Genetic convolutional neural network for intrusion detection systems
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Genetic convolutional neural network for intrusion detection systems
Authors
Keywords
Intrusion detection system, Genetic algorithm, Machine learning, Deep learning, Fuzzy C-mean clustering
Journal
Future Generation Computer Systems-The International Journal of eScience
Volume 113, Issue -, Pages 418-427
Publisher
Elsevier BV
Online
2020-07-18
DOI
10.1016/j.future.2020.07.042
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Improving the effectiveness of intrusion detection systems for hierarchical data
- (2019) Ran Yahalom et al. KNOWLEDGE-BASED SYSTEMS
- Network Intrusion Detection Based on Novel Feature Selection Model and Various Recurrent Neural Networks
- (2019) Thi-Thu-Huong Le et al. Applied Sciences-Basel
- Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network
- (2019) Yanqing Yang et al. SENSORS
- Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms
- (2019) Zouhair Chiba et al. COMPUTERS & SECURITY
- Network anomaly detection using channel boosted and residual learning based deep convolutional neural network
- (2019) Naveed Chouhan et al. APPLIED SOFT COMPUTING
- Adaptive intrusion detection via GA-GOGMM-based pattern learning with fuzzy rough set-based attribute selection
- (2019) Jinping Liu et al. EXPERT SYSTEMS WITH APPLICATIONS
- A practical group blind signature scheme for privacy protection in smart grid
- (2019) Wei Kong et al. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
- A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments
- (2018) Amandeep Singh Sohal et al. COMPUTERS & SECURITY
- Distributed attack detection scheme using deep learning approach for Internet of Things
- (2018) Abebe Abeshu Diro et al. Future Generation Computer Systems-The International Journal of eScience
- A novel statistical technique for intrusion detection systems
- (2018) Enamul Kabir et al. Future Generation Computer Systems-The International Journal of eScience
- An Improved Intrusion Detection Algorithm Based on GA and SVM
- (2018) Peiying Tao et al. IEEE Access
- Effective Feature Extraction via Stacked Sparse Autoencoder to Improve Intrusion Detection System
- (2018) Binghao Yan et al. IEEE Access
- A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks
- (2018) Kehe Wu et al. IEEE Access
- Deep Learning Approach Combining Sparse Autoen-coder with SVM for Network Intrusion Detection
- (2018) Majjed Al-Qatf et al. IEEE Access
- Firefly algorithm based feature selection for network intrusion detection
- (2018) Selvakumar B et al. COMPUTERS & SECURITY
- DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation
- (2017) Shruti Kalsi et al. JOURNAL OF MEDICAL SYSTEMS
- A survey of intrusion detection in Internet of Things
- (2017) Bruno Bogaz Zarpelão et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks
- (2017) Chuanlong Yin et al. IEEE Access
- A novel SVM-kNN-PSO ensemble method for intrusion detection system
- (2016) Abdulla Amin Aburomman et al. APPLIED SOFT COMPUTING
- A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
- (2016) Anna L. Buczak et al. IEEE Communications Surveys and Tutorials
- Towards Achieving Data Security with the Cloud Computing Adoption Framework
- (2016) Victor Chang et al. IEEE Transactions on Services Computing
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started