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What is Autoencoding Neural Network

Chapter 139
Encyclopedia of Data Science and Machine Learning
A form of unsupervised machine learning that utilizes multiple layers within a neural network to first encode and then later decode the attributes of information about a dataset, for the purpose of learning which attributes are significant features. This feature extraction process is often referred to as a self-supervised process. The autoencoding neural network is then able to produce an anomaly score for each observation of data using a reconstructed mean square of the error (RMSE) score, with higher RMSE scores denoting increasing difficulty in reproducing the observation in the decoding layer of the neural network. Thus, higher RMSE scores generally denote a greater potential anomaly in the multivariate dataset observation.
Published in Chapter:
Malware Detection in Network Flows With Self-Supervised Deep Learning
Thomas Alan Woolman (On Target Technologies, Inc., USA) and Philip Lunsford (East Carolina University, USA)
Copyright: © 2023 |Pages: 18
DOI: 10.4018/978-1-7998-9220-5.ch139
Abstract
This article explores the application of anomaly detection models from network flow data using multi-layer perceptron autoencoding neural networks, for the purpose of self-supervised detection of novel network intrusion events and malware classes over unrestrained internet connections. The authors utilized network flows rather than more detailed (and larger) packet capture logs in order to create a more cost-effective and potentially faster anomaly detection tool that could more easily scale enterprise class network traffic analysis. Unsupervised/self-supervised deep learning anomaly detection was used against this less-granular dataset to maximize the likelihood of detecting novel network activities within the less-detailed dataset without relying on pre-defined rules and training data. The authors conclude with a test of statistical significance against known threat classes (unknown to the anomaly detection model) that the proposed methodology results were statistically significant for detecting threat classes in unrestrained internet networks using network flow data.
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