Chapaneri et al., 2018 - Google Patents
A comprehensive survey of machine learning-based network intrusion detectionChapaneri et al., 2018
View PDF- Document ID
- 17615813416767311633
- Author
- Chapaneri R
- Shah S
- Publication year
- Publication venue
- Smart Intelligent Computing and Applications: Proceedings of the Second International Conference on SCI 2018, Volume 1
External Links
Snippet
In this paper, we survey the published work on machine learning-based network intrusion detection systems covering recent state-of-the-art techniques. We address the problems of conventional datasets and present a detailed comparison of modern network intrusion …
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
-
- 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
- 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
-
- 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
- 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
-
- 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
-
- 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
-
- 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
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2221/00—Indexing scheme relating to security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Chapaneri et al. | A comprehensive survey of machine learning-based network intrusion detection | |
Disha et al. | Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique | |
Vu et al. | Deep transfer learning for IoT attack detection | |
Elsayed et al. | DDoSNet: A deep-learning model for detecting network attacks | |
Carrasco et al. | Unsupervised intrusion detection through skip-gram models of network behavior | |
Alkahtani et al. | [Retracted] Adaptive Anomaly Detection Framework Model Objects in Cyberspace | |
Elsayed et al. | The role of CNN for intrusion detection systems: An improved CNN learning approach for SDNs | |
Grill et al. | Learning combination of anomaly detectors for security domain | |
Abirami et al. | Building an ensemble learning based algorithm for improving intrusion detection system | |
Nagaraja et al. | UTTAMA: an intrusion detection system based on feature clustering and feature transformation | |
Ustebay et al. | Cyber attack detection by using neural network approaches: shallow neural network, deep neural network and autoencoder | |
Pérez et al. | Comparison of network intrusion detection performance using feature representation | |
Saurabh et al. | Nfdlm: A lightweight network flow based deep learning model for ddos attack detection in iot domains | |
Manjunatha et al. | A network intrusion detection framework on sparse deep denoising auto-encoder for dimensionality reduction | |
Ahmed et al. | Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in intrusion detection systems | |
Roopak et al. | An unsupervised approach for the detection of zero-day DDoS attacks in IoT networks | |
Kulkarni et al. | An intrusion detection system using extended Kalman filter and neural networks for IoT networks | |
Güney | Feature selection‐integrated classifier optimisation algorithm for network intrusion detection | |
Maddu et al. | Res2Net-ERNN: deep learning based cyberattack classification in software defined network | |
Ahmad et al. | Supervised machine learning approaches for attack detection in the IoT network | |
Omer et al. | Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks. | |
Kushwaha et al. | mFCBF based lightweight intrusion detection system for IoT networks | |
Amin et al. | Ensemble based effective intrusion detection system for cloud environment over UNSW-NB15 dataset | |
Anand et al. | A comprehensive study of DDoS attack on internet of things network | |
Edosa | Comparative Analysis of Performance and Influence of PCA On Machine Learning Models Leveraging The NSL-KDD Dataset |