| Title: |
RISC-V Embedded AI for IDS Applications |
| Authors: |
Garreau, Pierre; Cotret, Pascal; Francq, Julien; Cexus, Jean-Christophe; Lagadec, Loïc |
| Contributors: |
Equipe Hardware ARchitectures and CAD tools (Lab-STICC_ARCAD); Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC); École Nationale d'Ingénieurs de Brest (ENIB); Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Brest (UBO EPE)-Institut National Polytechnique de Bretagne (Bretagne INP)-Université de Bretagne Sud (UBS)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique); Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB); Institut Mines-Télécom Paris (IMT); Ecole Navale (EN); Chaire de Cyber Défense des Systèmes Navals Brest; Institut de Recherche de l'Ecole Navale (IRENAV); Arts et Métiers Sciences et Technologies-Arts et Métiers Sciences et Technologies; École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne); Naval Group; Equipe PIM (Lab-STICC_PIM); Chaire de Cyberdéfense des Systèmes Navals |
| Source: |
RESSI 2024 : Rendez-vous de la Recherche et de l'Enseignement de la Sécurité des Systèmes d'Information ; https://hal.science/hal-04498047 ; RESSI 2024 : Rendez-vous de la Recherche et de l'Enseignement de la Sécurité des Systèmes d'Information, May 2024, Eppe-Sauvage, France ; https://ressi2024.sciencesconf.org/ |
| Publisher Information: |
CCSD |
| Publication Year: |
2024 |
| Collection: |
Université de Bretagne Occidentale: HAL |
| Subject Terms: |
[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR] |
| Subject Geographic: |
Eppe-Sauvage; France |
| Description: |
National audience ; IDSs (Intrusion Detection Systems) include more and more AI (Artificial Intelligence) engines to detect several attack types. However, in order to be efficient in both learning and inference phases, such systems must include hardware coprocessors to improve AI-related computations. In this PhD thesis, we would like to explore the capabilities of RISC-V based processors in this context. RISC-V is an open-source ISA (Instruction Set Architecture) than can be easily extended. The main goal of this thesis is to propose RISC-V extensions for an IDS embedded into collaborative and heterogeneous unmanned vehicles (submarine, marine, or aerial): it must detect abnormal behaviors and must be efficient in terms of power consumption, area and runtime overheads. Furthermore, coprocessors developed in this thesis should not introduce security breaches into the system. Finally, a proof-of-concept should be developed to demonstrate the efficiency of algorithms and hardware implementations compared to software solutions. |
| Document Type: |
conference object |
| Language: |
English |
| Availability: |
https://hal.science/hal-04498047; https://hal.science/hal-04498047v2/document; https://hal.science/hal-04498047v2/file/bare_conf.pdf |
| Rights: |
https://about.hal.science/hal-authorisation-v1/ ; info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.1D30C462 |
| Database: |
BASE |