Atli et al., 2018 - Google Patents
Anomaly-based intrusion detection using extreme learning machine and aggregation of network traffic statistics in probability spaceAtli et al., 2018
View PDF- Document ID
- 16133095580468360505
- Author
- Atli B
- Miche Y
- Kalliola A
- Oliver I
- Holtmanns S
- Lendasse A
- Publication year
- Publication venue
- Cognitive Computation
External Links
Snippet
Recently, with the increased use of network communication, the risk of compromising the information has grown immensely. Intrusions have become more sophisticated and few methods can achieve efficient results while the network behavior constantly changes. This …
- 238000001514 detection method 0 title abstract description 79
Classifications
-
- 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
- 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
-
- 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
- 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
- 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
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
-
- 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
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Atli et al. | Anomaly-based intrusion detection using extreme learning machine and aggregation of network traffic statistics in probability space | |
Thakkar et al. | A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions | |
Ge et al. | Towards a deep learning-driven intrusion detection approach for Internet of Things | |
Samy et al. | Fog-based attack detection framework for internet of things using deep learning | |
Jha et al. | Intrusion detection system using support vector machine | |
Folino et al. | Ensemble based collaborative and distributed intrusion detection systems: A survey | |
Abadeh et al. | A parallel genetic local search algorithm for intrusion detection in computer networks | |
Pektaş et al. | A deep learning method to detect network intrusion through flow‐based features | |
Carrasco et al. | Unsupervised intrusion detection through skip-gram models of network behavior | |
Fahad et al. | Toward an efficient and scalable feature selection approach for internet traffic classification | |
Ibrahimi et al. | Management of intrusion detection systems based-KDD99: Analysis with LDA and PCA | |
Altaf et al. | NE-GConv: A lightweight node edge graph convolutional network for intrusion detection | |
Garg et al. | HyClass: Hybrid classification model for anomaly detection in cloud environment | |
Monshizadeh et al. | Performance evaluation of a combined anomaly detection platform | |
Fitriani et al. | Review of semi-supervised method for intrusion detection system | |
Maseer et al. | Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges | |
Atli | Anomaly-based intrusion detection by modeling probability distributions of flow characteristics | |
Wei et al. | Reconstruction-based lstm-autoencoder for anomaly-based ddos attack detection over multivariate time-series data | |
Attak et al. | Application of distributed computing and machine learning technologies to cybersecurity | |
Awad et al. | Addressing imbalanced classes problem of intrusion detection system using weighted extreme learning machine | |
Ravi | Deep learning-based network intrusion detection in smart healthcare enterprise systems | |
Benmalek et al. | Particle swarm optimization-enhanced machine learning and deep learning techniques for Internet of Things intrusion detection | |
Mwitondi et al. | A robust domain partitioning intrusion detection method | |
Sarasamma et al. | Min-max hyperellipsoidal clustering for anomaly detection in network security | |
Yadav et al. | Enhancement of intrusion detection system using machine learning |