research-article

Deep Learning for IoT Intrusion Detection based on LSTMs-AE

Authors:

Qiang YangAuthors Info & Claims

AIAM2020: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture

October 2020

Pages 64 - 68

https://doi.org/10.1145/3421766.3421891

Published: 26 October 2020 Publication History

Abstract

With the advent of 5G era, The Internet of Things (IoT) is obtaining considerable attention in all walks of life nowadays. However, due to the hardware problems of devices, there may exists some security problems in IOT. While existing intrusion detection methods rarely consider the time series feature of the data. In this paper, we propose an anomaly monitoring model for Autoencoder based on Long-Short Term Memory (LSTMs-AE), in which LTSM is exploited to capture time-series features and the intrusion detection is performed by the feature learning ability of Autoencoder. Thorough experiments demonstrate that our model has better intrusion detection performance than ordinary Autoencoder, as in most of the dataset the accuracy rate of proposed scheme exceeds 0.95.

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Cited By

Otokwala UPetrovski AKalutarage H(2024)Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of thingsInternational Journal of Information Security10.1007/s10207-024-00855-7Online publication date: 30-Apr-2024
https://doi.org/10.1007/s10207-024-00855-7
Abdalgawad NSajun AKaddoura YZualkernan IAloul F(2022)Generative Deep Learning to Detect Cyberattacks for the IoT-23 DatasetIEEE Access10.1109/ACCESS.2021.314001510(6430-6441)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3140015
Kimbugwe NPei TKyebambe M(2021)Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A ReviewEnergies10.3390/en1419638414:19(6384)Online publication date: 6-Oct-2021
https://doi.org/10.3390/en14196384
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Index Terms

Deep Learning for IoT Intrusion Detection based on LSTMs-AE
1. Computing methodologies
  1. Concurrent computing methodologies
    1. Concurrent algorithms

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AIAM2020: Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture

October 2020

566 pages

ISBN:9781450375535

DOI:10.1145/3421766

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 26 October 2020

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AIAM2020

AIAM2020: 2nd International Conference on Artificial Intelligence and Advanced Manufacture

October 15 - 17, 2020

Manchester, United Kingdom

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Cited By

Otokwala UPetrovski AKalutarage H(2024)Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of thingsInternational Journal of Information Security10.1007/s10207-024-00855-7Online publication date: 30-Apr-2024
https://doi.org/10.1007/s10207-024-00855-7
Abdalgawad NSajun AKaddoura YZualkernan IAloul F(2022)Generative Deep Learning to Detect Cyberattacks for the IoT-23 DatasetIEEE Access10.1109/ACCESS.2021.314001510(6430-6441)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2021.3140015
Kimbugwe NPei TKyebambe M(2021)Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A ReviewEnergies10.3390/en1419638414:19(6384)Online publication date: 6-Oct-2021
https://doi.org/10.3390/en14196384
Pan YZhang XJiang HLi C(2021)A Network Traffic Classification Method Based on Graph Convolution and LSTMIEEE Access10.1109/ACCESS.2021.31281819(158261-158272)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3128181

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