Feature Extraction using LSTM Autoencoder in Network Intrusion Detection System

Ketepalli, Gayatri; Premamayudu, Bulla

doi:10.1109/icces54183.2022.9835788

2022 7th International Conference on Communication and Electronics Systems (ICCES) 2022

DOI: 10.1109/icces54183.2022.9835788

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Feature Extraction using LSTM Autoencoder in Network Intrusion Detection System

Gayatri Ketepalli

Bulla Premamayudu

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Cited by 4 publications

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Securing Fog-enabled IoT: federated learning and generative adversarial networks for intrusion detection

Lei

2025

Telecommun Syst

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Securing Fog-enabled IoT: federated learning and generative adversarial networks for intrusion detection

Lei

2025

Telecommun Syst

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Intrusion detection in cluster‐based wireless sensor networks: Current issues, opportunities and future research directions

John,

Isnin,

Hamid Hussain Madni

et al. 2024

IET Wireless Sensor Systems

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Wireless sensor network (WSN) cluster‐based architecture is a system designed to control and monitor specific events or phenomena remotely, and one of the important concerns that need quick attention is security risks such as an intrusion in WSN traffic. At the same time, a high‐level security method may refer to an intrusion detection system|intrusion detection systems (IDS), which may be employed effectively to achieve a higher level of security in detecting an intruder attack or any attack initiated within a WSN system. The significance of the detection of network intrusions on heterogeneous cluster‐based sensor networks with wireless connections, as well as the approaches to machine learning utilised in IDS model development, were discussed. In addition, this research conducted several comparative studies of feature selection techniques and machine learning methodologies in the development of intrusion detection systems. The authors used a bibliometric indicator to identify the leading trends when it comes to IDS, and the VOS viewer was used to create a spatial mapping of co‐authorship, co‐occurrence, and citation types of analysis with their respective units of study. The purpose of this research paper is to generate relevant findings and a research problem formulation that can lead to a research gap in the research topic's domain area.

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An Ensemble-Based Machine Learning-Envisioned Intrusion Detection in Industry 5.0-Driven Healthcare Applications

Wazid,

Singh,

Das

et al. 2024

IEEE Trans. Consumer Electron.

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Feature Extraction using LSTM Autoencoder in Network Intrusion Detection System

Cited by 4 publications

References 0 publications

Securing Fog-enabled IoT: federated learning and generative adversarial networks for intrusion detection

Securing Fog-enabled IoT: federated learning and generative adversarial networks for intrusion detection

Intrusion detection in cluster‐based wireless sensor networks: Current issues, opportunities and future research directions

An Ensemble-Based Machine Learning-Envisioned Intrusion Detection in Industry 5.0-Driven Healthcare Applications

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