Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection-Reference-Cited by-同舟云学术

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Hybrid Optimization Enabled Robust CNN-LSTM Technique for Network Intrusion Detection

Published:2022 Issue: Volume:10 Page:65611-65622
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Deore Bhushan¹^ORCID,Bhosale Surendra¹

Affiliation:

1. Department of Electrical Engineering, Veermata Jijabai Technological Institute, Mumbai, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09796521.pdf?arnumber=9796521

Reference25 articles.

1. Flow-based anomaly intrusion detection system using two neural network stages;yousef;Comput Sci Inf Syst,2014

2. Impact of supervised classifier on speech emotion recognition;anita;Multimed Res,2019

3. Intrusion detection by machine learning: A review

4. Race Detection using Mutated Salp Swarm Optimization Algorithm based DBN from Face Shape Features

5. Firefly algorithm based feature selection for network intrusion detection;selvakumar;Comput Secur,2019

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2. An intrusion detection algorithm based on joint symmetric uncertainty and hyperparameter optimized fusion neural network;Expert Systems with Applications;2024-06

3. Enhancing Water treatment predictions: a Machine Learning Approach with CNN and Water Wave optimization;Asian Journal of Civil Engineering;2024-05-28

4. Network Intrusion Detection with Feature Elimination and Selection Using Deep Learning;2024 International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS);2024-04-22

5. Intrusion detection systems for IoT based on bio-inspired and machine learning techniques: a systematic review of the literature;Cluster Computing;2024-04-14

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