GRU Enabled Intrusion Detection System for IoT Environment with Swarm Optimization and Gaussian Random Forest Classification
Source abstract
Abstract
In recent years, machine learning (ML) and deep learning (DL) have significantly advanced intrusion detection systems, effectively addressing potential malicious attacks across networks. This paper introduces a robust method ... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (32)
Show source-provided references
1. TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system10.1109/ACCESS.2019.2928048 ↗
2. Network intrusion detection system: A systematic study of machine learning and deep learning approaches
3. Performance analysis of machine learning algorithms in intrusion detection system: A review10.1016/j.procs.2020.04.133 ↗
4. Machine learning-based lightweight intrusion detection system for the internet of things
5. Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system10.1109/ACCESS.2018.2858277 ↗
6. Intrusion prediction using LSTM and GRU with UNSW-NB15
7. Network intrusion detection based on supervised adversarial variational auto-encoder with regularization10.1109/ACCESS.2020.2977007 ↗
8. A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer10.1016/j.eswa.2020.113249 ↗
9. Cuckoo search algorithm and BF Tree used for anomaly detection in data mining
10. A smart anomaly-based intrusion detection system for the internet of things (IoT) network using GWO–PSO–RF model10.1007/s40860-020-00126-x ↗
11. Introduced a new method for enhancement of intrusion detection with random forest and PSO algorithm10.1002/spy2.147 ↗
12. NBC-MAIDS: Naïve Bayesian classification technique in multi-agent system-enriched IDS for securing IoT against DDoS attacks10.1007/s11227-018-2413-7 ↗
13. Building auto-encoder intrusion detection system based on random forest feature selection10.1016/j.cose.2020.101851 ↗
14. A hybrid intrusion detection system based on scalable k-means+ random forest and deep learning10.1109/ACCESS.2021.3082147 ↗
15. An adaptive ensemble machine learning model for intrusion detection10.1109/ACCESS.2019.2923640 ↗
16. Deep learning approach for intelligent intrusion detection system10.1109/ACCESS.2019.2895334 ↗
17. GRU-based deep learning approach for network intrusion alert prediction10.1016/j.future.2021.09.040 ↗
18. Features extraction for network intrusion detection using genetic algorithm (GA)
19. An enhanced intrusion detection system using particle swarm optimization feature extraction technique10.1016/j.procs.2021.10.052 ↗
20. Particle swarm optimization and feature selection for intrusion detection system10.1007/s12046-020-1308-5 ↗
21. An efficient anomaly intrusion detection method with feature selection and evolutionary neural network10.1109/ACCESS.2020.2986217 ↗
22. Distributed variational bayes-based in-network security for the internet of things10.1109/JIOT.2020.3041656 ↗
23. Network intrusion detection system based experimental study of combined classifiers using random forest classifiers for feature selection
24. An effective way of cloud intrusion detection system using decision tree, support vector machine and Naïve Bayes algorithm
25. Real-time anomaly detection based on long short-term memory and Gaussian Mixture Model10.1016/j.compeleceng.2019.106458 ↗
26. A review on machine learning and deep learning perspectives of IDS for IoT: Recent updates, security issues, and challenges10.1007/s11831-020-09496-0 ↗
27. BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset10.1109/ACCESS.2020.2972627 ↗
28. Intrusion detection approach based on optimised artificial neural network10.1016/j.neucom.2020.07.138 ↗
29. DL-IDS: A deep learning-based intrusion detection framework for securing IoT
30. An efficient XGBoost–DNN-based classification model for network intrusion detection system10.1007/s00521-020-04708-x ↗
31. Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks10.1007/s12652-020-02228-z ↗
32. Feature engineering based performance analysis of ML and DL algorithms for Botnet attack detection in IoMT10.1007/s13198-023-01883-7 ↗