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

Masarat et al., 2014 - Google Patents

A novel framework, based on fuzzy ensemble of classifiers for intrusion detection systems

Masarat et al., 2014

Document ID
9970352696249626842
Author
Masarat S
Taheri H
Sharifian S
Publication year
Publication venue
2014 4th international conference on computer and knowledge engineering (ICCKE)

External Links

Snippet

By developing technology and speed of communications, providing security of networks becomes a significant topic in network interactions. Intrusion Detection Systems (IDS) play important role in providing general security in the networks. The major challenges with IDSs …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • 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
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00Network architectures or network communication protocols for network security
    • H04L63/02Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
    • H04L63/0227Filtering policies

Similar Documents

Publication Publication Date Title
Novaes et al. Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
Aljawarneh et al. Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model
Chiba et al. A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection
Tesfahun et al. Intrusion detection using random forests classifier with SMOTE and feature reduction
Masarat et al. A novel framework, based on fuzzy ensemble of classifiers for intrusion detection systems
Wang et al. Constructing important features from massive network traffic for lightweight intrusion detection
Iqbal et al. A feed-forward and pattern recognition ANN model for network intrusion detection
Ozkan-Ozay et al. A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
Ullah et al. A filter-based feature selection model for anomaly-based intrusion detection systems
Ghosh et al. Proposed GA-BFSS and logistic regression based intrusion detection system
Juneja et al. Artificial intelligence and cybersecurity: current trends and future prospects
Langin et al. Soft computing in intrusion detection: the state of the art
Beaver et al. A learning system for discriminating variants of malicious network traffic
Zainaddin et al. Hybrid of fuzzy clustering neural network over NSL dataset for intrusion detection system
Anifowose et al. Application of artificial intelligence in network intrusion detection
Dixit et al. Comparing and analyzing applications of intelligent techniques in cyberattack detection
Kajal et al. A hybrid approach for cyber security: improved intrusion detection system using Ann-Svm
Rastogi et al. An analysis of intrusion detection classification using supervised machine learning algorithms on nsl-kdd dataset
Manavi et al. A new intrusion detection system based on gated recurrent unit (GRU) and genetic algorithm
Bhati et al. Intrusion detection systems and techniques: a review
RajBalaji et al. Design of deep learning models for the identifications of harmful attack activities in IIOT
Napanda et al. Artificial intelligence techniques for network intrusion detection
Kumar et al. Intrusion detection using artificial neural network with reduced input features
Abdullahi et al. Comparison and investigation of AI-based approaches for cyberattack detection in cyber-physical systems
Alheeti et al. Increasing the rate of intrusion detection based on a hybrid technique