AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection

TL;DR

This paper introduces AEGR, a novel gradient-reversal technique for autoencoders that enhances network anomaly detection by reducing noise influence without needing an anomaly-free training set, and combines it with LOF and data augmentation.

Contribution

The paper presents a new gradient-reversal method for autoencoders that improves anomaly detection without requiring clean training data, and integrates LOF and data augmentation for better accuracy.

Findings

Outperforms existing anomaly detection methods in experiments.

Effective in noisy training environments without needing anomaly-free data.

Combines autoencoder reconstruction with LOF for improved detection.

Abstract

Anomaly detection is referred to as a process in which the aim is to detect data points that follow a different pattern from the majority of data points. Anomaly detection methods suffer from several well-known challenges that hinder their performance such as 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 is also added to further improve performance. The experimental results show that the proposed model can outperform other well-know approaches.