@inproceedings{10f5db2a12cd48e3b75da2c21cc3c340,
title = "Path Optimization Using DQN in the SCION Internet Architecture",
abstract = "Path-aware Internet architectures such as SCION expose multiple end-to-end paths to endhosts. However, optimal path selection in dynamic network conditions remains challenging. This paper presents a Deep-Q-Network approach for intelligent path selection in SCION networks. We formulate SCION path selection as a reinforcement learning problem and train a lightweight Deep-Q-Network agent that observes latency, loss, and bandwidth metrics and outputs the optimal path. We evaluate our approach in an simulated SCION environment with realistic time-varying traffic conditions. The DQN agent consistently matches the performance of oracle-based selection methods with full network visibility, while reducing probing overhead by 95\%. These results demonstrate the potential of reinforcement learning to effectively leverage the path diversity and control offered by next-generation Internet architectures.",
keywords = "DQN, Deep Reinforcement Learning, Path Optimization, Path Selection, SCION",
author = "Oran Bourak and Tomer Burman and Tony John and Hadassa Daltrophe and Tammar Shrot and David Hausheer",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 9th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2025 ; Conference date: 04-12-2025 Through 05-12-2025",
year = "2026",
month = jan,
day = "1",
doi = "10.1007/978-3-032-10759-6\_12",
language = "English",
isbn = "9783032107589",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "192--206",
editor = "Adi Akavia and Shlomi Dolev and Anna Lysyanskaya and Rami Puzis",
booktitle = "Cyber Security, Cryptology, and Machine Learning - 9th International Symposium, CSCML 2025, Proceedings",
address = "Germany",
}