Mahfouz et al., 2020 - Google Patents
Ensemble classifiers for network intrusion detection using a novel network attack datasetMahfouz et al., 2020
View HTML- Document ID
- 6089098499487090237
- Author
- Mahfouz A
- Abuhussein A
- Venugopal D
- Shiva S
- Publication year
- Publication venue
- Future Internet
External Links
Snippet
Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a …
- 238000001514 detection method 0 title abstract description 73
Classifications
-
- 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
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/55—Detecting local intrusion or implementing counter-measures
-
- 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
- G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
- G06F21/57—Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
- G06F21/577—Assessing vulnerabilities and evaluating computer system security
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1425—Traffic logging, e.g. anomaly detection
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/14—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
- H04L63/1408—Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
- H04L63/1416—Event detection, e.g. attack signature detection
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
-
- 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mahfouz et al. | Ensemble classifiers for network intrusion detection using a novel network attack dataset | |
| ElKashlan et al. | A machine learning-based intrusion detection system for IoT electric vehicle charging stations (EVCSs) | |
| Inayat et al. | Learning-based methods for cyber attacks detection in IoT systems: A survey on methods, analysis, and future prospects | |
| Alraizza et al. | Ransomware detection using machine learning: A survey | |
| Chaganti et al. | Deep learning approach for SDN-enabled intrusion detection system in IoT networks | |
| Khraisat et al. | Hybrid intrusion detection system based on the stacking ensemble of c5 decision tree classifier and one class support vector machine | |
| Alzaqebah et al. | A modified grey wolf optimization algorithm for an intrusion detection system | |
| Quintero-Bonilla et al. | A new proposal on the advanced persistent threat: A survey | |
| Aiyanyo et al. | A systematic review of defensive and offensive cybersecurity with machine learning | |
| Preuveneers et al. | Sharing machine learning models as indicators of compromise for cyber threat intelligence | |
| Ji et al. | Artificial intelligence-based anomaly detection technology over encrypted traffic: A systematic literature review | |
| Ban et al. | Breaking alert fatigue: AI-assisted SIEM framework for effective incident response | |
| Priyadarshini | Anomaly detection of IoT cyberattacks in smart cities using federated learning and split learning | |
| Demertzis et al. | The next generation cognitive security operations center: network flow forensics using cybersecurity intelligence | |
| Verma et al. | A novel intrusion detection approach using machine learning ensemble for IoT environments | |
| Nkongolo et al. | Ugransome1819: A novel dataset for anomaly detection and zero-day threats | |
| Azeez et al. | Network intrusion detection with a hashing based apriori algorithm using Hadoop MapReduce | |
| Abu Al-Haija et al. | A lightweight double-stage scheme to identify malicious DNS over HTTPS traffic using a hybrid learning approach | |
| Jmal et al. | Distributed blockchain-SDN secure IoT system based on ANN to mitigate DDoS attacks | |
| Pivarníková et al. | Early-stage detection of cyber attacks | |
| Shah et al. | Network intrusion detection through discriminative feature selection by using sparse logistic regression | |
| Shanmugam et al. | Addressing class imbalance in intrusion detection: A comprehensive evaluation of machine learning approaches | |
| ElDahshan et al. | Meta-heuristic optimization algorithm-based hierarchical intrusion detection system | |
| Paracha et al. | Leveraging ai for network threat detection—a conceptual overview | |
| Fatema et al. | Federated XAI IDS: An explainable and safeguarding privacy approach to detect intrusion combining federated learning and SHAP |