Wang et al., 2023 - Google Patents
Network anomaly intrusion detection based on deep learning approachWang et al., 2023
View HTML- Document ID
- 14327538701231278587
- Author
- Wang Y
- Houng Y
- Chen H
- Tseng S
- Publication year
- Publication venue
- Sensors
External Links
Snippet
The prevalence of internet usage leads to diverse internet traffic, which may contain information about various types of internet attacks. In recent years, many researchers have applied deep learning technology to intrusion detection systems and obtained fairly strong …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | Network anomaly intrusion detection based on deep learning approach | |
Balyan et al. | A hybrid intrusion detection model using ega-pso and improved random forest method | |
Awan et al. | Real-time DDoS attack detection system using big data approach | |
Alghazzawi et al. | Efficient detection of DDoS attacks using a hybrid deep learning model with improved feature selection | |
Gao et al. | Malicious network traffic detection based on deep neural networks and association analysis | |
Almaiah et al. | Performance investigation of principal component analysis for intrusion detection system using different support vector machine kernels | |
Gao et al. | Research on network intrusion detection based on incremental extreme learning machine and adaptive principal component analysis | |
Tareq et al. | Analysis of ton-iot, unw-nb15, and edge-iiot datasets using dl in cybersecurity for iot | |
Siddiqi et al. | Optimizing filter-based feature selection method flow for intrusion detection system | |
Mhawi et al. | Advanced feature-selection-based hybrid ensemble learning algorithms for network intrusion detection systems | |
Lee et al. | AE-CGAN model based high performance network intrusion detection system | |
Sarnovsky et al. | Hierarchical intrusion detection using machine learning and knowledge model | |
Xiao et al. | An intrusion detection system based on a simplified residual network | |
Alosaimi et al. | An intrusion detection system using BoT-IoT | |
Alkahtani et al. | Developing cybersecurity systems based on machine learning and deep learning algorithms for protecting food security systems: industrial control systems | |
Song et al. | TGA: a novel network intrusion detection method based on TCN, BiGRU and attention mechanism | |
Wang et al. | MFDroid: A stacking ensemble learning framework for Android malware detection | |
Liu et al. | Towards effective feature selection for iot botnet attack detection using a genetic algorithm | |
Paulauskas et al. | Application of histogram-based outlier scores to detect computer network anomalies | |
Afrifa et al. | Ensemble machine learning techniques for accurate and efficient detection of botnet attacks in connected computers | |
Shieh et al. | Detection of unknown ddos attack using convolutional neural networks featuring geometrical metric | |
Abad et al. | Classification of malicious URLs using machine learning | |
Joshi et al. | Machine-learning techniques for predicting phishing attacks in blockchain networks: A comparative study | |
Al-Taleb et al. | Towards a hybrid machine learning model for intelligent cyber threat identification in smart city environments | |
Larriva-Novo et al. | An approach for the application of a dynamic multi-class classifier for network intrusion detection systems |