EVENT

JFLI seminar on "AI-enhanced Intrusion Detection" by Kandaraj Piamrat

We are pleased to inform you about the upcoming seminar by Kandaraj Piamrat, Associate Professor at Nantes University and member of the JFLI, titled : "AI-enhanced Intrusion Detection System for Cloud-Edge-IoT continuum" Everyone interested is cordially invited to attend!

Title:

AI-enhanced Intrusion Detection System for Cloud-Edge-IoT continuum

Abstract:

Abstract: The rapid proliferation of Internet of Things (IoT) devices with AI capabilities and the transition to distributed computing environments necessitate advanced intrusion detection systems (IDS) to safeguard the new paradigm known as Cloud-Edge-IoT (CEI) continuum. The first part of the talk, we introduce a novel approach, integrating Hierarchical Federated Learning (HFL) with Spiking Neural Networks (SNN) to propose a robust and energy-efficient IDS for this continuum. HFL, with its hierarchical learning strategy, keeps data where they are generated, thus preserving user privacy and reducing communication overhead through its combination of decentralized and centralized architecture. On the other hand, SNN, inspired by human neural mechanisms, offers significant computational efficiency. Our proposed IDS combines these strengths, facilitating localized and energy-efficient detection at the edge and IoT layers while enabling global model aggregation and updates at the cloud layer. Through extensive experiments using one of the most recent datasets (Edge-IIoTset), we demonstrate that our approach not only detects attacks with high accuracy but also substantially reduces energy consumption across the continuum. The second part of the talk, we dive into a specific case of distributed learning within decentralized and distributed (P2P) system. One variant of FL called semi-decentralized FL (SDFL) enables multiple servers to coordinate the learning task instead of relying on only one central server, hence preventing single-point failures. However, SDFL requires careful consideration regarding the coordination between server nodes, and dealing with the heterogeneous computing resources and data distributions across end devices (FL clients). To handle this, we propose TUNE-FL, an adapTive semi-synchronoUs semi-deceNtralizEd Federated Learning that addresses the clients' heterogeneity challenges. TUNE-FL alleviates these challenges by (i) ensuring consensus regardless of the network topology, and (ii) deploying an adaptive semi-synchronous mechanism for coordinating the learning process across all nodes while taking into consideration the heterogeneity presented by end devices. The talk concludes with ongoing works and envisioned deployment in smart healthcare use case.

Speaker Bio:

Kandaraj Piamrat , Associate Professor at Nantes University

Time/Date:

10:30-12:00 / Wednesday, November. 20 , 2024

Place:

Room 1512, NII

Contact:

If you would like to join, please contact by email.
Email : boudin[at]nii.ac.jp

Link:

Japanese-French Laboratory for Informatics