Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach
This work addresses the problem of efficient and high-performance intrusion detection for Edge computing environments, representing an incremental improvement with a novel hardware-focused method.
The paper tackles the challenge of deploying machine learning-based intrusion detection systems on resource-constrained Edge devices by proposing a systematic approach using reconfigurable hardware, specifically implementing FPGA-based dataflow processors and co-designed soft-core processors, with results showing the dataflow processor outperforms state-of-the-art methods in performance and cost-efficiency.
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.