Poster: Robust Edge-Based Detection of Bot Attacks Through Federated Learning

This work investigates the application of federated learning for detecting web bots in edge computing settings. The key challenge lies in developing machine learning models that are not only accurate but also robust against evasion attacks, where adversarial actors attempt to bypass detection. Addition-ally, the models must be privacy-preserving to protect sensitive information, ensuring that confidential data is not exposed to third parties during the learning process. By leveraging federated learning, the proposed approach enables collaborative model training across distributed edge nodes without sharing raw data, maintaining user privacy while enhancing detection capabilities against sophisticated web bot attacks.