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Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach

Wadid Foudhaili, Anouar Nechi, Celine Thermann, Mohammad Al Johmani, Rainer Buchty, Mladen Berekovic, Saleh Mulhem

TL;DR

The paper tackles the challenge of deploying high-performance ML-based intrusion detection at the edge by proposing a systematic methodology for reconfigurable edge hardware. It evaluates two FPGA-based configurations—a dataflow processor (DFP) and a RISC-V soft-core processor (SCP)—alongside a thorough hardware/software performance framework. Using BOT-IoT for intelligent IDS construction, the study demonstrates that the FPGA-based DFP delivers substantially higher throughput while maintaining favorable energy efficiency and logic density compared to SCP and state-of-the-art approaches. The results validate that high-speed, edge-appropriate IDS can be achieved with reconfigurable hardware at reasonable cost, enabling robust defense for contemporary high-speed networks.

Abstract

Intrusion detection systems (IDS) are crucial security measures nowadays to enforce network security. Their task is to detect anomalies in network communication and identify, if not thwart, possibly malicious behavior. Recently, machine learning has been deployed to construct intelligent IDS. This approach, however, is quite challenging particularly in distributed, highly dynamic, yet resource-constrained systems like Edge setups. In this paper, we tackle this issue from multiple angles by analyzing the concept of intelligent IDS (I-IDS) while addressing the specific requirements of Edge devices with a special focus on reconfigurability. Then, we introduce a systematic approach to constructing the I-IDS on reconfigurable Edge hardware. For this, we implemented our proposed IDS on state-of-the-art Field Programmable Gate Arrays (FPGAs) technology as (1) a purely FPGA-based dataflow processor (DFP) and (2) a co-designed approach featuring RISC-V soft-core as FPGA-based soft-core processor (SCP). We complete our paper with a comparison of the state of the art (SoA) in this domain. The results show that DFP and SCP are both suitable for Edge applications from hardware resource and energy efficiency perspectives. Our proposed DFP solution clearly outperforms the SoA and demonstrates that required high performance can be achieved without prohibitively high hardware costs. This makes our proposed DFP suitable for Edge-based high-speed applications like modern communication technology.

Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach

TL;DR

The paper tackles the challenge of deploying high-performance ML-based intrusion detection at the edge by proposing a systematic methodology for reconfigurable edge hardware. It evaluates two FPGA-based configurations—a dataflow processor (DFP) and a RISC-V soft-core processor (SCP)—alongside a thorough hardware/software performance framework. Using BOT-IoT for intelligent IDS construction, the study demonstrates that the FPGA-based DFP delivers substantially higher throughput while maintaining favorable energy efficiency and logic density compared to SCP and state-of-the-art approaches. The results validate that high-speed, edge-appropriate IDS can be achieved with reconfigurable hardware at reasonable cost, enabling robust defense for contemporary high-speed networks.

Abstract

Intrusion detection systems (IDS) are crucial security measures nowadays to enforce network security. Their task is to detect anomalies in network communication and identify, if not thwart, possibly malicious behavior. Recently, machine learning has been deployed to construct intelligent IDS. This approach, however, is quite challenging particularly in distributed, highly dynamic, yet resource-constrained systems like Edge setups. In this paper, we tackle this issue from multiple angles by analyzing the concept of intelligent IDS (I-IDS) while addressing the specific requirements of Edge devices with a special focus on reconfigurability. Then, we introduce a systematic approach to constructing the I-IDS on reconfigurable Edge hardware. For this, we implemented our proposed IDS on state-of-the-art Field Programmable Gate Arrays (FPGAs) technology as (1) a purely FPGA-based dataflow processor (DFP) and (2) a co-designed approach featuring RISC-V soft-core as FPGA-based soft-core processor (SCP). We complete our paper with a comparison of the state of the art (SoA) in this domain. The results show that DFP and SCP are both suitable for Edge applications from hardware resource and energy efficiency perspectives. Our proposed DFP solution clearly outperforms the SoA and demonstrates that required high performance can be achieved without prohibitively high hardware costs. This makes our proposed DFP suitable for Edge-based high-speed applications like modern communication technology.
Paper Structure (23 sections, 4 figures, 5 tables)

This paper contains 23 sections, 4 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Machine Learning-based IDS on the Edge
  3. Paper Contribution
  4. Related Work
  5. FPGA-based Dataflow Processor for ML-based IDS
  6. FPGA-based Soft-Core Processor for ML-based IDS
  7. Reconfigurable Edge Hardware for IDS: Pros & Cons
  8. Performance Evaluation Criteria for IDS on the Edge
  9. IDS Algorithm Evaluation Criteria
  10. Reconfigurable Hardware Evaluation Criteria
  11. Proposed IDS Design Methodology
  12. Step 1: Intelligent IDS
  13. The BOT-IoT Dataset
  14. Intelligent IDS Construction
  15. Step 2: Intelligent IDS Selection
  16. ...and 8 more sections

Figures (4)

  • Figure 1: (a) Conventional IDS vs (b) Distributed IDS on the Edge
  • Figure 2: Attack categories and subcategories distribution of the BOT-IoT dataset
  • Figure 3: IDS Experimental Setup RISC-V Soft-core and the FPGA-based DFP.
  • Figure 4: Hardware Utilization of (a) our 3 MLPs on FPGA and (b) FPGA-based RISC-V SCP vs. Overall MLPs as FPGA-based DFP.