Enhanced Anomaly Detection in IoT Networks Using Deep Autoencoders with Feature Selection Techniques

An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people’s lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the n...

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Published in:	Sensors (Basel, Switzerland) Vol. 25; no. 10; p. 3150
Main Authors:	Rhachi, Hamza, Balboul, Younes, Bouayad, Anas
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 16.05.2025 MDPI
Subjects:	Algorithms anomaly detection Artificial intelligence Comparative analysis Cybersecurity data pre-processing Data security dataset Datasets deep autoencoder Deep learning Energy consumption Feature selection Internet of Things Literature reviews Machine learning Neural networks Research methodology Wireless networks Morocco internet of things data pre-processing anomaly detection deep autoencoder classification dataset feature selection
ISSN:	1424-8220, 1424-8220
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Abstract	An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people’s lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the network that interconnected IoT devices uses advanced communications norms and technologies to capture and transmit data. Still, these networks are subject to various types of attacks that will lead to the loss of user data. Concurrently, the field of anomaly detection for the Internet of Things (IoT) is experiencing rapid expansion. This expansion requires a thorough analysis of application trends and existing gaps. Furthermore, it is critical in detecting interesting phenomena such as device damage and unknown events. However, this task is tough due to the unpredictable nature of anomalies and the complexity of the environment. This paper offers a technique that uses an autoencoder neural network to identify anomalous network communications in IoT networks. More specifically, we propose and implement a model that uses DAE (deep autoencoder) to detect and classify the network data, with an ANOVA F-Test for the feature selection. The proposed model is validated using the NSL-KDD dataset. Compared to some IoT-based anomaly detection models, the experimental results reveal that the suggested model is more efficient at enhancing the accuracy of detecting malicious data. The simulation results show that it works better, with an overall accuracy rate of 85% and 92% successively for the binary and multi-class classifications.
AbstractList	An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people’s lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the network that interconnected IoT devices uses advanced communications norms and technologies to capture and transmit data. Still, these networks are subject to various types of attacks that will lead to the loss of user data. Concurrently, the field of anomaly detection for the Internet of Things (IoT) is experiencing rapid expansion. This expansion requires a thorough analysis of application trends and existing gaps. Furthermore, it is critical in detecting interesting phenomena such as device damage and unknown events. However, this task is tough due to the unpredictable nature of anomalies and the complexity of the environment. This paper offers a technique that uses an autoencoder neural network to identify anomalous network communications in IoT networks. More specifically, we propose and implement a model that uses DAE (deep autoencoder) to detect and classify the network data, with an ANOVA F-Test for the feature selection. The proposed model is validated using the NSL-KDD dataset. Compared to some IoT-based anomaly detection models, the experimental results reveal that the suggested model is more efficient at enhancing the accuracy of detecting malicious data. The simulation results show that it works better, with an overall accuracy rate of 85% and 92% successively for the binary and multi-class classifications. An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people's lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the network that interconnected IoT devices uses advanced communications norms and technologies to capture and transmit data. Still, these networks are subject to various types of attacks that will lead to the loss of user data. Concurrently, the field of anomaly detection for the Internet of Things (IoT) is experiencing rapid expansion. This expansion requires a thorough analysis of application trends and existing gaps. Furthermore, it is critical in detecting interesting phenomena such as device damage and unknown events. However, this task is tough due to the unpredictable nature of anomalies and the complexity of the environment. This paper offers a technique that uses an autoencoder neural network to identify anomalous network communications in IoT networks. More specifically, we propose and implement a model that uses DAE (deep autoencoder) to detect and classify the network data, with an ANOVA F-Test for the feature selection. The proposed model is validated using the NSL-KDD dataset. Compared to some IoT-based anomaly detection models, the experimental results reveal that the suggested model is more efficient at enhancing the accuracy of detecting malicious data. The simulation results show that it works better, with an overall accuracy rate of 85% and 92% successively for the binary and multi-class classifications.An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people's lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the network that interconnected IoT devices uses advanced communications norms and technologies to capture and transmit data. Still, these networks are subject to various types of attacks that will lead to the loss of user data. Concurrently, the field of anomaly detection for the Internet of Things (IoT) is experiencing rapid expansion. This expansion requires a thorough analysis of application trends and existing gaps. Furthermore, it is critical in detecting interesting phenomena such as device damage and unknown events. However, this task is tough due to the unpredictable nature of anomalies and the complexity of the environment. This paper offers a technique that uses an autoencoder neural network to identify anomalous network communications in IoT networks. More specifically, we propose and implement a model that uses DAE (deep autoencoder) to detect and classify the network data, with an ANOVA F-Test for the feature selection. The proposed model is validated using the NSL-KDD dataset. Compared to some IoT-based anomaly detection models, the experimental results reveal that the suggested model is more efficient at enhancing the accuracy of detecting malicious data. The simulation results show that it works better, with an overall accuracy rate of 85% and 92% successively for the binary and multi-class classifications.
Audience	Academic
Author	Rhachi, Hamza Bouayad, Anas Balboul, Younes
AuthorAffiliation	IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez 30050, Morocco; younes.balboul@usmba.ac.ma (Y.B.); anas.bouayad@usmba.ac.ma (A.B.)
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Cites_doi	10.47760/ijcsmc.2020.v09i10.012 10.1109/ACCESS.2021.3094024 10.1016/j.neucom.2019.11.016 10.3390/electronics10161876 10.3390/app11157050 10.1016/j.adhoc.2020.102177 10.1109/TEMSMET51618.2020.9557464 10.1007/978-981-15-6876-3_17 10.1007/s41870-022-01115-4 10.18280/isi.250503 10.1145/1541880.1541882 10.1109/SPW.2018.00013 10.1016/j.iot.2019.100059 10.1007/s13369-024-08951-5 10.1109/ICOEI48184.2020.9142921 10.1109/ACCESS.2022.3176317 10.1109/ACCESS.2021.3116612 10.1109/CCWC57344.2023.10099056 10.1109/ICAECT49130.2021.9392483 10.1109/ACCESS.2021.3132127 10.20944/preprints202408.0945.v1
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References	Xu (ref_15) 2021; 9 Ieracitano (ref_17) 2020; 387 Ullah (ref_4) 2021; 9 Gurung (ref_23) 2019; 11 Goyal (ref_1) 2023; 23 Sivasubramanian (ref_18) 2024; 49 ref_14 ref_13 ref_24 ref_11 ref_22 Chandola (ref_2) 2009; 41 Sarkar (ref_19) 2023; 15 Hasan (ref_6) 2019; 7 Thamaraiselvi (ref_10) 2020; 9 ref_3 Huang (ref_21) 2020; 105 ref_16 ref_9 ref_8 Ullah (ref_12) 2021; 9 Ullah (ref_5) 2022; 10 Fenanir (ref_20) 2020; 25 ref_7
References_xml	– volume: 9 start-page: 95 year: 2020 ident: ref_10 article-title: Attack and anomaly detection in iot networks using machine learning publication-title: Int. J. Comput. Sci. Mob. Comput. doi: 10.47760/ijcsmc.2020.v09i10.012 – volume: 9 start-page: 123456 year: 2021 ident: ref_12 article-title: Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3094024 – ident: ref_9 – volume: 387 start-page: 51 year: 2020 ident: ref_17 article-title: A novel statistical analysis and autoencoder driven intelligent intrusion detection approach publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.11.016 – ident: ref_13 doi: 10.3390/electronics10161876 – ident: ref_8 doi: 10.3390/app11157050 – volume: 105 start-page: 102177 year: 2020 ident: ref_21 article-title: IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks publication-title: Ad. Hoc. Netw. doi: 10.1016/j.adhoc.2020.102177 – ident: ref_11 doi: 10.1109/TEMSMET51618.2020.9557464 – ident: ref_24 doi: 10.1007/978-981-15-6876-3_17 – volume: 15 start-page: 423 year: 2023 ident: ref_19 article-title: A supervised machine learning-based solution for efficient network intrusion detection using ensemble learning based on hyperparameter optimization publication-title: Int. J. Inf. Tecnol. doi: 10.1007/s41870-022-01115-4 – volume: 25 start-page: 569 year: 2020 ident: ref_20 article-title: A semi-supervised deep auto-encoder based intrusion detection for IoT publication-title: Ingénierie Des Systèmes D’information doi: 10.18280/isi.250503 – volume: 11 start-page: 8 year: 2019 ident: ref_23 article-title: Deep Learning Approach on Network Intrusion Detection System using NSL-KDD Dataset publication-title: Int. J. Comput. Netw. Inf. Secur. (IJCNIS) – volume: 41 start-page: 1 year: 2009 ident: ref_2 article-title: Anomaly detection: A survey publication-title: ACM Comput. Surv. doi: 10.1145/1541880.1541882 – ident: ref_3 doi: 10.1109/SPW.2018.00013 – volume: 7 start-page: 100059 year: 2019 ident: ref_6 article-title: Attack and anomaly detection in iot sensors in iot sites using machine learning approaches publication-title: Internet Things doi: 10.1016/j.iot.2019.100059 – volume: 49 start-page: 13061 year: 2024 ident: ref_18 article-title: Feature Extraction and Anomaly Detection Using Different Autoencoders for Modeling Intrusion Detection Systems publication-title: Arab. J. Sci. Eng. doi: 10.1007/s13369-024-08951-5 – ident: ref_7 doi: 10.1109/ICOEI48184.2020.9142921 – volume: 10 start-page: 62722 year: 2022 ident: ref_5 article-title: Design and development of rnn anomaly detection model for iot networks publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3176317 – volume: 9 start-page: 140136 year: 2021 ident: ref_15 article-title: Improving Performance of Autoencoder Based Network Anomaly Detection on NSL-KDD Dataset publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3116612 – ident: ref_22 doi: 10.1109/CCWC57344.2023.10099056 – ident: ref_16 doi: 10.1109/ICAECT49130.2021.9392483 – volume: 9 start-page: 165907 year: 2021 ident: ref_4 article-title: A framework for anomaly detection in iot networks using conditional generative adversarial networks publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3132127 – ident: ref_14 doi: 10.20944/preprints202408.0945.v1 – volume: 23 start-page: 100 year: 2023 ident: ref_1 article-title: A review on IoT: Layered architecture, security issues and protocols publication-title: Int. J. Comput. Sci. Netw. Secur. (IJCSNS)
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SubjectTerms	Algorithms anomaly detection Artificial intelligence Comparative analysis Cybersecurity data pre-processing Data security dataset Datasets deep autoencoder Deep learning Energy consumption Feature selection Internet of Things Literature reviews Machine learning Neural networks Research methodology Wireless networks
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