[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Raahemi et al., 2009 - Google Patents

Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method

Raahemi et al., 2009

View PDF
Document ID
815693927488250847
Author
Raahemi B
Zhong W
Liu J
Publication year
Publication venue
Peer-to-peer networking and applications

External Links

Snippet

Unlabeled training examples are readily available in many applications, but labeled examples are fairly expensive to obtain. For instance, in our previous works on classification of peer-to-peer (P2P) Internet traffics, we observed that only about 25% of examples can be …
Continue reading at www.academia.edu (PDF) (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
    • H04L47/00—Traffic regulation in packet switching networks
    • H04L47/10—Flow control or congestion control
    • H04L47/24—Flow control or congestion control depending on the type of traffic, e.g. priority or quality of service [QoS]
    • H04L47/2441—Flow classification
    • 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
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing packet switching networks
    • H04L43/02—Arrangements for monitoring or testing packet switching networks involving a reduction of monitoring data
    • H04L43/026—Arrangements for monitoring or testing packet switching networks involving a reduction of monitoring data using flow generation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/28—Network-specific arrangements or communication protocols supporting networked applications for the provision of proxy services, e.g. intermediate processing or storage in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/10—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/02—Network-specific arrangements or communication protocols supporting networked applications involving the use of web-based technology, e.g. hyper text transfer protocol [HTTP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/02—Details
    • H04L12/26—Monitoring arrangements; Testing arrangements
    • H04L12/2602—Monitoring arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
    • H04L41/50—Network service management, i.e. ensuring proper service fulfillment according to an agreement or contract between two parties, e.g. between an IT-provider and a customer
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
    • H04L41/14—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/54—Store-and-forward switching systems

Similar Documents

Publication Publication Date Title
Zhao et al. Network traffic classification for data fusion: A survey
Shafiq et al. A machine learning approach for feature selection traffic classification using security analysis
Liu et al. Attention-based bidirectional GRU networks for efficient HTTPS traffic classification
US11663067B2 (en) Computerized high-speed anomaly detection
Najafimehr et al. A hybrid machine learning approach for detecting unprecedented DDoS attacks
US8311956B2 (en) Scalable traffic classifier and classifier training system
Zhang et al. Feature selection for optimizing traffic classification
Perera et al. A comparison of supervised machine learning algorithms for classification of communications network traffic
Thottan et al. Anomaly detection approaches for communication networks
Liu et al. Mobile app traffic flow feature extraction and selection for improving classification robustness
Liu et al. Adaptive learning on mobile network traffic data
Zhong et al. Classifying peer-to-peer applications using imbalanced concept-adapting very fast decision tree on IP data stream
Horchulhack et al. Toward feasible machine learning model updates in network-based intrusion detection
Liu et al. Extending labeled mobile network traffic data by three levels traffic identification fusion
Salman et al. Data representation for CNN based internet traffic classification: a comparative study
Raahemi et al. Peer-to-peer traffic identification by mining IP layer data streams using concept-adapting very fast decision tree
Archanaa et al. A comparative performance analysis on network traffic classification using supervised learning algorithms
Almomani Classification of virtual private networks encrypted traffic using ensemble learning algorithms
Luxemburk et al. Encrypted traffic classification: the QUIC case
Safari Khatouni et al. Machine learning based classification accuracy of encrypted service channels: analysis of various factors
Bozkır et al. A new platform for machine-learning-based network traffic classification
Ding et al. Internet traffic classification based on expanding vector of flow
Khatouni et al. How much training data is enough to move a ML-based classifier to a different network?
Raveendran et al. A novel aggregated statistical feature based accurate classification for internet traffic
Raahemi et al. Exploiting unlabeled data to improve peer-to-peer traffic classification using incremental tri-training method