Kolloquiumsvortrag: 17. Juni 2025, Sushmetha Arumugam (Al Sardy/Muhammad)
AI-Based Anomaly Detection to Enhance Cybersecurity in IoT Networks
The rapid increase of IoT devices has introduced unique challenges in ensuring network security. The heterogeneity, resource constraints, and dynamic communication protocols make these environments particularly vulnerable to cyberattacks. Intrusion Detection Systems (IDS) supported with machine learning techniques have shown promise, but often suffer from high false positives and limited adaptability. This thesis focuses on developing an anomaly detection system utilising Long Short-Term Memory (LSTM) networks and ensemble learning to enhance the robustness and accuracy of IDS in IoT environments.
Zeit: 10:15 Uhr
Ort: Raum 04.137, Martensstr. 3, Erlangen
oder
Zoom-Meeting beitreten:
https://fau.zoom-x.de/j/68350702053?pwd=UkF3aXY0QUdjeSsyR0tyRWtLQ0hYUT09
Meeting-ID: 683 5070 2053
Kenncode: 647333