Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Elhajjout, A.; Houda, Z. A. E.; Moudoud, H.; Brik, B.; Jan, M. A.
Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions Article de journal
Dans: IEEE Journal of Biomedical and Health Informatics, 2026, ISSN: 21682194 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Application research, Artificial intelligence, digital health, Distributed computer systems, Federated learning, Fine tuning, Health care, Health care application, Healthcare AI, human, Internet, Internet of medical thing, Internet of Medical Things, Language model, Large language model, large language models, Learning systems, male, Medical computing, natural language processing, Parameter-efficient fine-tuning, patient coding, Privacy, Privacy preservation, Privacy preserving, Privacy-preserving techniques, review, Sensitive data, trustworthiness
@article{elhajjoutFederatedLargeLanguage2026,
title = {Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and Future Research Directions},
author = {A. Elhajjout and Z. A. E. Houda and H. Moudoud and B. Brik and M. A. Jan},
url = {https://www.scopus.com/pages/publications/105034640126?origin=resultslist},
doi = {10.1109/JBHI.2026.3679612},
issn = {21682194 (ISSN)},
year = {2026},
date = {2026-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Large-scale foundation models, especially Federated Large Language Models (FLLMs), aim to transform digital health by enabling clinically-grade natural language processing while keeping sensitive data local. However, their adoption is hindered by two main issues: (i) the computational and communication burden of parameter-rich models on resource-constrained Internet-of-Medical-Things (IoMT) devices, and (ii) performance degradation caused by Non-Independent and Identically Distributed (Non-IID) patient data. This paper presents a comprehensive survey of Federated Learning (FL) for LLMs in Healthcare (FedMed-LLMs). We review the foundations of FL and medical LLMs. Then, we present the FL-enabled LLMs applications in healthcare, and we examine their issues in terms of privacy, robustness, and trustworthiness. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions for building robust and scalable FedMed-LLMs systems. © 2013 IEEE.},
keywords = {Application research, Artificial intelligence, digital health, Distributed computer systems, Federated learning, Fine tuning, Health care, Health care application, Healthcare AI, human, Internet, Internet of medical thing, Internet of Medical Things, Language model, Large language model, large language models, Learning systems, male, Medical computing, natural language processing, Parameter-efficient fine-tuning, patient coding, Privacy, Privacy preservation, Privacy preserving, Privacy-preserving techniques, review, Sensitive data, trustworthiness},
pubstate = {published},
tppubtype = {article}
}
Large-scale foundation models, especially Federated Large Language Models (FLLMs), aim to transform digital health by enabling clinically-grade natural language processing while keeping sensitive data local. However, their adoption is hindered by two main issues: (i) the computational and communication burden of parameter-rich models on resource-constrained Internet-of-Medical-Things (IoMT) devices, and (ii) performance degradation caused by Non-Independent and Identically Distributed (Non-IID) patient data. This paper presents a comprehensive survey of Federated Learning (FL) for LLMs in Healthcare (FedMed-LLMs). We review the foundations of FL and medical LLMs. Then, we present the FL-enabled LLMs applications in healthcare, and we examine their issues in terms of privacy, robustness, and trustworthiness. Finally, we present a set of core research problems and a comprehensive research agenda that identifies future directions for building robust and scalable FedMed-LLMs systems. © 2013 IEEE.
2.
Kadi, A.; Moudoud, H.; Khoukhi, L.; Houda, Z. A. El
Quantum-Enhanced LSTM for Sequential Network Flow Analysis: A Hybrid Approach to DDoS Detection Article d'actes
Dans: Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC, p. 830–834, Institute of Electrical and Electronics Engineers Inc., 2026, ISBN: 979-833156110-9 (ISBN), (Journal Abbreviation: Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC).
Résumé | Liens | BibTeX | Étiquettes: Denialof- service attacks, Distributed computer systems, Distributed denial of service, Distributed denial-of-service, Distributed Denial-of-Service (DDoS), Hybrid approach, Intrusion Detection, Learning systems, Logic gates, Long short-term memory, Machine-learning, Memory architecture, Network architecture, Network flow analysis, Network security, QLSTM, Quantum entanglement, Quantum machine learning, Quantum Machine Learning(QML), Quantum machines, Qubits, short term memory
@inproceedings{kadiQuantumEnhancedLSTMSequential2026,
title = {Quantum-Enhanced LSTM for Sequential Network Flow Analysis: A Hybrid Approach to DDoS Detection},
author = {A. Kadi and H. Moudoud and L. Khoukhi and Z. A. El Houda},
url = {https://www.scopus.com/pages/publications/105040813271?origin=resultslist},
doi = {10.1109/QCNC69040.2026.00136},
isbn = {979-833156110-9 (ISBN)},
year = {2026},
date = {2026-01-01},
booktitle = {Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC},
pages = {830–834},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Distributed Denial-of-Service (DDoS) attacks continue to grow at a rapid rate, making timely and reliable detection a challenge for intrusion detection systems (IDSs). Classical deep models such as Long Short-Term Memory (LSTM) can capture temporal dependencies; however, they still struggle with highly nonlinear and noisy flow dynamics, especially when attack patterns shift. To address this, we propose QLSTM-E, an enhanced hybrid quantum-classical architecture for network intrusion detection that uses Variational Quantum Circuits (VQCs) and LSTM modeling to learn complex temporal dependencies in network traffic. QLSTM-E exploits quantum characteristics, including superposition and entanglement, to improve the representation of nonlinear patterns in sequential flow features. To balance expressivity and circuit cost on near-term devices, we adopt an angle-based encoding strategy and a star-topology entangling layout that reduces two-qubit gate overhead while preserving effective quantum correlations. We implement QLSTM-E using PennyLane and Qiskit and evaluate it on the CIC-DDoS2019 dataset under a fully simulated setting, requiring no access to quantum hardware. Experimental results demonstrate strong detection effectiveness, achieving 99.7% accuracy and F1-score, and show strong robustness under depolarizing noise up to p= 0.1. © 2026 IEEE.},
note = {Journal Abbreviation: Proc. - Int. Conf. Quantum Commun., Netw., Comput., QCNC},
keywords = {Denialof- service attacks, Distributed computer systems, Distributed denial of service, Distributed denial-of-service, Distributed Denial-of-Service (DDoS), Hybrid approach, Intrusion Detection, Learning systems, Logic gates, Long short-term memory, Machine-learning, Memory architecture, Network architecture, Network flow analysis, Network security, QLSTM, Quantum entanglement, Quantum machine learning, Quantum Machine Learning(QML), Quantum machines, Qubits, short term memory},
pubstate = {published},
tppubtype = {inproceedings}
}
Distributed Denial-of-Service (DDoS) attacks continue to grow at a rapid rate, making timely and reliable detection a challenge for intrusion detection systems (IDSs). Classical deep models such as Long Short-Term Memory (LSTM) can capture temporal dependencies; however, they still struggle with highly nonlinear and noisy flow dynamics, especially when attack patterns shift. To address this, we propose QLSTM-E, an enhanced hybrid quantum-classical architecture for network intrusion detection that uses Variational Quantum Circuits (VQCs) and LSTM modeling to learn complex temporal dependencies in network traffic. QLSTM-E exploits quantum characteristics, including superposition and entanglement, to improve the representation of nonlinear patterns in sequential flow features. To balance expressivity and circuit cost on near-term devices, we adopt an angle-based encoding strategy and a star-topology entangling layout that reduces two-qubit gate overhead while preserving effective quantum correlations. We implement QLSTM-E using PennyLane and Qiskit and evaluate it on the CIC-DDoS2019 dataset under a fully simulated setting, requiring no access to quantum hardware. Experimental results demonstrate strong detection effectiveness, achieving 99.7% accuracy and F1-score, and show strong robustness under depolarizing noise up to p= 0.1. © 2026 IEEE.
3.
Moudoud, H.; Houda, Z. Abou El; Brik, B.
Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain Article de journal
Dans: IEEE Internet of Things Journal, vol. 12, no 22, p. 46129–46137, 2025, ISSN: 23274662 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Artificial intelligence algorithms, Artificial intelligence techniques, Block-chain, Blockchain, cancer diagnosis, Diagnosis, Distributed computer systems, Federated edge learning, Health care, Healthcare, Healthcare systems, Internet of medical thing, Internet of Medical Things (IoMT), Learning systems, Medical computing, Medical data, Network security, Privacy, Sensitive data
@article{moudoudAdvancingPrivacyFairness2025,
title = {Advancing Privacy and Fairness in Healthcare Using Federated Edge Learning and Blockchain},
author = {H. Moudoud and Z. Abou El Houda and B. Brik},
url = {https://www.scopus.com/pages/publications/105012264671?origin=resultslist},
doi = {10.1109/JIOT.2025.3589179},
issn = {23274662 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Internet of Things Journal},
volume = {12},
number = {22},
pages = {46129–46137},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {artificial intelligence (AI) has revolutionized many fields, including healthcare. The adoption of AI techniques in critical healthcare tasks, such as cancer diagnosis, holds great promise for revolutionizing the healthcare system. AI algorithms can be trained on vast datasets to recognize patterns, detect anomalies, and provide accurate assessments. However, the lack of realistic and up-to-date medical data poses a significant challenge to the widespread adoption of AI techniques. Additionally, privacy concerns surrounding sensitive medical data, particularly patient health records (PHRs), hinder data sharing among healthcare practitioners. This article aims to address these challenges by proposing a novel framework, entitled SecureMed, that uses federated learning (FL) and Blockchain to preserve privacy in the healthcare system. In particular, SecureMed consists of: 1) a novel distributed architecture that enables secure collaboration among multiple mobile edge computing (MEC)-based Internet of Medical Things (IoMT) devices, while ensuring the privacy of healthcare systems; 2) a fairness-aware FL solution to ensure that model performance is balanced across all participating healthcare institutions, addressing the issue of imbalanced data contributions; 3) a secure multiparty computation (SMPC) protocol to ensure secure aggregation of local model updates; and 4) a blockchain-based reputation model for collaborative FL training. The proposed framework leverages smart contracts to ensure trustworthiness, decentralization, and transparency in the FL process. The experimental results using the CIC IoMT dataset 2024 highlight the promising potential of SecureMed in revolutionizing healthcare systems. © 2014 IEEE.},
keywords = {Artificial intelligence algorithms, Artificial intelligence techniques, Block-chain, Blockchain, cancer diagnosis, Diagnosis, Distributed computer systems, Federated edge learning, Health care, Healthcare, Healthcare systems, Internet of medical thing, Internet of Medical Things (IoMT), Learning systems, Medical computing, Medical data, Network security, Privacy, Sensitive data},
pubstate = {published},
tppubtype = {article}
}
artificial intelligence (AI) has revolutionized many fields, including healthcare. The adoption of AI techniques in critical healthcare tasks, such as cancer diagnosis, holds great promise for revolutionizing the healthcare system. AI algorithms can be trained on vast datasets to recognize patterns, detect anomalies, and provide accurate assessments. However, the lack of realistic and up-to-date medical data poses a significant challenge to the widespread adoption of AI techniques. Additionally, privacy concerns surrounding sensitive medical data, particularly patient health records (PHRs), hinder data sharing among healthcare practitioners. This article aims to address these challenges by proposing a novel framework, entitled SecureMed, that uses federated learning (FL) and Blockchain to preserve privacy in the healthcare system. In particular, SecureMed consists of: 1) a novel distributed architecture that enables secure collaboration among multiple mobile edge computing (MEC)-based Internet of Medical Things (IoMT) devices, while ensuring the privacy of healthcare systems; 2) a fairness-aware FL solution to ensure that model performance is balanced across all participating healthcare institutions, addressing the issue of imbalanced data contributions; 3) a secure multiparty computation (SMPC) protocol to ensure secure aggregation of local model updates; and 4) a blockchain-based reputation model for collaborative FL training. The proposed framework leverages smart contracts to ensure trustworthiness, decentralization, and transparency in the FL process. The experimental results using the CIC IoMT dataset 2024 highlight the promising potential of SecureMed in revolutionizing healthcare systems. © 2014 IEEE.
4.
Moudoud, H.; Houda, Z. A. El; Brik, B.
Securing O-RAN with Zero Trust Architecture and Large Language Models Article d'actes
Dans: C., Iwendi; Z., Boulouard; N., Kryvinska (Ed.): Lect. Notes Networks Syst., p. 357–368, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23673370 (ISSN); 978-303194619-6 (ISBN), (Journal Abbreviation: Lect. Notes Networks Syst.).
Résumé | Liens | BibTeX | Étiquettes: Access management, Access Management system, Architecture, Authentication, Block-chain, Blockchain, Computer architecture, Computer crime, Cryptography, Distributed computer systems, Intrusion Detection, Language model, Large language model, Management systems, Mobile security, Mobile telecommunication systems, Network architecture, Network security, O-RAN, Open radio access network, Radio access networks, Security systems, Security vulnerabilities, Trusted computing, Zero Trust
@inproceedings{moudoudSecuringORANZero2025,
title = {Securing O-RAN with Zero Trust Architecture and Large Language Models},
author = {H. Moudoud and Z. A. El Houda and B. Brik},
editor = {Iwendi C. and Boulouard Z. and Kryvinska N.},
url = {https://www.scopus.com/pages/publications/105011259647?origin=resultslist},
doi = {10.1007/978-3-031-94620-2_31},
isbn = {23673370 (ISSN); 978-303194619-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Networks Syst.},
volume = {1312 LNNS},
pages = {357–368},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The Open Radio Access Network (O-RAN) architecture is critical for the development of 6G networks, offering flexibility and interoperability through disaggregated components. However, this openness exposes O-RAN to new security vulnerabilities, including unauthorized access, data breaches, and malicious xApp deployments. To address these challenges, we propose DistillORAN, a novel Zero-Trust architecture designed specifically for O-RAN. DistillORAN features two core components: (1) a blockchain-based decentralized trust management system for secure verification, authentication, and dynamic access control of xApps, and (2) a lightweight intrusion detection module powered by DistilBERT, a transformer-based model optimized for resource-constrained environments. DistilBERT’s ability to analyze network activities and detect anomalies in real-time allows it to identify complex security threats and multi-step attack scenarios within the O-RAN ecosystem. Its lightweight nature makes it ideal for O-RAN’s distributed infrastructure, where computational resources may be limited. By combining blockchain technology for trust management with DistilBERT’s powerful pattern recognition for intrusion detection, DistillORAN enforces a Zero-Trust security model, ensuring continuous monitoring and verification of all network components. This comprehensive solution enhances the security and resilience of O-RAN networks, aligning with the dynamic needs of next-generation mobile infrastructures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
note = {Journal Abbreviation: Lect. Notes Networks Syst.},
keywords = {Access management, Access Management system, Architecture, Authentication, Block-chain, Blockchain, Computer architecture, Computer crime, Cryptography, Distributed computer systems, Intrusion Detection, Language model, Large language model, Management systems, Mobile security, Mobile telecommunication systems, Network architecture, Network security, O-RAN, Open radio access network, Radio access networks, Security systems, Security vulnerabilities, Trusted computing, Zero Trust},
pubstate = {published},
tppubtype = {inproceedings}
}
The Open Radio Access Network (O-RAN) architecture is critical for the development of 6G networks, offering flexibility and interoperability through disaggregated components. However, this openness exposes O-RAN to new security vulnerabilities, including unauthorized access, data breaches, and malicious xApp deployments. To address these challenges, we propose DistillORAN, a novel Zero-Trust architecture designed specifically for O-RAN. DistillORAN features two core components: (1) a blockchain-based decentralized trust management system for secure verification, authentication, and dynamic access control of xApps, and (2) a lightweight intrusion detection module powered by DistilBERT, a transformer-based model optimized for resource-constrained environments. DistilBERT’s ability to analyze network activities and detect anomalies in real-time allows it to identify complex security threats and multi-step attack scenarios within the O-RAN ecosystem. Its lightweight nature makes it ideal for O-RAN’s distributed infrastructure, where computational resources may be limited. By combining blockchain technology for trust management with DistilBERT’s powerful pattern recognition for intrusion detection, DistillORAN enforces a Zero-Trust security model, ensuring continuous monitoring and verification of all network components. This comprehensive solution enhances the security and resilience of O-RAN networks, aligning with the dynamic needs of next-generation mobile infrastructures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
5.
Papapanagiotou, P.; Manataki, A.; Davoust, A.; Kleek, M. Van; Robertson, D.; Murray-Rust, D.; Shadbolt, N.
Social machines for all: Blue sky ideas track Article d'actes
Dans: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, p. 1208–1212, International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), 2018, ISSN: 15488403, (ISSN: 15488403).
Résumé | Liens | BibTeX | Étiquettes: Agent based simulation, Analysis, Autonomous agents, Design, Development method, Distributed agents, Distributed computer systems, Easy-to-use systems, Economic and social effects, Electronic institutions, Intelligent agents, Model driven development, Model-driven Engineering, Models, Multi agent systems, Systems analysis
@inproceedings{papapanagiotou_social_2018,
title = {Social machines for all: Blue sky ideas track},
author = {P. Papapanagiotou and A. Manataki and A. Davoust and M. Van Kleek and D. Robertson and D. Murray-Rust and N. Shadbolt},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054668594&partnerID=40&md5=77eba348dbafa30aef9d016186b46804},
issn = {15488403},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS},
volume = {2},
pages = {1208–1212},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)},
abstract = {In today's interconnected world, people interact to a unprecedented degree through the use of digital platforms and services, forming complex 'social machines'. These are now homes to autonomous agents as well as people, providing an open space where human and computational intelligence can mingle-a new frontier for distributed agent systems. However, participants typically have limited autonomy to define and shape the machines they are part of. In this paper, we envision a future where individuals are able to develop their own Social Machines, enabling them to interact in a trustworthy, decentralized way. To make this possible, development methods and tools must see their barriers-to-entry dramatically lowered. People should be able to specify the agent roles and inte-raction patterns in an intuitive, visual way, analyse and test their designs and deploy them as easy to use systems. We argue that this is a challenging but realistic goal, which should be tackled by navigating the trade-off between the accessibility of the design methods -primarily the modelling formalisms- And their expressive power. We support our arguments by drawing ideas from different research areas including electronic institutions, agent-based simulation, process modelling, formal verification, and model-driven engineering. © 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.},
note = {ISSN: 15488403},
keywords = {Agent based simulation, Analysis, Autonomous agents, Design, Development method, Distributed agents, Distributed computer systems, Easy-to-use systems, Economic and social effects, Electronic institutions, Intelligent agents, Model driven development, Model-driven Engineering, Models, Multi agent systems, Systems analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
In today's interconnected world, people interact to a unprecedented degree through the use of digital platforms and services, forming complex 'social machines'. These are now homes to autonomous agents as well as people, providing an open space where human and computational intelligence can mingle-a new frontier for distributed agent systems. However, participants typically have limited autonomy to define and shape the machines they are part of. In this paper, we envision a future where individuals are able to develop their own Social Machines, enabling them to interact in a trustworthy, decentralized way. To make this possible, development methods and tools must see their barriers-to-entry dramatically lowered. People should be able to specify the agent roles and inte-raction patterns in an intuitive, visual way, analyse and test their designs and deploy them as easy to use systems. We argue that this is a challenging but realistic goal, which should be tackled by navigating the trade-off between the accessibility of the design methods -primarily the modelling formalisms- And their expressive power. We support our arguments by drawing ideas from different research areas including electronic institutions, agent-based simulation, process modelling, formal verification, and model-driven engineering. © 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
6.
Esfandiari, B.; Davoust, A.
Distributed Wikis and Social Networks: a Good Fit Article d'actes
Dans: WWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web, p. 937–938, Association for Computing Machinery, Inc, 2016, ISBN: 978-145034144-8 (ISBN), (Journal Abbreviation: WWW Companion - Proc. Int. Conf. World Wide Web).
Résumé | Liens | BibTeX | Étiquettes: Discussion forum, Distributed computer systems, distributed wiki, distributed wikis, Peer to peer, Peer to peer networks, Self-organize, Social network, Social networking (online), social networks, Subjective quality, World Wide Web
@inproceedings{esfandiari_distributed_2016,
title = {Distributed Wikis and Social Networks: a Good Fit},
author = {B. Esfandiari and A. Davoust},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115132914&doi=10.1145%2f2872518.2890584&partnerID=40&md5=8887bea19553f8239bf60ebf694bac82},
doi = {10.1145/2872518.2890584},
isbn = {978-145034144-8 (ISBN)},
year = {2016},
date = {2016-01-01},
booktitle = {WWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web},
pages = {937–938},
publisher = {Association for Computing Machinery, Inc},
abstract = {Social networks can play an important role in the process of decentralizing authority in distributed systems. We will focus on distributed wiki systems, and we show how, in the special case of a peer-to-peer wiki, there is a rational incentive for users to self-organize and form a meaningful social network. We discuss to that effect the basic metrics that can be derived from the topology of the social network to help assess the subjective quality of wiki entries. Demos and experimental results will illustrate and support our discussion. We finally speculate as to how these results may also translate to discussion forums or recommender systems. © 2016 International World Wide Web Conference Committee (IW3C2).},
note = {Journal Abbreviation: WWW Companion - Proc. Int. Conf. World Wide Web},
keywords = {Discussion forum, Distributed computer systems, distributed wiki, distributed wikis, Peer to peer, Peer to peer networks, Self-organize, Social network, Social networking (online), social networks, Subjective quality, World Wide Web},
pubstate = {published},
tppubtype = {inproceedings}
}
Social networks can play an important role in the process of decentralizing authority in distributed systems. We will focus on distributed wiki systems, and we show how, in the special case of a peer-to-peer wiki, there is a rational incentive for users to self-organize and form a meaningful social network. We discuss to that effect the basic metrics that can be derived from the topology of the social network to help assess the subjective quality of wiki entries. Demos and experimental results will illustrate and support our discussion. We finally speculate as to how these results may also translate to discussion forums or recommender systems. © 2016 International World Wide Web Conference Committee (IW3C2).
7.
Murtaza, S. S.; Khreich, W.; Hamou-Lhadj, A.; Gagnon, S.
A trace abstraction approach for host-based anomaly detection Article d'actes
Dans: 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2015 - Proceedings, p. 170–177, Institute of Electrical and Electronics Engineers Inc., 2015, ISBN: 978-146737557-3 (ISBN), (Journal Abbreviation: IEEE Symp. Comput. Intell. Secur. Def. Appl., CISDA - Proc.).
Résumé | Liens | BibTeX | Étiquettes: Abstracting, Abstraction techniques, Alarm systems, Anomaly detection, Anomaly detection systems, Anomaly detector, Artificial intelligence, Chemical detection, Computer programming languages, Distributed computer systems, Errors, Hidden Markov models, Host-based Anomaly Detection System, Markov processes, Signal detection, Software dependability, Software security, System call traces, Time delay, Time delay embedding, Trace analysis, Trace Analysis and Abstraction
@inproceedings{murtaza_trace_2015,
title = {A trace abstraction approach for host-based anomaly detection},
author = {S. S. Murtaza and W. Khreich and A. Hamou-Lhadj and S. Gagnon},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953310135&doi=10.1109%2fCISDA.2015.7208644&partnerID=40&md5=30e47f711b04bc6c44be9f6daea5ebf1},
doi = {10.1109/CISDA.2015.7208644},
isbn = {978-146737557-3 (ISBN)},
year = {2015},
date = {2015-01-01},
booktitle = {2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2015 - Proceedings},
pages = {170–177},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {High false alarm rates and execution times are among the key issues in host-based anomaly detection systems. In this paper, we investigate the use of trace abstraction techniques for reducing the execution time of anomaly detectors while keeping the same accuracy. The key idea is to represent system call traces as traces of kernel module interactions and use the resulting abstract traces as input to known anomaly detection techniques, such as STIDE (the Sequence Time-Delay Embedding) and HMM (Hidden Markov Models). We performed experiments on three datasets, namely, the traditional UNM dataset as well as two modern datasets, Firefox and ADFA-LD. The results show that kernel module traces can lead to similar or fewer false alarms and considerably smaller execution times compared to raw system call traces for host-based anomaly detection systems. © 2015 IEEE.},
note = {Journal Abbreviation: IEEE Symp. Comput. Intell. Secur. Def. Appl., CISDA - Proc.},
keywords = {Abstracting, Abstraction techniques, Alarm systems, Anomaly detection, Anomaly detection systems, Anomaly detector, Artificial intelligence, Chemical detection, Computer programming languages, Distributed computer systems, Errors, Hidden Markov models, Host-based Anomaly Detection System, Markov processes, Signal detection, Software dependability, Software security, System call traces, Time delay, Time delay embedding, Trace analysis, Trace Analysis and Abstraction},
pubstate = {published},
tppubtype = {inproceedings}
}
High false alarm rates and execution times are among the key issues in host-based anomaly detection systems. In this paper, we investigate the use of trace abstraction techniques for reducing the execution time of anomaly detectors while keeping the same accuracy. The key idea is to represent system call traces as traces of kernel module interactions and use the resulting abstract traces as input to known anomaly detection techniques, such as STIDE (the Sequence Time-Delay Embedding) and HMM (Hidden Markov Models). We performed experiments on three datasets, namely, the traditional UNM dataset as well as two modern datasets, Firefox and ADFA-LD. The results show that kernel module traces can lead to similar or fewer false alarms and considerably smaller execution times compared to raw system call traces for host-based anomaly detection systems. © 2015 IEEE.
8.
Davoust, A.; Wainer, G.; Esfandiari, B.
DEVS simulation of peer-to-peer file-sharing Article d'actes
Dans: Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012, p. 357–364, Madrid, 2012, ISBN: 978-146732359-8 (ISBN), (Journal Abbreviation: Proc. Int. Conf. High Perform. Comput. Simul., HPCS).
Résumé | Liens | BibTeX | Étiquettes: Coupled models, DEVS, DEVS simulation, Discrete event systems Specifications, Distributed computer systems, File Sharing, File sharing networks, Framework models, Internet protocols, Network models, Network simulation tools, Peer model, Peer to peer, Peer to peer networks, Peer-To-Peer, Real-time simulator, simulation
@inproceedings{davoust_devs_2012,
title = {DEVS simulation of peer-to-peer file-sharing},
author = {A. Davoust and G. Wainer and B. Esfandiari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866984789&doi=10.1109%2fHPCSim.2012.6266937&partnerID=40&md5=0a024e6c84c3befb05bb063b538ee71a},
doi = {10.1109/HPCSim.2012.6266937},
isbn = {978-146732359-8 (ISBN)},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the 2012 International Conference on High Performance Computing and Simulation, HPCS 2012},
pages = {357–364},
address = {Madrid},
abstract = {We present a framework to simulate a peer-to-peer (P2P) file-sharing network, based on the Discrete Event Systems Specification (DEVS) formalism. Our framework models a file-sharing network as a coupled model, comprising a network model and a large number of peer models. While most available network simulation tools focus on transportlevel dynamics, we provide extensible and reusable models for the file-sharing protocol and for the behavior of peers. These models, implemented using the CD++ toolkit, can readily be used on existing simulators, including parallel and real-time simulators. As a case study, we apply our framework to simulate a P2P web, and show the emergence of an interesting page distribution. © 2012 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. High Perform. Comput. Simul., HPCS},
keywords = {Coupled models, DEVS, DEVS simulation, Discrete event systems Specifications, Distributed computer systems, File Sharing, File sharing networks, Framework models, Internet protocols, Network models, Network simulation tools, Peer model, Peer to peer, Peer to peer networks, Peer-To-Peer, Real-time simulator, simulation},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a framework to simulate a peer-to-peer (P2P) file-sharing network, based on the Discrete Event Systems Specification (DEVS) formalism. Our framework models a file-sharing network as a coupled model, comprising a network model and a large number of peer models. While most available network simulation tools focus on transportlevel dynamics, we provide extensible and reusable models for the file-sharing protocol and for the behavior of peers. These models, implemented using the CD++ toolkit, can readily be used on existing simulators, including parallel and real-time simulators. As a case study, we apply our framework to simulate a P2P web, and show the emergence of an interesting page distribution. © 2012 IEEE.
9.
Craig, A.; Davoust, A.; Esfandiari, B.
A distributed wiki system based on peer-to-peer file sharing principles Article d'actes
Dans: Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011, p. 364–371, Lyon, 2011, ISBN: 978-076954513-4 (ISBN), (Journal Abbreviation: Proc. - IEEE/WIC/ACM Int. Conf. Web Intell., WI).
Résumé | Liens | BibTeX | Étiquettes: Collaboration models, Collaborative Editing, Distributed computer systems, Electronic document exchange, File Sharing, File sharing networks, Graph queries, Peer to peer, Peer to peer networks, Peer-To-Peer, Peer-to-peer file sharing, Search results, Semantics, System-based, Trust, User interfaces, Versioning, Wiki
@inproceedings{craig_distributed_2011,
title = {A distributed wiki system based on peer-to-peer file sharing principles},
author = {A. Craig and A. Davoust and B. Esfandiari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80155122622&doi=10.1109%2fWI-IAT.2011.231&partnerID=40&md5=ff6031acbb6d96f836879e6f2c378db3},
doi = {10.1109/WI-IAT.2011.231},
isbn = {978-076954513-4 (ISBN)},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011},
volume = {1},
pages = {364–371},
address = {Lyon},
abstract = {In peer-to-peer (P2P) file-sharing networks, each peer maintains its own repository, publishing files, downloading files from others, and making its own files available for download. We present P2Pedia, a distributed wiki system applying these principles to collaborative editing of documents: contributors may maintain their own version of each document, while accessing and reusing the contributions of others. This collaboration model, by allowing for multiple versions of a document, generates a different type of versioning hierarchy, and changes the semantics of wikilinks. We show how the versioning hierarchy of documents and the wikilinks create a graph of documents, that can be searched using an existing file-sharing infrastructure, and we propose some trust indicators to help users choose between available search results. Finally, we present the design and implementation of P2Pedia, and propose some scenarios where our proposed collaboration model is most appropriate. © 2011 IEEE.},
note = {Journal Abbreviation: Proc. - IEEE/WIC/ACM Int. Conf. Web Intell., WI},
keywords = {Collaboration models, Collaborative Editing, Distributed computer systems, Electronic document exchange, File Sharing, File sharing networks, Graph queries, Peer to peer, Peer to peer networks, Peer-To-Peer, Peer-to-peer file sharing, Search results, Semantics, System-based, Trust, User interfaces, Versioning, Wiki},
pubstate = {published},
tppubtype = {inproceedings}
}
In peer-to-peer (P2P) file-sharing networks, each peer maintains its own repository, publishing files, downloading files from others, and making its own files available for download. We present P2Pedia, a distributed wiki system applying these principles to collaborative editing of documents: contributors may maintain their own version of each document, while accessing and reusing the contributions of others. This collaboration model, by allowing for multiple versions of a document, generates a different type of versioning hierarchy, and changes the semantics of wikilinks. We show how the versioning hierarchy of documents and the wikilinks create a graph of documents, that can be searched using an existing file-sharing infrastructure, and we propose some trust indicators to help users choose between available search results. Finally, we present the design and implementation of P2Pedia, and propose some scenarios where our proposed collaboration model is most appropriate. © 2011 IEEE.
10.
Davoust, A.; Esfandiari, B.
Collaborative building, sharing and handling of graphs of documents using P2P file-sharing Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5872 LNCS, p. 888–897, 2009, ISSN: 03029743, (ISBN: 3642052894; 9783642052897 Place: Vilamoura).
Résumé | Liens | BibTeX | Étiquettes: Associative storage, Cams, Distributed computer systems, File Sharing, File sharing networks, File-sharing system, Graph queries, Internet, Peer to peer, Peer to peer networks, Peer-to-peer infrastructure, Semantic Web, Semantics
@article{davoust_collaborative_2009,
title = {Collaborative building, sharing and handling of graphs of documents using P2P file-sharing},
author = {A. Davoust and B. Esfandiari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650747536&doi=10.1007%2f978-3-642-05290-3_107&partnerID=40&md5=108722ad65a39a4289e6dd4aa6ceea7d},
doi = {10.1007/978-3-642-05290-3_107},
issn = {03029743},
year = {2009},
date = {2009-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {5872 LNCS},
pages = {888–897},
abstract = {We are interested in creating a peer-to-peer infrastructure for the collaborative creation of knowledge, with no centralized point of control. We show how documents in a P2P file-sharing network can be interlinked, using a naming scheme based on the document schema and content, rather than on the document location. The interlinked documents can be seen as a distributed graph of documents, for which we define a class of graph queries supported by our file-sharing system. © Springer-Verlag 2009.},
note = {ISBN: 3642052894; 9783642052897
Place: Vilamoura},
keywords = {Associative storage, Cams, Distributed computer systems, File Sharing, File sharing networks, File-sharing system, Graph queries, Internet, Peer to peer, Peer to peer networks, Peer-to-peer infrastructure, Semantic Web, Semantics},
pubstate = {published},
tppubtype = {article}
}
We are interested in creating a peer-to-peer infrastructure for the collaborative creation of knowledge, with no centralized point of control. We show how documents in a P2P file-sharing network can be interlinked, using a naming scheme based on the document schema and content, rather than on the document location. The interlinked documents can be seen as a distributed graph of documents, for which we define a class of graph queries supported by our file-sharing system. © Springer-Verlag 2009.