论文标题
通过联邦学习的异常检测
Anomaly Detection via Federated Learning
论文作者
论文摘要
机器学习通过将分类器和自动编码器纳入正常行为和异常行为来帮助推进异常检测领域。此外，联邦学习为全球模型提供了一种使用多个客户数据培训的方法，而无需客户直接共享其数据。本文通过联合学习提出了一种新颖的异常检测器，以检测客户服务器上的恶意网络活动。在我们的实验中，我们在联合学习框架中使用具有分类器的自动编码器来确定网络活动是良性还是恶意。通过使用我们新颖的Min-Max标量和采样技术（称为FEDSAM），我们确定了联合学习使全球模型可以从每个客户的数据中学习，进而为每个客户提供了一种改善其入侵检测系统对网络攻击的防御的手段。
Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior. Additionally, federated learning has provided a way for a global model to be trained with multiple clients' data without requiring the client to directly share their data. This paper proposes a novel anomaly detector via federated learning to detect malicious network activity on a client's server. In our experiments, we use an autoencoder with a classifier in a federated learning framework to determine if the network activity is benign or malicious. By using our novel min-max scalar and sampling technique, called FedSam, we determined federated learning allows the global model to learn from each client's data and, in turn, provide a means for each client to improve their intrusion detection system's defense against cyber-attacks.
