arXiv subjects

Muhammet Anil Yagiz

Publications and source records attributed to Muhammet Anil Yagiz.

5 recordsLinked to original sources

arXiv2026

Lightweight Tamper-Evident Log Integrity Verification for IoT Edge Environments: A Merkle Tree Pipeline with Adaptive Chunking

Integrity of audit logs produced by Internet of Things (IoT) devices is a prerequisite for post-incident forensics, regulatory compliance, and operational accountability. While blockchain-backed logging infrastructures can satisfy this requirement, they introduce consensus overhead, network dependencies, and deployment complexity that are often prohibitive at the IoT edge. This paper presents a lightweight and evaluated integrity verification pipeline that combines Merkle-tree commitments with resource-aware adaptive chunking to provide tamper evidence without relying on distributed ledger technologies. The proposed pipeline operates in three stages: (i) resource-aware batch ingestion via adaptive chunk sizing, (ii) Merkle-tree construction with O(logn) inclusion proof generation, and (iii) deterministic single-entry verification against a trusted root anchor. We further report an implementation audit that identified and corrected two evaluation defects: a double-counting bug in tampering metrics and a redundant full-tree reconstruction during batch appends. Using the corrected implementation, five-run benchmarks on synthetic IoT log datasets demonstrate throughput exceeding 130,000 logs/s for 100,000 records. The system achieves per-entry verification latency of approximately 22 ms, proof generation latency of 22 ms, an average proof size of 1,006 bytes, and peak memory usage below 5 MB. Tampering detection achieves perfect precision, recall, and F1-score (1.0) across corruption ratios ranging from 1% to 50%.

cs.CR↗

arXiv2025

MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems

The rapid expansion of immersive Metaverse applications introduces complex challenges at the intersection of performance, privacy, and environmental sustainability. Centralized architectures fall short in addressing these demands, often resulting in elevated energy consumption, latency, and privacy concerns. This paper proposes MetaFed, a decentralized federated learning (FL) framework that enables sustainable and intelligent resource orchestration for Metaverse environments. MetaFed integrates (i) multi-agent reinforcement learning for dynamic client selection, (ii) privacy-preserving FL using homomorphic encryption, and (iii) carbon-aware scheduling aligned with renewable energy availability. Evaluations on MNIST and CIFAR-10 using lightweight ResNet architectures demonstrate that MetaFed achieves up to 25% reduction in carbon emissions compared to conventional approaches, while maintaining high accuracy and minimal communication overhead. These results highlight MetaFed as a scalable solution for building environmentally responsible and privacy-compliant Metaverse infrastructures.

cs.LG↗

arXiv2025

LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity

The rapid proliferation of Industrial Internet of Things (IIoT) systems necessitates advanced, interpretable, and scalable intrusion detection systems (IDS) to combat emerging cyber threats. Traditional IDS face challenges such as high computational demands, limited explainability, and inflexibility against evolving attack patterns. To address these limitations, this study introduces the Lightweight Explainable Network Security framework (LENS-XAI), which combines robust intrusion detection with enhanced interpretability and scalability. LENS-XAI integrates knowledge distillation, variational autoencoder models, and attribution-based explainability techniques to achieve high detection accuracy and transparency in decision-making. By leveraging a training set comprising 10% of the available data, the framework optimizes computational efficiency without sacrificing performance. Experimental evaluation on four benchmark datasets: Edge-IIoTset, UKM-IDS20, CTU-13, and NSL-KDD, demonstrates the framework's superior performance, achieving detection accuracies of 95.34%, 99.92%, 98.42%, and 99.34%, respectively. Additionally, the framework excels in reducing false positives and adapting to complex attack scenarios, outperforming existing state-of-the-art methods. Key strengths of LENS-XAI include its lightweight design, suitable for resource-constrained environments, and its scalability across diverse IIoT and cybersecurity contexts. Moreover, the explainability module enhances trust and transparency, critical for practical deployment in dynamic and sensitive applications. This research contributes significantly to advancing IDS by addressing computational efficiency, feature interpretability, and real-world applicability. Future work could focus on extending the framework to ensemble AI systems for distributed environments, further enhancing its robustness and adaptability.

cs.CR↗

arXiv2024

Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI

In the evolving landscape of autonomous vehicles, ensuring robust in-vehicle network (IVN) security is paramount. This paper introduces an advanced intrusion detection system (IDS) called KD-XVAE that uses a Variational Autoencoder (VAE)-based knowledge distillation approach to enhance both performance and efficiency. Our model significantly reduces complexity, operating with just 1669 parameters and achieving an inference time of 0.3 ms per batch, making it highly suitable for resource-constrained automotive environments. Evaluations in the HCRL Car-Hacking dataset demonstrate exceptional capabilities, attaining perfect scores (Recall, Precision, F1 Score of 100%, and FNR of 0%) under multiple attack types, including DoS, Fuzzing, Gear Spoofing, and RPM Spoofing. Comparative analysis on the CICIoV2024 dataset further underscores its superiority over traditional machine learning models, achieving perfect detection metrics. We furthermore integrate Explainable AI (XAI) techniques to ensure transparency in the model's decisions. The VAE compresses the original feature space into a latent space, on which the distilled model is trained. SHAP(SHapley Additive exPlanations) values provide insights into the importance of each latent dimension, mapped back to original features for intuitive understanding. Our paper advances the field by integrating state-of-the-art techniques, addressing critical challenges in the deployment of efficient, trustworthy, and reliable IDSes for autonomous vehicles, ensuring enhanced protection against emerging cyber threats.

cs.CR↗

arXiv2024

Comprehensive Machine Learning Model Comparison for Cherenkov and Scintillation Light Separation due to Particle Interactions

The demand for novel detector mediums such as Water-based Liquid Scintillator (WbLS) has increased over the last few decades due to their capability for both low energy particle interactions and higher light yield. Recently, the usage of machine learning (ML) methods in high-energy physics has also been increasing. The ML and AI methods are used in many physics projects in the field since they provide effective and sensitive results. In this study, we aimed to develop a comprehensive analysis of water Cherenkov detectors and perform physics analyses to efficiently separate Cherenkov and scintillation photons with ML algorithms using the data from the WbLS detector environment. The main goal of this study was to produce more precise solutions to physics problems, such as signal classification, by applying ML techniques to the simulation and experimental data. Here, we trained more than 20 ML models, and our results revealed that three machine learning models, XGBoost, Light GBM, and Random Forest models, and their ensemble model gave us more than 95\% accuracy for separating Cherenkov and scintillation photons with balanced and unbalanced datasets. This is a significant increase in efficiency as compared with the results of the classical method by applying simple time cuts.

hep-ex↗