A lightweight deep autoencoder-based approach for unsupervised anomaly detection

Gcinizwe Dlamini, Rufina Galieva, Muhammad Fahim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Unsupervised anomaly detection is an important area of research to find abnormal behavior and integral part of many systems. In this research, a lightweight deep autoencoder based approach is presented to detect anomalies in unsupervised manner. It has the ability to learn the model over the normal patterns and any deviation is considered as an anomaly. Consequently, it can relax the condition to have anomalous data patterns during the training phase of the model. In this work, we examine lightweight autoencoder for anomaly detection task in order to show that simple architecture can show good performance in terms of training, testing time, number of parameters and metrics. We apply autoencoder for binary classification problem (i.e., each data point considered either normal or abnormal). The reconstruction error is used to detect anomalies. The experiments are carried out over the particular class of cyber security domain known as intrusion detection systems. We evaluated our model on standard publicly available benchmarks of KDD-99, NSL-KDD and UNSW-NB15 and achieved F1-score of 0.96, 0.88 and 0.95, respectively. It outperforms by a considerable margin when compared to state-of-the-art methods.

Original languageEnglish
Title of host publication16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728150529
DOIs
Publication statusPublished - Nov 2019
Externally publishedYes
Event16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019 - Abu Dhabi, United Arab Emirates
Duration: 03 Nov 201907 Nov 2019

Publication series

NameProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volume2019-November
ISSN (Print)2161-5322
ISSN (Electronic)2161-5330

Conference

Conference16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period03/11/201907/11/2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Intrusion Detection System
  • KDD-99
  • Novelty Detection
  • NSL-KDD
  • Outlier detection
  • UNSW-NB15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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