[go: up one dir, main page]

Meghdouri, 2023 - Google Patents

Machine learning for network traffic analysis: Feature spaces and model optimization

Meghdouri, 2023

View PDF
Document ID
13856486652060122628
Author
Meghdouri F
Publication year

External Links

Snippet

Machine Learning (ML) has revolutionized the field of network traffic analysis and anomaly detection, providing promising and efficient methods for predicting and defending against cyber threats. Traditional systems frequently rely on manual inspection and rule-based …
Continue reading at repositum.tuwien.at (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
    • 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
    • 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
    • H04L63/02—Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
    • H04L63/0209—Architectural arrangements, e.g. perimeter networks or demilitarized zones
    • H04L63/0218—Distributed architectures, e.g. distributed firewalls
    • 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
    • H04L63/02—Network architectures or network communication protocols for network security for separating internal from external traffic, e.g. firewalls
    • H04L63/0227—Filtering policies
    • 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
    • H04L63/1433—Vulnerability analysis
    • 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
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
    • 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
    • H04L63/16—Implementing security features at a particular protocol layer
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L69/00—Application independent communication protocol aspects or techniques in packet data networks
    • H04L69/22—Header parsing or analysis
    • 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
    • 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
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Ge et al. Towards a deep learning-driven intrusion detection approach for Internet of Things
Ahmed et al. Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques--A Comprehensive Survey
Zhao et al. ERNN: Error-resilient RNN for encrypted traffic detection towards network-induced phenomena
Soleimani et al. Real-time identification of three Tor pluggable transports using machine learning techniques
Setitra et al. An efficient approach to detect distributed denial of service attacks for software defined internet of things combining autoencoder and extreme gradient boosting with feature selection and hyperparameter tuning optimization
Magán-Carrión et al. Improving the reliability of network intrusion detection systems through dataset integration
US12401663B2 (en) Stack-HAC for machine learning based botnet detection
Dener et al. RFSE-GRU: Data balanced classification model for mobile encrypted traffic in big data environment
Golchin et al. Sscl-ids: Enhancing generalization of intrusion detection with self-supervised contrastive learning
Dalla et al. Leveraging LSTM for adaptive intrusion detection in IoT networks: a case study on the RT-IoT2022 dataset implemented on CPU computer device machine
Selim et al. DAE-BILSTM: A fog-based intrusion detection model using deep learning for IoT
Meghdouri Machine learning for network traffic analysis: Feature spaces and model optimization
Jarjis et al. Cyber attacks classification on enriching iot datasets
US20250055863A1 (en) System and method for lightweight iot botnet detection based on flow point clustering
Muraleedharan et al. Flow-based machine learning approach for slow HTTP distributed denial of service attack classification
Jaafar et al. Lightweight intrusion detection in MQTT based sensor network
Priambodo et al. Collaborative intrusion detection system with snort machine learning plugin
Jiang et al. Intelligent botnet detection in IoT networks using parallel CNN‐LSTM fusion
Meghdouri et al. Anomaly detection for mixed packet sequences
Golchin Machine learning models in network intrusion detection systems: Self-supervised detection of malicious flows and traffic patterns recognition in programmable networks
Giannuzzi Artificial Intelligence for Security Attacks Detection
Shalini et al. DDoS attack detection in SDN using CUSUM
Okolie et al. Anomaly Detection in Heterogeneous Cybersecurity Data
Rullo et al. Detecting the Invisible Without Knowledge: Unsupervised Packet-based Real-time Detection of Slow DoS Attacks
Qin et al. Bringing intelligence at the network data plane for internet of things security