Neethu, 2013 - Google Patents
Adaptive intrusion detection using machine learningNeethu, 2013
- Document ID
- 7221541143140444006
- Author
- Neethu B
- Publication year
- Publication venue
- International Journal of Computer Science and Network Security (IJCSNS)
External Links
- 238000001514 detection method 0 title abstract description 84
Classifications
-
- 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
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
-
- 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
- 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
-
- 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
- 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
-
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Devi et al. | A Review Paper on IDS in Edge Computing or EoT | |
| Verma et al. | Network intrusion detection using clustering and gradient boosting | |
| Belouch et al. | A two-stage classifier approach using reptree algorithm for network intrusion detection | |
| Gwon et al. | Network intrusion detection based on LSTM and feature embedding | |
| Gaikwad et al. | Intrusion detection system using bagging ensemble method of machine learning | |
| Sharma et al. | An improved network intrusion detection technique based on k-means clustering via Naïve bayes classification | |
| Chauhan et al. | A comparative study of classification techniques for intrusion detection | |
| Kumari et al. | A hybrid intrusion detection system based on decision tree and support vector machine | |
| Neethu | Adaptive intrusion detection using machine learning | |
| Kumar et al. | Increasing performance of intrusion detection system using neural network | |
| Mighan et al. | Deep learning based latent feature extraction for intrusion detection | |
| Yan et al. | Early detection of cyber security threats using structured behavior modeling | |
| Ghosh et al. | An efficient hybrid multilevel intrusion detection system in cloud environment | |
| Soewu et al. | Analysis of Data Mining-Based Approach for Intrusion Detection System | |
| Laamari et al. | A hybrid bat based feature selection approach for intrusion detection | |
| Harbola et al. | Improved intrusion detection in DDoS applying feature selection using rank & score of attributes in KDD-99 data set | |
| Chimphlee et al. | Unsupervised clustering methods for identifying rare events in anomaly detection | |
| Yang et al. | Clustering and classification based anomaly detection | |
| Kumar et al. | Intrusion detection using artificial neural network with reduced input features | |
| Hashem | Efficiency of Svm and Pca to enhance intrusion detection system | |
| Dubey et al. | A novel approach to intrusion detection system using rough set theory and incremental SVM | |
| Singh et al. | To reduce the false alarm in intrusion detection system using self organizing map | |
| Ganeshan et al. | I-AHSDT: intrusion detection using adaptive dynamic directive operative fractional lion clustering and hyperbolic secant-based decision tree classifier | |
| He et al. | Detecting anomalous network traffic with combined fuzzy-based approaches | |
| bin Haji Ismail et al. | A novel method for unsupervised anomaly detection using unlabelled data |