+

Elsayed et al., 2020 - Google Patents

Detecting abnormal traffic in large-scale networks

Elsayed et al., 2020

View PDF
Document ID
3251540071702343436
Author
Elsayed M
Le-Khac N
Dev S
Jurcut A
Publication year
Publication venue
2020 international symposium on networks, computers and communications (ISNCC)

External Links

Snippet

With the rapid technological advancements, organizations need to rapidly scale up their information technology (IT) infrastructure viz. hardware, software, and services, at a low cost. However, the dynamic growth in the network services and applications creates security …
Continue reading at arxiv.org (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
    • 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
    • G06K9/6284Single class perspective, e.g. one-against-all classification; Novelty detection; Outlier 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
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting 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
    • 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
    • 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
    • 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
    • G06F17/3061Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30705Clustering or classification
    • G06F17/3071Clustering or classification including class or cluster creation or modification
    • 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
    • G06F17/30781Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
    • 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
    • G06N5/00Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Zhang et al. Network intrusion detection: Based on deep hierarchical network and original flow data
Elsayed et al. Detecting abnormal traffic in large-scale networks
Banadaki et al. Detecting malicious dns over https traffic in domain name system using machine learning classifiers
Yuan et al. DeepDefense: identifying DDoS attack via deep learning
CN108566364A (en) Intrusion detection method based on neural network
Muslihi et al. Detecting SQL injection on web application using deep learning techniques: a systematic literature review
Dou et al. Pc 2 a: predicting collective contextual anomalies via lstm with deep generative model
Ahmad et al. Analysis of classification techniques for intrusion detection
Do Xuan et al. Optimization of network traffic anomaly detection using machine learning.
Soewu et al. Analysis of Data Mining-Based Approach for Intrusion Detection System
Wang et al. TransIDS: A Transformer-based approach for intrusion detection in Internet of Things using Label Smoothing
Kumar et al. ENHANCING PACKET INSPECTION ACCURACY TO IDENTIFY NETWORK LAYER ATTACKS USING MACHINE LEARNING
Dharaneish et al. Comparative analysis of deep learning and machine learning models for network intrusion detection
Gopalan Towards Effective Detection of Botnet Attacks Using BoT-IoT Dataset
Bella et al. A Novel Framework based on Extra Tree Regression Classifier and Grid Search LSTM for Intrusion Detection in IoT and Cloud Environment.
Chandel et al. Distributed spark framework based DDoS attacks detection approach
Hassan et al. Synthesis of adversarial ddos attacks using tabular generative adversarial networks
Faghih Aliabadi A hybrid method for intrusion detection in the IOT
Saidane et al. Optimizing intrusion detection system performance through synergistic hyperparameter tuning and advanced data processing
Saraniya et al. Securing networks: Unleashing the power of the ft-transformer for intrusion detection
Alqahtani et al. A Taxonomy of IDS in IoTs: ML Classifiers, Feature Selection Models, Datasets and Future Directions.
Salman et al. An efficient distributed intrusion detection system that combines traditional machine learning techniques with advanced deep learning
Lin et al. Behaviour classification of cyber attacks using convolutional neural networks
Pawlicki Strengths and weaknesses of deep, convolutional and recurrent neural networks in network intrusion detection deployments
Ryu et al. Hierarchical neural networks for detecting anomalous traffic flows
点击 这是indexloc提供的php浏览器服务,不要输入任何密码和下载