based IDS model that leverages blockchain technology to secure network traffic data. Our hypothesis is that integrating blockchain with CNNs can significantly improve the security and robustness of IDS models against data tampering and manipulation. To test our hypothesis, we employ a greedy-based genetic algorithm to select the most relevant features from network traffic data, followed by training a CNN model using the selected features. Finally, we evaluate the trained CNN model on a real-world dataset, demonstrating its ability to accurately classify network traffic as normal or intrusive. The results of our evaluation reveal that the proposed CNN-based IDS model achieves a classification accuracy of 99.2%, surpassing traditional IDS models. Moreover, our model exhibits enhanced resilience against data tampering and manipulation, demonstrating the effectiveness of blockchain integration in safeguarding the integrity of IDS models. Our findings underscore the potential of blockchain-enhanced CNNs as a robust and secure solution for intrusion detection in CPS, ensuring the integrity and protection of critical infrastructure.

Published
2024
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90. DRIFT-DRIVEN REGRESSION FOR PREDICTING THE EVOLUTION OF PANDEMICS.

Author

Jouini, Khaled and Korbaa, Ouajdi

Subjects
COVID-19 pandemic, REGRESSION analysis, PUBLIC health, PREDICTION models, MACHINE learning
Abstract

Pandemics will never cease to emerge and threaten public health and the global economy. Predicting the evolution of a pandemic is of paramount interest as it enables policymakers understand the potential spread of a virus and make informed decisions to mitigate its impact. Concept drifts refer to situations where the relationship between the input features (model input) and the learning targets (model output) changes or evolves over time. Concept drifts are common in pandemic curves, not only because new variants appear over time, but also due to factors such as seasonality, policy responses to the pandemic, and changes in the way the disease is treated. Without proper intervention, the accuracy of conventional (batch) machine learning models will deteriorate after drift occurs, since they were trained on outdated data. Incremental learning is an alternative to batch learning, where the training phase never ends, and the model is incrementally updated as new data becomes available. While incremental models are in general less accurate than batch models, they adapt better to concept drifts as they are continuously refined using the most recent data. Batch and incremental learning are often considered as two distinct and mutually exclusive approaches (Montiel et al., 2018a). In this work we propose CDR (Collaborative Drift-Driven Regression), a novel collaborative regression strategy where incremental and batch regressors work together to complement each other's strengths, ergo the overall predictive performance. Experiments conducted on COVID-19 pandemic data, show that CDR is an efficient collaborative learning strategy that yields better results than the underlying batch and incremental models used separately. [ABSTRACT FROM AUTHOR]

Published
2023

94. What do you mean by learning lab?

Author

Sanchez, Eric, primary, Paukovics, Elsa, additional, Cheniti-Belcadhi, Lilia, additional, El Khayat, Ghada, additional, Said, Bilal, additional, and Korbaa, Ouajdi, additional

Published
2021
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99. Cyclic scheduling for F.M.S.: Modelling and evolutionary solving approach

Author

Hsu, Tiente, Korbaa, Ouajdi, Dupas, ReMy, and Goncalves, Gilles

Subjects
Algorithms -- Analysis, Algorithms -- Models, Production engineering -- Analysis, Production engineering -- Models, Department stores -- Analysis, Department stores -- Models, Algorithm, Business, Business, general, Business, international
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2007.02.055 Byline: Tiente Hsu (a), Ouajdi Korbaa (b), Remy Dupas (a), Gilles Goncalves (a) Keywords: Flexible manufacturing systems; Cyclic scheduling; Genetic algorithm; Petri nets Abstract: This paper concerns the domain of flexible manufacturing systems (FMS) and focuses on the scheduling problems encountered in these systems. We have chosen the cyclic behaviour to study this problem, to reduce its complexity. This cyclic scheduling problem, whose complexity is NP-hard in the general case, aims to minimise the work in process (WIP) to satisfy economic constraints. We first recall and discuss the best known cyclic scheduling heuristics. Then, we present a two-step resolution approach. In the first step, a performance analysis is carried out; it is based on the Petri net modelling of the production process. This analysis resolves some indeterminism due to the system's flexibility and allows a lower bound of the WIP to be obtained. In the second step, after a formal model of the scheduling problem has been given, we describe a genetic algorithm approach to find a schedule which can reach the optimal production speed while minimizing the WIP. Finally, our genetic approach is validated and compared with known heuristics on a set of test problems. Author Affiliation: (a) Laboratoire de Genie Informatique et Automatique de l'Artois (L.G.I.2.A), Universite d'Artois Faculte des Sciences Appliquees, Technoparc Zone Futura BP 819, 62400 Bethune, France (b) Institut Superieur d'Informatique et des Technologies de Communication de Hammam Sousse, 5 bis rue 01 Juin 1955, 4011 Hammam Sousse, Tunisia Article History: Received 27 January 2004; Accepted 1 February 2007

Published
2008

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