Dawoud et al., 2018 - Google Patents
Deep learning for network anomalies detectionDawoud et al., 2018
- Document ID
- 3300777458871638244
- Author
- Dawoud A
- Shahristani S
- Raun C
- Publication year
- Publication venue
- 2018 International Conference on Machine Learning and Data Engineering (iCMLDE)
External Links
Snippet
Intrusion Detection Systems (IDS) provide substantial measures to protect networks assets. IDSs are software/hardware systems dedicated to exposing network threats. Signature- based, and anomalies detection are conventional approaches applied for the detection …
- 238000001514 detection method 0 title abstract description 58
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
- G06K9/627—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
-
- 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
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- 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
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- 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
- G06K9/6296—Graphical models, e.g. Bayesian networks
-
- 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
- G06N3/04—Architectures, e.g. interconnection topology
-
- 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
- G06N5/025—Extracting rules from data
-
- 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
- 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
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Dawoud et al. | Deep learning for network anomalies detection | |
Yang et al. | Network intrusion detection based on supervised adversarial variational auto-encoder with regularization | |
Liu et al. | CNN and RNN based payload classification methods for attack detection | |
CN109284606B (en) | Data flow anomaly detection system based on empirical characteristics and convolutional neural network | |
Verma et al. | On evaluation of network intrusion detection systems: Statistical analysis of CIDDS-001 dataset using machine learning techniques | |
Haggag et al. | Implementing a deep learning model for intrusion detection on apache spark platform | |
Alrawashdeh et al. | Fast activation function approach for deep learning based online anomaly intrusion detection | |
Disha et al. | A Comparative study of machine learning models for Network Intrusion Detection System using UNSW-NB 15 dataset | |
CN115801374A (en) | Network intrusion data classification method and device, electronic equipment and storage medium | |
Sidharth et al. | Network Intrusion Detection System Using Stacking and Boosting Ensemble Methods | |
Abdullah et al. | An artificial deep neural network for the binary classification of network traffic | |
Macas et al. | Deep Learning Methods for Cybersecurity and Intrusion Detection Systems | |
Mohiuddin et al. | Intrusion detection using hybridized meta-heuristic techniques with Weighted XGBoost Classifier | |
CN113609480B (en) | Multipath learning intrusion detection method based on large-scale network flow | |
Shahraki et al. | Internet traffic classification using an ensemble of deep convolutional neural networks | |
Vartouni et al. | Auto-threshold deep SVDD for anomaly-based web application firewall | |
Aburomman et al. | Evolutionof Intrusion Detection Systems Based on Machine Learning Methods | |
Ahuja et al. | DDoS attack traffic classification in SDN using deep learning | |
Huynh et al. | On the performance of intrusion detection systems with hidden multilayer neural network using DSD training | |
Manandhar et al. | Intrusion detection based on outlier detection method | |
Farahnakian et al. | Anomaly-based intrusion detection using deep neural networks | |
Bui et al. | One-class fusion-based learning model for anomaly detection | |
Musthafa et al. | Evaluation of IDS model by improving accuracy and reducing overfitting using stacking LSTM | |
Virmani et al. | 6 IoT attack detection using quantum deep learning in large-scale networks | |
Syed et al. | Intrusion Detection for Time-Series IoT Data with Recurrent Neural Networks and Feature Selection |