In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previously observed behaviors. Motivated by this challenge, we propose a neural network-based method for anomaly detection that does not rely on (1) prior knowledge of anomaly distributions or (2) the availability of real anomalies during training. Our proposed method trains a neural network classifier using only normal samples, combining the supervision from synthetic anomalies, and is particularly suitable when collecting real anomaly samples is expensive or impractical. The trained classifier is proven to attain minimax excess risk, and more importantly, it is guaranteed to learn the boundary of the normal region. Once the normal region is well estimated, the model can detect a wide range of anomalies without requiring explicit modeling of their distributions. Extensive experiments across cybersecurity, industrial, and medical anomaly detection tasks demonstrate that our method is consistently robust and competitive compared to state-of-the-art baselines. Notably, in the context of network intrusion detection, our approach significantly enhances the detection of difficult and previously unseen cyberattacks compared to other baselines.

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