In recent years, many methods for intrusion detection systems (IDS) have been designed and developed in the research community, which have achieved a perfect detection rate using IDS datasets. Deep neural networks (DNNs) are representative examples applied widely in IDS. However, DNN models are becoming increasingly complex in model architectures with high resource computing in hardware requirements. In addition, it is difficult for humans to obtain explanations behind the decisions made by these DNN models using large IoT-based IDS datasets. Many proposed IDS methods have not been applied in practical deployments, because of the lack of explanation given to cybersecurity experts, to support them in terms of optimizing their decisions according to the judgments of the IDS models. This paper aims to enhance the attack detection performance of IDS with big IoT-based IDS datasets as well as provide explanations of machine learning (ML) model predictions. The proposed ML-based IDS method is based on the ensemble trees approach, including decision tree (DT) and random forest (RF) classifiers which do not require high computing resources for training models. In addition, two big datasets are used for the experimental evaluation of the proposed method, NF-BoT-IoT-v2, and NF-ToN-IoT-v2 (new versions of the original BoT-IoT and ToN-IoT datasets), through the feature set of the net flow meter. In addition, the IoTDS20 dataset is used for experiments. Furthermore, the SHapley additive exPlanations (SHAP) is applied to the eXplainable AI (XAI) methodology to explain and interpret the classification decisions of DT and RF models; this is not only effective in interpreting the final decision of the ensemble tree approach but also supports cybersecurity experts in quickly optimizing and evaluating the correctness of their judgments based on the explanations of the results.

Memory isolation is an essential technology for safeguarding the resources of lightweight embedded systems. This technique isolates system resources by constraining the scope of the processor’s accessible memory into distinct units known as domains. Despite the security offered by this approach, the Memory Protection Unit (MPU), the most common memory isolation method provided in most lightweight systems, incurs overheads during domain switching due to the privilege level intervention. However, as IoT environments become increasingly interconnected and more resources become required for protection, the significant overhead associated with domain switching under this constraint is expected to be crucial, making it harder to operate with more granular domains. To mitigate these issues, we propose DEMIX, which supports efficient memory isolation for multiple domains. DEMIX comprises two mainelements—Domain-Enforced Memory Isolation and instruction-level domain isolation—with the primary idea of enabling granular access control for memory by validating the domain state of the processor and the executed instructions. By achieving fine-grained validation of memory regions, our technique safely extends the supported domain capabilities of existing technologies while eliminating the overhead associated with switching between domains. Our implementation of eight user domains shows that our approach yields a hardware overhead of a slight 8% in Ibex Core, a very lightweight RISC-V processor.

Purpose:The final aim of operation for anorectal malformations (ARMs) is acquisition of normal bowel habit by preserving an anorectal function. This study was performed to assess the functional results after definite correction of the malformations.