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Successful intrusion detection with a single deep autoencoder: theory and practice.

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Title: Successful intrusion detection with a single deep autoencoder: theory and practice.
Authors: Catillo, Marta, Pecchia, Antonio, Villano, Umberto
Source: Software Quality Journal; Mar2024, Vol. 32 Issue 1, p95-123, 29p
Subject Terms: Intrusion detection systems (Computer security), Pattern recognition systems, Computer security, Feature selection, Machine learning, Theory-practice relationship
Abstract: Intrusion detection is a key topic in computer security. Due to the ever-increasing number of network attacks, several accurate anomaly-based techniques have been proposed for intrusion detection, wherein pattern recognition through machine learning techniques is typically used. Many proposals rely on the use of autoencoders, due to their capability to analyze complex, high-dimensional, and large-scale data. They capitalize on composite architectures and accurate learning approaches, possibly in combination with sophisticated feature selection techniques. However, due to their high complexity and lack of transferability of the impressive intrusion detection results, they are hardly ever used in production environments. This paper is developed around the intuition that complexity is not necessarily justified because a single autoencoder is enough to obtain similar, if not better, intrusion detection results compared to related proposals. The wide study presented here addresses the effect of the seed, a deep investigation on the training loss, and feature selection across the use of different hardware platforms. The best practices presented, regarding set-up and training, threshold setting, and possible use of feature selection techniques for performance improvement, can be valuable for any future work on the use of autoencoders for successful intrusion detection purposes. [ABSTRACT FROM AUTHOR]
Copyright of Software Quality Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  – Url: https://dx.doi.org/doi:10.1007/s11219-023-09636-2
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  Data: Successful intrusion detection with a single deep autoencoder: theory and practice.
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  Data: Software Quality Journal; Mar2024, Vol. 32 Issue 1, p95-123, 29p
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  Data: <searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+security%22">Computer security</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Theory-practice+relationship%22">Theory-practice relationship</searchLink>
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  Data: Intrusion detection is a key topic in computer security. Due to the ever-increasing number of network attacks, several accurate anomaly-based techniques have been proposed for intrusion detection, wherein pattern recognition through machine learning techniques is typically used. Many proposals rely on the use of autoencoders, due to their capability to analyze complex, high-dimensional, and large-scale data. They capitalize on composite architectures and accurate learning approaches, possibly in combination with sophisticated feature selection techniques. However, due to their high complexity and lack of transferability of the impressive intrusion detection results, they are hardly ever used in production environments. This paper is developed around the intuition that complexity is not necessarily justified because a single autoencoder is enough to obtain similar, if not better, intrusion detection results compared to related proposals. The wide study presented here addresses the effect of the seed, a deep investigation on the training loss, and feature selection across the use of different hardware platforms. The best practices presented, regarding set-up and training, threshold setting, and possible use of feature selection techniques for performance improvement, can be valuable for any future work on the use of autoencoders for successful intrusion detection purposes. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Software Quality Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2024
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