Ding et al., 2018 - Google Patents
Intrusion detection system for NSL-KDD dataset using convolutional neural networksDing et al., 2018
- Document ID
- 7206773220661580301
- Author
- Ding Y
- Zhai Y
- Publication year
- Publication venue
- Proceedings of the 2018 2nd International conference on computer science and artificial intelligence
External Links
Snippet
With the increment of cyber traffic, there is a growing demand for cyber security. How to accurately detect cyber intrusions is the hotspot of recent research. Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability …
- 238000001514 detection method 0 title abstract description 53
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
-
- 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
- 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
- G06K9/6247—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
-
- 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
- G06K9/36—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
- 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
-
- 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
-
- 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
- H04L63/1441—Countermeasures against malicious traffic
- H04L63/1458—Denial of Service
-
- 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
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- H04L63/1433—Vulnerability analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ding et al. | Intrusion detection system for NSL-KDD dataset using convolutional neural networks | |
Park et al. | An enhanced AI-based network intrusion detection system using generative adversarial networks | |
Disha et al. | Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique | |
Kasim | An efficient and robust deep learning based network anomaly detection against distributed denial of service attacks | |
Wang et al. | HAST-IDS: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection | |
Haq et al. | Development of PCCNN-based network intrusion detection system for EDGE computing. | |
Yang et al. | TLS/SSL encrypted traffic classification with autoencoder and convolutional neural network | |
Jongsuebsuk et al. | Network intrusion detection with fuzzy genetic algorithm for unknown attacks | |
Sarwar et al. | Design of an advance intrusion detection system for IoT networks | |
Lee et al. | CoNN-IDS: Intrusion detection system based on collaborative neural networks and agile training | |
Reddy et al. | Detection of DDoS Attack in IoT Networks Using Sample elected RNN-ELM | |
Dharaneish et al. | Comparative analysis of deep learning and machine learning models for network intrusion detection | |
Nazarudeen et al. | Efficient DDoS Attack Detection using Machine Learning Techniques | |
Raja Sree et al. | HAP: detection of HTTP flooding attacks in cloud using diffusion map and affinity propagation clustering | |
Papalkar et al. | Review of unknown attack detection with deep learning techniques | |
Gayatri et al. | A Two-Level Hybrid Intrusion Detection Learning Method | |
Haoyi et al. | IDS-GAN: Stepping up Intrusion Detection Method using GAN Algorithm | |
Lin et al. | Behaviour classification of cyber attacks using convolutional neural networks | |
Adeshina | Machine learning based approach for detecting Distributed Denial of Service attack | |
Al-Nafjan et al. | Intrusion detection using PCA based modular neural network | |
Shpinareva et al. | Detection and classification of network attacks using the deep neural network cascade | |
Sravanthi et al. | Cyber Threat Detection Based On Artificial Neural Networks Using Event Profiles | |
Gaikwad et al. | One versus all classification in network intrusion detection using decision tree | |
Gowthami et al. | Convolution Neural Network-Based Efficient Development of Intrusion Detection Using Various Deep Learning Approaches | |
Söderström | Anomaly-based Intrusion Detection Using Convolutional Neural Networks for IoT Devices |