Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach

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.

Practical Trustworthiness Model for DNN in Dedicated 6G Application

Artificial intelligence (AI) is considered an efficient response to several challenges facing 6G technology. However, AI still suffers from a huge trust issue due to its ambiguous way of making predictions. Therefore, there is a need for a method to evaluate the AI's trustworthiness in practice for future 6G applications. This paper presents a practical model to analyze the trustworthiness of AI in a dedicated 6G application. In particular, we present two customized Deep Neural Networks (DNNs) to solve the Automatic Modulation Recognition (AMR) problem in Terahertz communications-based 6G technology. Then, a specific trustworthiness model and its attributes, namely data robustness, parameter sensitivity, and security covering adversarial examples, are introduced. The evaluation results indicate that the proposed trustworthiness attributes are crucial to evaluate the trustworthiness of DNN for this 6G application.