[go: up one dir, main page]

Chapaneri et al., 2018 - Google Patents

A comprehensive survey of machine learning-based network intrusion detection

Chapaneri 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 …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55Detecting local intrusion or implementing counter-measures
    • G06F21/552Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1425Traffic logging, e.g. anomaly detection
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1408Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic by monitoring network traffic
    • H04L63/1416Event detection, e.g. attack signature detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1433Vulnerability analysis
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/14Network architectures or network communication protocols for network security for detecting or protecting against malicious traffic
    • H04L63/1441Countermeasures against malicious traffic
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F2221/00Indexing 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