Deep Learning-Based Hybrid Intelligent Intrusion Detection System
Source abstract
Abstract
Machine learning (ML) algorithms are often used to design effective intrusion detection (ID) systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels. However, cyber... | 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 · Advanced Malware Detection Techniques · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (67)
Show source-provided references
1. An intrusion detection system using a deep neural network with gated recurrent units10.1109/ACCESS.2018.2867564 ↗
2. A review of android malware detection approaches based on machine learning10.1109/ACCESS.2020.3006143 ↗
3. Toward developing efficient Conv-AE-based intrusion detection system using the heterogeneous dataset10.3390/electronics9111771 ↗
4. An effective intrusion detection classifier using long short-term memory with gradient descent optimization
5. A fast learning algorithm for deep belief nets10.1162/neco.2006.18.7.1527 ↗
6. Cyber intrusion detection using machine learning classification techniques
7. The AI-based cyber threat landscape: A survey10.1145/3372823 ↗
8. Deep Fed: Federated deep learning for intrusion detection in industrial cyber-physical systems
9. Deep learning for cybersecurity intrusion detection approaches datasets and comparative study
10. Interactive three-dimensional visualization of network intrusion detection data for machine learning10.1016/j.future.2019.07.045 ↗
11. A GA-LR wrapper approach for feature selection in network intrusion detection10.1016/j.cose.2017.06.005 ↗
12. The evaluation of network anomaly detection systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set
13. A survey on intrusion detection systems and classification techniques
14. Random forest modeling for network intrusion detection system10.1016/j.procs.2016.06.047 ↗
15. Decision tree-based lightweight intrusion detection using a wrapper approach10.1016/j.eswa.2011.06.013 ↗
16. A survey of data mining and machine learning methods for cybersecurity intrusion detection10.1109/COMST.2015.2494502 ↗
17. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
18. A novel hybrid intrusion detection method integrating anomaly detection with misuse detection10.1016/j.eswa.2013.08.066 ↗
19. Analysis of data pre-processing influence on intrusion detection using NSL-KDD dataset
20. Features selection for intrusion detection systems based on support vector machines
21. Deep learning approach for network intrusion detection in software-defined networking
22. Deep learning approach combining sparse autoencoder with SVM for network intrusion detection10.1109/ACCESS.2018.2869577 ↗
23. A deep auto-encoder based approach for an intrusion detection system
24. Enhanced network anomaly detection based on deep neural networks10.1109/ACCESS.2018.2863036 ↗
25. A step forward to revolutionize intrusion detection system using deep convolution neural network
26. Deep autoencoders and feedforward networks based on a new regularization for anomaly detection10.1155/2020/7086367 ↗
27. Performance evaluation of a combined anomaly detection platform10.1109/ACCESS.2019.2930832 ↗
28. network intrusion detection based on novel feature selection model and various recurrent neural networks
29. Deep learning-based latent feature extraction for intrusion detection
30. HAST-IDS learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection10.1109/ACCESS.2017.2780250 ↗
31. Deep learning and Its applications to machine health monitoring: A survey
32. High dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning10.1016/j.patcog.2016.03.028 ↗
33. Network anomaly detection with the restricted Boltzmann machine10.1016/j.neucom.2012.11.050 ↗
34. The applications of deep learning on traffic identification
35. A deep learning approach for network intrusion detection system
36. A deep learning approach for intrusion detection using recurrent neural networks10.1109/ACCESS.2017.2762418 ↗
37. A scalable and hybrid intrusion detection system based on the convolutional-LSTM network10.3390/sym11040583 ↗
38. Toward an online anomaly intrusion detection system based on deep learning
39. A deep learning approach to network intrusion detection10.1109/TETCI.2017.2772792 ↗
40. Method of intrusion detection using deep neural network
41. A new approach to intrusion detection using artificial neural networks and fuzzy clustering10.1016/j.eswa.2010.02.102 ↗
42. Intrusion detection using an ensemble of intelligent paradigms10.1016/j.jnca.2004.01.003 ↗
43. Effective dimensionality reduction of payload-based anomaly detection in TMAD model for HTTP Payload
44. Design of an evolutionary approach for intrusion detection10.1155/2013/962185 ↗
45. Anomaly-based intrusion detection through k-means clustering and naive Bayes classification
46. Oving K-means clustering using discretization technique in network intrusion detection system
47. Detection of denial-of-service attacks based on computer vision techniques10.1109/TC.2014.2375218 ↗
48. A real-time adaptive intrusion detection alert classifier for high-speed networks
49. M-AdaBoost-A based ensemble system for network intrusion detection10.1016/j.eswa.2020.113864 ↗
50. I-Siam IDS: An improved Siam-IDS for handling class imbalance in network-based intrusion detection systems
51. Data imbalance in classification: Experimental evaluation10.1016/j.ins.2019.11.004 ↗
52. Toward developing a systematic approach to generate benchmark datasets for intrusion detection10.1016/j.cose.2011.12.012 ↗
53. MLlib: Machine learning in apache spark
54. Apache spark: A unified engine for big data processing10.1145/2934664 ↗
55. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
56. Learning precise timing with LSTM recurrent networks
57. A two-stage big data analytics framework with real-world applications using spark machine learning and long short-term memory network10.3390/sym10100485 ↗
58. Modeling spatial-temporal clues in a hybrid deep learning framework for video classification
59. Document modeling with gated recurrent neural network for sentiment classification
60. Video-based emotion recognition using Cnn-rnn and c3d hybrid networks
61. Abnormal event detection on BMTT-PETS, 2017 surveillance challenge
62. Intrusion detection based on normal traffic specifications10.5815/ijcnis.2015.09.04 ↗
63. Development of a network intrusion detection system using apache Hadoop and spark
64. Network intrusion detection system based on recursive feature addition and bigram technique10.1016/j.cose.2017.10.011 ↗
65. A novel scalable intrusion detection system based on deep learning
66. An improved ensemble approach for effective intrusion detection10.1007/s11227-019-03035-w ↗
67. Using a recurrent neural network and restricted Boltzmann machines for malicious traffic detection