Comparative Analysis of Classical and Quantum Machine Learning Approaches for Anomaly Detection in IoT Security IoT Güvenliginde Anomali Tespiti için Klasik ve Kuantum Makine Ögrenmesi Yaklasimlarinin Karsilastirmali Analizi
34th Signal Processing and Communications Applications Conference, SIU 2026, İstanbul, Türkiye, 7 - 10 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu71813.2026.11636712
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: anomaly detection, hybrid architecture, intrusion detection, IoT security, NISQ, quantum machine learning
- Gazi Üniversitesi Adresli: Evet
Özet
The increase in security threats within Internet of Things (IoT) environments challenges the limits of traditional security methods. This study reviews recent literature focused on IoT intrusion detection, providing a comparative analysis of classical machine learning and Quantum Machine Learning (QML) approaches. Within this scope, attack types, datasets, preprocessing steps, QML architectures, and the strengths and weaknesses of both approaches are evaluated. Findings indicate that both approaches offer complementary advantages under different conditions; while QML stands out particularly in lowdata and zero-day scenarios, hybrid architectures gain prominence in practice due to the limitations of current quantum hardware.