标题
Intrusion Detection Methods Based on Integrated Deep Learning Model
作者
关键词
Deep learning, Deep neural network, Feature learning, Mini-batch gradient descent, Intrusion detection
出版物
COMPUTERS & SECURITY
Volume -, Issue -, Pages 102177
出版商
Elsevier BV
发表日期
2021-01-08
DOI
10.1016/j.cose.2021.102177
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- A real-time and ubiquitous network attack detection based on deep belief network and support vector machine
- (2020) Hao Zhang et al. IEEE-CAA Journal of Automatica Sinica
- Genetic convolutional neural network for intrusion detection systems
- (2020) Minh Tuan Nguyen et al. Future Generation Computer Systems-The International Journal of eScience
- Intrusion Detection for Cyber–Physical Systems Using Generative Adversarial Networks in Fog Environment
- (2020) Paulo Freitas de Araujo-Filho et al. IEEE Internet of Things Journal
- An Efficient Stochastic Gradient Descent Algorithm to Maximize the Coverage of Cellular Networks
- (2019) Yaxi Liu et al. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
- Online GMM Clustering and Mini-Batch Gradient Descent Based Optimization for Industrial IoT 4.0
- (2019) Seifeddine Messaoud et al. IEEE Transactions on Industrial Informatics
- An Industrial Network Intrusion Detection Algorithm Based on Multifeature Data Clustering Optimization Model
- (2019) Wei Liang et al. IEEE Transactions on Industrial Informatics
- Speech Enhancement Based on Teacher–Student Deep Learning Using Improved Speech Presence Probability for Noise-Robust Speech Recognition
- (2019) Yan-Hui Tu et al. IEEE-ACM Transactions on Audio Speech and Language Processing
- Sequence Covering for Efficient Host-Based Intrusion Detection
- (2018) Pierre-Francois Marteau IEEE Transactions on Information Forensics and Security
- Host-based misuse intrusion detection using PCA feature extraction and kNN classification algorithms
- (2018) Gursel Serpen et al. Intelligent Data Analysis
- Design of cognitive fog computing for intrusion detection in Internet of Things
- (2018) S. Prabavathy et al. JOURNAL OF COMMUNICATIONS AND NETWORKS
- Deep Learning Approach Combining Sparse Autoen-coder with SVM for Network Intrusion Detection
- (2018) Majjed Al-Qatf et al. IEEE Access
- LR-HIDS: logistic regression host-based intrusion detection system for cloud environments
- (2018) Elham Besharati et al. Journal of Ambient Intelligence and Humanized Computing
- Dynamic Feature Acquisition Using Denoising Autoencoders
- (2018) Mohammad Kachuee et al. IEEE Transactions on Neural Networks and Learning Systems
- A Hybrid Feature Selection Method for Improved Detection of Wired/Wireless Network Intrusions
- (2017) J. Rene Beulah et al. WIRELESS PERSONAL COMMUNICATIONS
- Host-Based Intrusion Detection for VANETs: A Statistical Approach to Rogue Node Detection
- (2016) Kamran Zaidi et al. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- A Comparative Study of Bag-of-Words and Bag-of-Topics Models of EO Image Patches
- (2015) Reza Bahmanyar et al. IEEE Geoscience and Remote Sensing Letters
- Feature Selection Based on Hybridization of Genetic Algorithm and Particle Swarm Optimization
- (2015) Pedram Ghamisi et al. IEEE Geoscience and Remote Sensing Letters
- Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning
- (2015) C. L. Philip Chen et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Quaternion softmax classifier
- (2014) Rui Zeng et al. ELECTRONICS LETTERS
- Semi-Supervised and Unsupervised Extreme Learning Machines
- (2014) Gao Huang et al. IEEE Transactions on Cybernetics
- Representation Learning: A Review and New Perspectives
- (2013) Y. Bengio et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A memory-efficient parallel string matching for intrusion detection systems
- (2009) Hyunjin Kim et al. IEEE COMMUNICATIONS LETTERS
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