Research Article
Machine Learning-Driven Intrusion Detection with Forensic Readiness in Cloud-Native IoT Environments
Issue:
Volume 10, Issue 1, June 2026
Pages:
1-18
Received:
13 May 2026
Accepted:
22 May 2026
Published:
29 June 2026
Abstract: Cloud-native Internet of Things (IoT) environments combine connected devices, edge gateways, containerized services, Kubernetes orchestration, microservices, and cloud infrastructure. Although this architecture improves scalability and automation, it also expands the attack surface and complicates forensic investigation. Conventional intrusion detection systems often focus on detection accuracy but provide limited support for evidence preservation, chain-of-custody management, and incident timeline reconstruction. This study proposes and evaluates a machine learning-driven intrusion detection framework with forensic readiness for cloud-native IoT environments. The CICIoT2023 dataset was used to evaluate Logistic Regression, Decision Tree, Random Forest, XGBoost, and Autoencoder models under binary and multi-class classification settings using an 80: 20 train-test split and 5-fold cross-validation. Experimental results show that XGBoost achieved the best performance. In binary classification, it obtained 99.34% accuracy, 99.35% precision, 99.34% recall, 99.34% F1-score, and 99.89% ROC-AUC. In multi-class classification, it achieved 97.69% accuracy, 96.12% macro precision, 95.07% macro recall, 95.54% macro F1-score, and 97.65% weighted F1-score. The forensic readiness evaluation showed 96.15% Evidence Completeness Ratio, 100.00% Chain-of-Custody Completeness, 100.00% Evidence Integrity Score, 0.84-second average preservation latency, 98.72% Alert-to-Evidence Mapping Rate, 94.60% Timeline Reconstruction Success Rate, and 96.89% Investigation Readiness Index. The findings demonstrate that the proposed framework supports accurate intrusion detection and investigation-ready evidence preservation for cloud-native IoT security.
Abstract: Cloud-native Internet of Things (IoT) environments combine connected devices, edge gateways, containerized services, Kubernetes orchestration, microservices, and cloud infrastructure. Although this architecture improves scalability and automation, it also expands the attack surface and complicates forensic investigation. Conventional intrusion dete...
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Research Article
Optimal Load Frequency Control of an Isolated Multi Source Power System Using Sailfish Optimization Algorithm
Issue:
Volume 10, Issue 1, June 2026
Pages:
19-34
Received:
14 June 2026
Accepted:
22 July 2026
Published:
25 August 2026
DOI:
10.11648/j.ajece.20261001.12
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Views:
Abstract: Load Frequency Control (LFC) plays a vital role in maintaining the stability, reliability, and quality of power system operation by regulating system frequency and balancing generation with load demand. This paper investigates the LFC problem for an isolated multi-source power system comprising Thermal, Hydro, and Gas power generation units. To achieve effective frequency regulation under load disturbances, a decentralized control strategy employing three Proportional-Integral (PI) controllers is adopted. Among these, one PI controller is commonly shared by all generating units, while the remaining two controllers are independently assigned to the Thermal and Gas generating units to improve their dynamic response. The optimal tuning of the PI controller parameters is formulated as an optimization problem and solved using the Sailfish Optimization (SFO) algorithm, a recent nature-inspired metaheuristic optimization technique known for its excellent exploration and exploitation capabilities. The Integral Square Error (ISE) is selected as the objective function to minimize frequency deviations and enhance the overall dynamic performance of the power system. The effectiveness of the proposed SFO-based PI controller is evaluated through comprehensive time-domain simulations under step load perturbations. The system performance is assessed using transient response characteristics, including maximum overshoot, rise time, settling time,together with standard performance indices and eigenvalue analysis to examine system stability. The simulation results demonstrate that the proposed SFO-PI controller significantly improves frequency regulation, reduces oscillations, and achieves faster settling with lower performance index values compared with conventional PI tuning approaches and other optimization-based controllers reported in the literature. Furthermore, eigenvalue analysis confirms enhanced damping characteristics and improved stability of the proposed control scheme. The results establish that the SFO provides an effective and reliable framework for optimal controller design in isolated multi-source power systems.
Abstract: Load Frequency Control (LFC) plays a vital role in maintaining the stability, reliability, and quality of power system operation by regulating system frequency and balancing generation with load demand. This paper investigates the LFC problem for an isolated multi-source power system comprising Thermal, Hydro, and Gas power generation units. To ach...
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,
Hilary Onyeka Okonkwo
