Anomaly detection is referred to a process in which the aim is to detect data points that follow a different pattern from the majority of data points. With the rapid development of computer technology, protecting networks from various threats such as network intruders is becoming crucial. Traditional anomaly detection methods suffer from several well-known challenges that hinder their performance, such as the curse of high dimensionality. Autoencoders are unsupervised neural networks that have been used for the purpose of reducing dimensionality and also detecting network anomalies in large datasets. The performance of autoencoders debilitates when the training set contains noise and anomalies. In this paper, a new gradient-reversal method is proposed to overcome the influence of anomalies on the training phase for the purpose of detecting network anomalies. The method is different from other approaches as it does not require an anomaly-free training set and is based on reconstruction error. Once latent variables are extracted from the network, local outlier factor is used to separate normal data points from anomalies. A simple pruning approach and data augmentation are also added to improve performance further. The experimental results show that the proposed model can outperform other well-known approaches.
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Babaei, K., Chen, Z.Y. & Maul, T. AEGR: a simple approach to gradient reversal in autoencoders for network anomaly detection. Soft Comput 25, 15269–15280 (2021). https://doi.org/10.1007/s00500-021-06110-8
- Network anomaly detection
- High dimensionality
- Autoencoders (AEs)
- Local outlier factor (LOF)
- Gradient reversal