Aravind et al., 2017 - Google Patents
Design of an intrusion detection system based on distance feature using ensemble classifierAravind et al., 2017
- Document ID
- 6509482772409334300
- Author
- Aravind M
- Kalaiselvi V
- Publication year
- Publication venue
- 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)
External Links
Snippet
This paper focuses on designing an Intrusion Detection System (IDS), which detects the family of attack in a dataset. An IDS detects various types of malicious traffic and computer usage which cannot be detected by a conventional firewall. In this proposed work, the data …
- 238000001514 detection method 0 title abstract description 35
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
- G06K9/6279—Classification techniques relating to the number of classes
- G06K9/6284—Single class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- 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
- G06K9/6228—Selecting the most significant subset of features
-
- 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
- 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
-
- 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
-
- 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
- G06F21/50—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
- 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
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Thakkar et al. | A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions | |
| Om et al. | A hybrid system for reducing the false alarm rate of anomaly intrusion detection system | |
| Sangkatsanee et al. | Practical real-time intrusion detection using machine learning approaches | |
| Ektefa et al. | Intrusion detection using data mining techniques | |
| Satpute et al. | A survey on anomaly detection in network intrusion detection system using particle swarm optimization based machine learning techniques | |
| Ghosh et al. | Proposed GA-BFSS and logistic regression based intrusion detection system | |
| Stokes et al. | Aladin: Active learning of anomalies to detect intrusions | |
| Mitrokotsa et al. | Detecting denial of service attacks using emergent self-organizing maps | |
| Aravind et al. | Design of an intrusion detection system based on distance feature using ensemble classifier | |
| Ghosh et al. | An efficient hybrid multilevel intrusion detection system in cloud environment | |
| Arshad et al. | Comparative study of machine learning techniques for intrusion detection on CICIDS-2017 Dataset | |
| Soewu et al. | Analysis of Data Mining-Based Approach for Intrusion Detection System | |
| Khor et al. | The effectiveness of sampling methods for the imbalanced network intrusion detection data set | |
| Singh et al. | Machine learning mechanisms for network anomaly detection system: A review | |
| Pallaprolu et al. | Zero-day attack identification in streaming data using semantics and Spark | |
| Chliah et al. | Hybrid Machine Learning-Based Approach for Anomaly Detection using Apache Spark | |
| Franco et al. | Implementation of an intrusion detection system based on self organizing map | |
| Yadav et al. | Intrusion detection system using machine learning algorithms: a comparative study | |
| Termos et al. | Intrusion detection system for iot based on complex networks and machine learning | |
| Alsulami et al. | A review on machine learning based approaches of network intrusion detection systems | |
| Shetty et al. | Data mining techniques for real time intrusion detection systems | |
| Dubey et al. | A novel approach to intrusion detection system using rough set theory and incremental SVM | |
| Vargheese et al. | Machine Learning for Enhanced Cyber Security | |
| Safa et al. | Optimizing the Performance of the IDS through Feature-Relevant Selection Using PSO and Random Forest Techniques | |
| Golovko et al. | Neural network approaches for intrusion detection and recognition |