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Journals / Firat University Journal of Experimental and Computational Engineering / 2026 / Cilt: 5 Sayı: 3

Experimental Validation of a Zero-Trust-Based Layered Cybersecurity and Hardening Framework for Smart Metering and IoT Modem Infrastructures

Akıllı Sayaç ve IoT Modem Altyapıları İçin Sıfır Güven Temelli Katmanlı Siber Güvenlik ve Sıkılaştırma Çerçevesinin Deneysel Doğrulanması

Celal Ateşşahin(DICLE UNIVERSITY)0000-0002-7375-1329
Hüseyin Acar(DICLE UNIVERSITY)0000-0001-5127-4632
Cem Haydaroglu(DİCLE ÜNİVERSİTESİ)0000-0003-0830-5530
Pages
928–956
Publication Date
2026-10-25
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These laboratory results support the feasibility and effectiveness of the proposed layered design for smart-meter and IoT-modem infrastructures under the tested conditions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Smart meters and Internet of Things (IoT) modems are now essential for remote monitoring and management in energy distribution systems, but they also widen the attack surface.
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Key Findings & Data:Seven mechanisms are integrated within a single architecture: endpoint hardware protection, secure boot, mutual authentication, network micro-segmentation, operational command verification, artificial intelligence-based anomaly detection, and privacy-preserving data aggregation.
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Conclusion & Impact:These laboratory results support the feasibility and effectiveness of the proposed layered design for smart-meter and IoT-modem infrastructures under the tested conditions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Akıllı sayaçlar · Katmanlı siber güvenlik · Sıfır güven

Abstract

Smart meters and Internet of Things (IoT) modems are now essential for remote monitoring and management in energy distribution systems, but they also widen the attack surface. Vulnerabilities span the hardware, firmware, communication, and data-management layers, and this study addresses them with a five-layer security and hardening framework based on the zero-trust model. Seven mechanisms are integrated within a single architecture: endpoint hardware protection, secure boot, mutual authentication, network micro-segmentation, operational command verification, artificial intelligence-based anomaly detection, and privacy-preserving data aggregation. The framework was evaluated in a laboratory testbed across eight scenarios: physical tampering, malicious firmware, unauthorized connection, network scanning, denial-of-service, replay attacks, low-rate stealthy network anomalies, and consumer profiling via data exposure. Cryptographic key zeroization had a mean latency of 14.2 ± 1.1 ms, unauthorized network traffic was blocked according to the configured security policies, and the SIEM alert associated with replay rejection was generated within 127.08 ms. The Long Short-Term Memory (LSTM)-based time-series model achieved 95.73% accuracy with an area under the curve (AUC) of 0.9696. For the privacy layer, anonymized load-profile data obtained from field-deployed smart meters operating within the Advanced Metering Infrastructure of Dicle Electricity Distribution Inc. were used. The MCFE hourly aggregate of the 11 complete meter profiles matched the plaintext reference with zero error in all 24 hourly slots. These laboratory results support the feasibility and effectiveness of the proposed layered design for smart-meter and IoT-modem infrastructures under the tested conditions.

Özet

Akıllı sayaçlar ve Nesnelerin İnterneti (Internet of Things, IoT) modemleri, enerji dağıtım sistemlerinde uzaktan izleme ve yönetim faaliyetlerinin temel bileşenleri hâline gelmiştir. Bununla birlikte, bu cihazların yaygınlaşması enerji dağıtım altyapılarının siber saldırılara maruz kalabileceği saldırı yüzeyini de genişletmektedir. Söz konusu güvenlik açıkları; donanım, yazılım/firmware, haberleşme ve veri yönetimi katmanları başta olmak üzere sistemin farklı bileşenlerinde ortaya çıkabilmektedir. Bu çalışmada, söz konusu tehditleri ele almak amacıyla sıfır güven (Zero Trust) modeli temelinde beş katmanlı bir güvenlik ve sistem sertleştirme (hardening) çerçevesi önerilmektedir. Önerilen mimari içerisinde yedi güvenlik mekanizması bütünleşik olarak ele alınmıştır. Bu mekanizmalar; uç nokta donanım koruması, güvenli önyükleme (Secure Boot), karşılıklı kimlik doğrulama, ağ mikro-segmentasyonu, operasyonel komut doğrulama, yapay zekâ tabanlı anomali tespiti ve mahremiyet korumalı veri toplulaştırmadan oluşmaktadır. Önerilen yaklaşım, sekiz farklı değerlendirme senaryosunu içeren bir laboratuvar test ortamında doğrulanmıştır. Test senaryoları; fiziksel kurcalama, kötü amaçlı firmware, yetkisiz bağlantı, ağ taraması, hizmet engelleme (Denial-of-Service, DoS), tekrar (replay) saldırıları, düşük hızlı gizli ağ anomalileri ve veri ifşası yoluyla tüketici profillemeyi kapsamaktadır. Deneysel sonuçlarda kriptografik anahtar sıfırlama gecikmesi 14.2 ± 1.1 ms olarak ölçülmüş, yetkisiz ağ trafiği güvenlik politikaları doğrultusunda engellenmiş ve tekrar saldırısının reddedilmesiyle ilişkili SIEM uyarısı 127.08 ms içinde üretilmiştir. Uzun Kısa-Süreli Bellek (LSTM) tabanlı zaman serisi derin öğrenme modeli %95.73 doğruluk ve 0.9696 eğri altı alan (AUC) değerine ulaşmıştır. Mahremiyet katmanının değerlendirilmesinde, Dicle Elektrik Dağıtım A.Ş. Gelişmiş Ölçüm Altyapısı (Advanced Metering Infrastructure, AMI) kapsamında işletilen saha tipi akıllı sayaçlardan elde edilen anonimleştirilmiş yük profili verileri kullanılmıştır. On bir tam sayaç profilinin Çok İstemcili Fonksiyonel Şifreleme (MCFE) tabanlı saatlik toplulaştırma sonucu, 24 zaman diliminin tamamında düz metin referansıyla sıfır hata ile eşleşmiştir. Bu laboratuvar bulguları, önerilen katmanlı tasarımın akıllı sayaç ve IoT modem altyapılarında uygulanabilirliğini ve etkinliğini test edilen koşullar altında desteklemektedir.

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Cite This Article

Ateşşahin, C., Acar, H., Haydaroglu, C. (2026). Experimental Validation of a Zero-Trust-Based Layered Cybersecurity and Hardening Framework for Smart Metering and IoT Modem Infrastructures. Firat University Journal of Experimental and Computational Engineering, Cilt: 5 Sayı: 3, 928–956. https://doi.org/10.62520/fujece.2001687

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