DL2Fence: integrating deep learning and frame fusion for enhanced detection and localization of refined denial-of-service in large-scale NoCs
DL2Fence: integrating deep learning and frame fusion for enhanced detection and localization of refined denial-of-service in large-scale NoCs
This study introduces a refined Flooding Injection Rate-adjustable Denial-of-Service (DoS) model for Network-on-Chips (NoCs) and more importantly presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for DoS detection and localization. Two Convolutional Neural Networks models for classification and segmentation were developed to detect and localize DoS respectively. It achieves detection and localization accuracies of 95.8% and 91.7%, and precision rates of 98.5% and 99.3% in a 16x16 mesh NoC. The framework’s hardware overhead notably decreases by 76.3% when scaling from 8x8 to 16x16 NoCs, and it requires 42.4% less hardware compared to state-of-the-arts. This advancement demonstrates DL2Fence’s effectiveness in balancing outstanding detection performance in large-scale NoCs with extremely low hardware overhead.
NoC, Machine Learning, Deep Learning, Dos
Wang, Haoyu
3d04a266-1db2-42a6-9a4d-052c33c43873
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Ren, Jianjie
f6667eb7-ee16-49f6-bede-5f805809d6a1
Atamli, Ahmad
dacf7d9e-9898-4385-bf88-5aec14d76872
20 June 2024
Wang, Haoyu
3d04a266-1db2-42a6-9a4d-052c33c43873
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Ren, Jianjie
f6667eb7-ee16-49f6-bede-5f805809d6a1
Atamli, Ahmad
dacf7d9e-9898-4385-bf88-5aec14d76872
Wang, Haoyu, Halak, Basel, Ren, Jianjie and Atamli, Ahmad
(2024)
DL2Fence: integrating deep learning and frame fusion for enhanced detection and localization of refined denial-of-service in large-scale NoCs.
Design Automation Conference 2024, San Francisco, san francisco, United States.
17 - 21 Jun 2024.
6 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
This study introduces a refined Flooding Injection Rate-adjustable Denial-of-Service (DoS) model for Network-on-Chips (NoCs) and more importantly presents DL2Fence, a novel framework utilizing Deep Learning (DL) and Frame Fusion (2F) for DoS detection and localization. Two Convolutional Neural Networks models for classification and segmentation were developed to detect and localize DoS respectively. It achieves detection and localization accuracies of 95.8% and 91.7%, and precision rates of 98.5% and 99.3% in a 16x16 mesh NoC. The framework’s hardware overhead notably decreases by 76.3% when scaling from 8x8 to 16x16 NoCs, and it requires 42.4% less hardware compared to state-of-the-arts. This advancement demonstrates DL2Fence’s effectiveness in balancing outstanding detection performance in large-scale NoCs with extremely low hardware overhead.
Text
2403.13563v2
- Version of Record
Restricted to Repository staff only
Request a copy
More information
Published date: 20 June 2024
Venue - Dates:
Design Automation Conference 2024, San Francisco, san francisco, United States, 2024-06-17 - 2024-06-21
Keywords:
NoC, Machine Learning, Deep Learning, Dos
Identifiers
Local EPrints ID: 492844
URI: http://eprints.soton.ac.uk/id/eprint/492844
PURE UUID: 4084abf4-fc11-4442-8f1c-7f56bbdef70f
Catalogue record
Date deposited: 15 Aug 2024 16:58
Last modified: 16 Aug 2024 01:45
Export record
Contributors
Author:
Haoyu Wang
Author:
Basel Halak
Author:
Jianjie Ren
Author:
Ahmad Atamli
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics