Kullanıcı Tüketim Kalıpları Aracılığıyla Akıllı Şebekelerdeki Enerji Hırsızlığını Tespit Etmek İçin Etkili Bir DNN Tabanlı Yaklaşım

Year 2023, Volume: 12 Issue: 4, 163 - 170, 28.12.2023

Muhammed Zekeriya Gündüz Resul Daş

https://doi.org/10.46810/tdfd.1383065

Abstract

İnternetin ilerlemesi insan hayatını giderek kolaylaştırmaktadır. Mobil iletişim teknolojilerinin gelişmesi, Nesnelerin İnterneti (Internet of Things-IoT) uygulamalarının yaygın olarak benimsenmesine yol açmıştır. Böylece, çoğu sistem ve cihaz internete daha verimli bir şekilde bağlanmıştır. İletişim sistemlerinin elektrik şebekeleri gibi kritik altyapılara entegre edilmesi, IoT tabanlı akıllı şebekeler kavramını ortaya çıkarmıştır. Akıllı şebeke sistemlerinde veri iletişimi, Gelişmiş Ölçüm Altyapısı (Advanced Metering Infrastructure - AMI) aracılığıyla sağlanmaktadır. İletişim sistemlerinin doğal özellikleri nedeniyle, AMI siber saldırılara karşı savunmasız olabilir. Bazı güvenlik açıkları, akıllı sayaçlardan elde edilen enerji tüketim verilerine karşı siber saldırı vektörlerinin ortaya çıkmasına neden olmuştur. Bu çalışmada, kullanıcıların tüketim modellerine dayalı etkili bir enerji hırsızlığı saldırı tespit sistemi önerilmektedir. Hem dürüst hem de kötü niyetli tüketim kalıplarının tahmin edilebilirliğini değerlendirmek için Derin Sinir Ağı (Deep Neural Network - DNN) tabanlı bir sınıflandırma modeli kullanılmıştır. Önerilen model yüksek ve ayarlanabilir performans sergilemektedir. Yaklaşık 2000 müşteriden oluşan gerçek bir tüketim veri kümesi üzerinde kapsamlı deneyler gerçekleştirilmiştir. Veri kümesine iki farklı saldırı vektörü ile gerçek okumalardan elde edilen manipüle edilmiş veriler enjekte edilmiştir. K-katlı çapraz-doğrulama tekniği kullanılmıştır. Önerilen model %97,4 doğruluğa ulaşarak yüksek bir performans göstermiştir.

Keywords

AMI, DNN, Siber güvenlik, Enerji hırsızlığı, Akıllı şebeke güvenliği

An effective DNN-based Approach for Detecting Energy Theft in Smart Grids through User Consumption Patterns

Abstract

The advancement of the Internet has been progressively easing human life. The development of mobile communication technologies has led to the widespread adoption of Internet of Things (IoT) applications. Thus, most systems and devices have connected to the Internet more efficiently. The integration of communication systems into critical infrastructures, such as electricity grids, has given rise to the concept of IoT-based smart grids. In smart grid systems, data communication is facilitated through the Advanced Metering Infrastructure (AMI). Due to the inherent characteristics of communication systems, AMI may be vulnerable to cyber-attacks. Some vulnerabilities have resulted in the emergence of cyber-attack vectors against energy consumption data obtained from smart meters. In this study, an effective energy theft intrusion detection system (IDS) based on users' consumption patterns is proposed. A Deep Neural Network (DNN) based classification model was employed to assess the predictability of both honest and malicious consumption patterns. The proposed model exhibits high and adjustable performance. Extensive experiments have been carried out on a real consumption dataset of approximately 2000 customers. Manipulated data from real readings with two different attack vectors were injected into the dataset. K-fold cross-validation technique was used. The proposed model performed a high performance reaching up to 97.4% accuracy.

Keywords

AMI, DNN, Cyber security, Energy theft, Smart grid security

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