Jinsul Kim

Ibrahim Aliyu, Selinde van Engelenburg, Muhammed Bashir Muazu et al.

The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models created this way are still vulnerable to evasion, poisoning, and exploratory attacks using adversarial examples. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We proposed integrating a statistical detector to detect and extract unknown adversarial samples. By including the unknown detected samples into the dataset of the detector, we augment the BFF-IDS with an additional model to detect original known attacks and the new adversarial inputs. The statistical adversarial detector confidently detected adversarial examples at the sample size of 50 and 100 input samples. Furthermore, the augmented BFF-IDS (BFF-IDS(AUG)) successfully mitigates the adversarial examples with more than 96% accuracy. With this approach, the model will continue to be augmented in a sandbox whenever an adversarial sample is detected and subsequently adopt the BFF-IDS(AUG) as the active security model. Consequently, the proposed integration of the statistical adversarial detector and the subsequent augmentation of the BFF-IDS with detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks.

Abubakar Isah, Ibrahim Aliyu, Sulaiman Muhammad Rashid et al.

Graph neural networks are gaining attention in fifth-generation (5G) core network digital twins, which are data-driven complex systems with numerous components. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classification in multiclass settings. Digital twins of 5G networks increasingly employ graph classification as the main method for identifying failure types. However, the skewed distribution of failure occurrences is a significant class-imbalance problem that prevents practical graph data mining. Previous studies have not sufficiently addressed this complex problem. This paper, proposes class-Fourier GNN (CF-GNN) that introduces a class-oriented spectral filtering mechanism to ensure precise classification by estimating a unique spectral filter for each class. This work employs eigenvalue and eigenvector spectral filtering to capture and adapt to variations in minority classes, ensuring accurate class-specific feature discrimination, and adept at graph representation learning for complex local structures among neighbors in an end-to-end setting. The extensive experiments demonstrate that the proposed CF-GNN could help create new techniques for enhancing classifiers and investigate the characteristics of the multiclass imbalanced data in a network digital twin system.

Ibrahim Aliyu, Awwal M. Arigi, Tai-Won Um et al.

This paper addresses the challenge of representing complex human action (HA) in a nuclear power plant (NPP) digital twin (DT) and minimizing latency in partial computation offloading (PCO) in sixth-generation-enabled computing in the network (COIN) assisted multiaccess edge computing (MEC). Accurate HA representation in the DT-HA model is vital for modeling human interventions that are crucial for the safe and efficient operation of NPPs. In this context, DT-enabled COIN-assisted MEC harnesses DT (known as a cybertwin) capabilities to optimize resource allocation and reduce latency effectively. A two-stage approach is employed to address system complexity. First, a probabilistic graphical model (PGM) is introduced to capture HAs in the DT abstraction. In the PGM, HA and NPP asset-twin abstractions form coupled systems that evolve and interact through observable data and control input. Next, the underlying PCO problem is formulated as a multiuser game, where NPP assets can partially offload tasks to COIN and MEC. We propose a decentralized algorithm to optimize offloading decisions, offloading ratios, and resource allocation. The simulation results demonstrate the effectiveness of the proposed method in capturing complex HAs and optimal resource allocation in DT-enabled NPPs.