Nothing Special   »   [go: up one dir, main page]

Robinson et al., 2015 - Google Patents

Ranking of machine learning algorithms based on the performance in classifying DDoS attacks

Robinson et al., 2015

Document ID
1961992330856190950
Author
Robinson R
Thomas C
Publication year
Publication venue
2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS)

External Links

Snippet

Network Security has become one of the most important factors to consider as the Internet evolves. The most important attack which affects the availability of service is Distributed Denial of Service. The service disruption may cause substantial financial loss as well as …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • 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

Similar Documents

Publication Publication Date Title
Robinson et al. Ranking of machine learning algorithms based on the performance in classifying DDoS attacks
Hosseini et al. The hybrid technique for DDoS detection with supervised learning algorithms
Almasoudy et al. Differential evolution wrapper feature selection for intrusion detection system
Khalaf et al. An adaptive protection of flooding attacks model for complex network environments
Abdullah et al. Performance evaluation of a genetic algorithm based approach to network intrusion detection system
Mehibs et al. Proposed network intrusion detection system‎ in cloud environment based on back‎ propagation neural network
Monshizadeh et al. Performance evaluation of a combined anomaly detection platform
Ahmed et al. Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques--A Comprehensive Survey
Mahbooba et al. Trust in intrusion detection systems: an investigation of performance analysis for machine learning and deep learning models
Buragohain et al. Anomaly based DDoS attack detection
Maslan et al. Feature selection for DDoS detection using classification machine learning techniques
Coscia et al. Automatic decision tree-based nidps ruleset generation for dos/ddos attacks
McCarthy et al. Feature vulnerability and robustness assessment against adversarial machine learning attacks
Ibrahim et al. Performance comparison of intrusion detection system using three different machine learning algorithms
Gehri et al. Towards generalizing machine learning models to detect command and control attack traffic
Lobato et al. A fast and accurate threat detection and prevention architecture using stream processing
Pinto et al. Detecting DDoS attacks using a cascade of machine learning classifiers based on Random Forest and MLP-ANN
Rani et al. Analysis of machine learning and deep learning intrusion detection system in Internet of Things network
Babenko et al. LVQ models of DDOS attacks identification
Sinha et al. Mining anomalies in large ISCX dataset using machine learning algorithms in KNIME
Beulah et al. Detection of DDoS attack using ensemble machine learning techniques
Vargheese et al. Machine Learning for Enhanced Cyber Security
Parekh et al. An Empirical Overview on DDoS: Taxonomy, Attacks, Tools and Attack Detection Mechanism
Agha et al. Improving discrimination accuracy rate of DDoS attacks and flash events
Padhiar et al. Performance evaluation of botnet detection using machine learning techniques