Anomaly detection is essential in cybersecurity for identifying abnormal activities, a requirement that has grown increasingly critical with the complexity of cyberthreats. This study leverages the BPF-Extended Tracking Honeypot (BETH) dataset, a comprehensive resource designed to benchmark robustness in detecting anomalous behavior in kernel-level process and network logs. The symmetry of the proposed system lies in its ability to identify balanced and consistent patterns within kernel-level process logs, which form the foundation for accurately distinguishing anomalies. This study focuses on anomaly detection in kernel-level process logs by introducing an enhanced Isolation Forest (iForest) model, which is integrated into a structured framework that includes exploratory data analysis (EDA), data pre-processing, model training, validation, and evaluation. The proposed approach achieves a significant performance improvement in the anomaly detection results, with an area under the receiver operating characteristic curve (AUROC) score of 0.917—an approximate 7.88% increase over the baseline model’s AUROC of 0.850. Additionally, the model demonstrates high precision (99.57%), F1-score (91.69%), and accuracy (86.03%), effectively minimizing false positives while maintaining balanced detection capabilities. These results underscore the role of leveraging symmetry in designing advanced intrusion detection systems, offering a structured and efficient solution for identifying cyberthreats.

The rapid expansion of the Internet of Things (IoT) has led to billions of interconnected devices generating and exchanging sensitive data across diverse domains, which introduces challenges in identity management (IdM) regarding privacy, scalability, and verifiability. While blockchain technology provides decentralization and tamper resistance, its transparency and increasing on-chain storage demands make it unsuitable for large-scale IoT identity ecosystems. To overcome these challenges, IoT-SBIdM is proposed as a lightweight, privacy-preserving, and stateless blockchain-based identity management framework designed for IoT environments. This framework incorporates Elliptic Curve Cryptography (ECC)-based accumulators and Zero-Knowledge Proofs (ZKPs) to facilitate selective disclosure, enabling entities to prove credential authenticity without exposing sensitive identity information. Furthermore, the framework adopts W3C-compliant Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) to promote interoperability and user-controlled identity ownership. The experimental results indicate that IoT-SBIdM achieves efficient smart contract execution by reducing gas costs through optimized registry logic. Moreover, the system maintains a compact block size of only 45 MB at higher block heights, outperforming comparable schemes in storage efficiency by achieving a 55% reduction relative to recent models and an approximate 94% reduction relative to older systems, thereby demonstrating superior scalability and storage efficiency, making it suitable for identity management solutions for IoT environments.