Gogoi et al., 2013 - Google Patents
A rough set–based effective rule generation method for classification with an application in intrusion detectionGogoi et al., 2013
View PDF- Document ID
- 11724641894601689076
- Author
- Gogoi P
- Bhattacharyya D
- Kalita J
- Publication year
- Publication venue
- International Journal of Security and Networks
External Links
Snippet
In this paper, we use Rough Set Theory (RST) to address the important problem of generating decision rules for data mining. In particular, we propose a rough set–based approach to mine rules from inconsistent data. It computes the lower and upper …
- 238000001514 detection method 0 title description 8
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
- G06F17/30286—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
- G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
- G06F17/30946—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type indexing structures
-
- 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
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
-
- 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
- H04L63/02—Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
- H04L63/0227—Filtering policies
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gogoi et al. | MLH-IDS: a multi-level hybrid intrusion detection method | |
Rossi et al. | Modeling dynamic behavior in large evolving graphs | |
Gogoi et al. | A rough set–based effective rule generation method for classification with an application in intrusion detection | |
Maza et al. | Feature selection algorithms in intrusion detection system: A survey | |
CN114172688B (en) | Method for automatically extracting key nodes of network threat of encrypted traffic based on GCN-DL (generalized traffic channel-DL) | |
CN112333195B (en) | APT attack scene reduction detection method and system based on multi-source log correlation analysis | |
CN113469234A (en) | Network flow abnormity detection method based on model-free federal meta-learning | |
Jiang et al. | An incremental decision tree algorithm based on rough sets and its application in intrusion detection | |
CN113821793B (en) | Multi-stage attack scene construction method and system based on graph convolution neural network | |
CN113992349A (en) | Malicious traffic identification method, device, device and storage medium | |
Nishiyama et al. | SILU: Strategy involving large-scale unlabeled logs for improving malware detector | |
Wang et al. | An automatic application signature construction system for unknown traffic | |
Himura et al. | Synoptic graphlet: Bridging the gap between supervised and unsupervised profiling of host-level network traffic | |
Golczynski et al. | End-to-end anomaly detection for identifying malicious cyber behavior through NLP-based log embeddings | |
CN119254507A (en) | Cyberspace counter-mapping method, device, computer equipment and storage medium | |
Shanbhogue et al. | Survey of data mining (DM) and machine learning (ML) methods on cyber security | |
Singh | Real Time Intrusion Detection In Edge Computing Using Machine Learning Techniques | |
Wang et al. | Machine learned real-time traffic classifiers | |
Smeriga et al. | Behavior-aware network segmentation using ip flows | |
Fang et al. | Active exploration: simultaneous sampling and labeling for large graphs | |
Nalini et al. | Enhancing early attack detection: novel hybrid density-based isolation forest for improved anomaly detection | |
Joon et al. | A Comprehensive Investigation Into the Implementation of Machine Learning Solutions for Network Traffic Classification | |
Nascimento et al. | Comparative study of a Hybrid Model for network traffic identification and its optimization using Firefly Algorithm | |
Gogoi et al. | Efficient rule set generation using rough set theory for classification of high dimensional data | |
Bazan et al. | Classifiers for behavioral patterns identification induced from huge temporal data |