Explainable federated learning for IOT security systems: Balancing privacy, transparency, and performance
Explaınable federated learnıng for ıot securıty systems: balancıng prıvacy, transparency, and performance
- Tez No: 1005541
- Danışmanlar: YRD. DOÇ. DR. AHMED ŞENOL
- Tez Türü: Yüksek Lisans
- Konular: Bilim ve Teknoloji, Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Science and Technology, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2025
- Dil: İngilizce
- Üniversite: Üsküdar Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Siber Güvenlik Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
There is an accelerating trend for governments to adopt digital transformation, which is clearly reflected in the growing presence of Internet of Things (IoT) deployments worldwide. These technologies have significantly enhanced the quality and performance of various services. However, this rapid increase in the adoption of the IoT has also raised significant security and privacy concerns, necessitating the development of new technological solutions to secure IoT systems. In these solutions, Intrusion Detection Systems (IDSs) play a crucial role. AI-powered IDSs have been developed as effective real-time intrusion detection and response solutions. However, the dependence on classical centralized systems would be questionable in terms of user privacy and transparency, as such models often act as“black boxes.”To address these issues, we propose and evaluate a conceptual framework, XFL-IoT, which combines Federated Learning (FL) for decentralized model training with data privacy and Explainable AI (XAI) to provide comprehensible explanations of the decisions to the user. The working of FL to train the machine-learning model for multi-class intrusion detection is through the Edge-IIoTset dataset. Post-hoc interpretability is provided via Shapley Additive explanations (SHAP) to explain model predictions on a per-instance basis. The results demonstrate that the combined XFL-IoT system can offer a high level of predictive performance when providing interpretable local and global explanations for model decisions. Ultimately, while this XFL-IoT paradigm offers an exciting framework for secure and transparent IoT security, it requires careful navigation and balancing the (latent) trade-offs that are embedded in the underlying tension between data privacy and model transparency, and ultimately model predictiveness. This work highlights the importance of resolving this trade-off for the successful implementation of the systems.
Özet (Çeviri)
There is an accelerating trend for governments to adopt digital transformation, which is clearly reflected in the growing presence of Internet of Things (IoT) deployments worldwide. These technologies have significantly enhanced the quality and performance of various services. However, this rapid increase in the adoption of the IoT has also raised significant security and privacy concerns, necessitating the development of new technological solutions to secure IoT systems. In these solutions, Intrusion Detection Systems (IDSs) play a crucial role. AI-powered IDSs have been developed as effective real-time intrusion detection and response solutions. However, the dependence on classical centralized systems would be questionable in terms of user privacy and transparency, as such models often act as“black boxes.”To address these issues, we propose and evaluate a conceptual framework, XFL-IoT, which combines Federated Learning (FL) for decentralized model training with data privacy and Explainable AI (XAI) to provide comprehensible explanations of the decisions to the user. The working of FL to train the machine-learning model for multi-class intrusion detection is through the Edge-IIoTset dataset. Post-hoc interpretability is provided via Shapley Additive explanations (SHAP) to explain model predictions on a per-instance basis. The results demonstrate that the combined XFL-IoT system can offer a high level of predictive performance when providing interpretable local and global explanations for model decisions. Ultimately, while this XFL-IoT paradigm offers an exciting framework for secure and transparent IoT security, it requires careful navigation and balancing the (latent) trade-offs that are embedded in the underlying tension between data privacy and model transparency, and ultimately model predictiveness. This work highlights the importance of resolving this trade-off for the successful implementation of the systems.
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