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

Feng et al., 2019 - Google Patents

BotFlowMon: Learning-based, content-agnostic identification of social bot traffic flows

Feng et al., 2019

View PDF
Document ID
16594900553755377543
Author
Feng Y
Li J
Jiao L
Wu X
Publication year
Publication venue
2019 IEEE Conference on Communications and Network Security (CNS)

External Links

Snippet

With the fast-growing popularity of online social networks (OSN), maintaining the security of OSN ecosystems becomes essential for the public. Among all the security threats facing OSN, malicious social bots have become the most common and detrimental. These bot …
Continue reading at yebof.github.io (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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • 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
    • 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
    • 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
    • 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
    • H04L12/00—Data switching networks
    • H04L12/54—Store-and-forward switching systems
    • 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
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Orabi et al. Detection of bots in social media: a systematic review
Haddadi et al. Benchmarking the effect of flow exporters and protocol filters on botnet traffic classification
Guerra et al. Datasets are not enough: Challenges in labeling network traffic
Kostas Anomaly detection in networks using machine learning
Ali et al. A review of current machine learning approaches for anomaly detection in network traffic
Feng et al. BotFlowMon: Learning-based, content-agnostic identification of social bot traffic flows
Stergiopoulos et al. Automatic detection of various malicious traffic using side channel features on TCP packets
CN107483488A (en) A kind of malice Http detection methods and system
Ortet Lopes et al. Towards effective detection of recent DDoS attacks: A deep learning approach
Feng et al. Towards learning-based, content-agnostic detection of social bot traffic
Krishnaveni et al. Ensemble approach for network threat detection and classification on cloud computing
Bodström et al. State of the art literature review on network anomaly detection with deep learning
Nicholas et al. Study of long short-term memory in flow-based network intrusion detection system
Mahbooba et al. Trust in intrusion detection systems: an investigation of performance analysis for machine learning and deep learning models
Sheikh et al. Procedures, criteria, and machine learning techniques for network traffic classification: a survey
Silva et al. A statistical analysis of intrinsic bias of network security datasets for training machine learning mechanisms
Catillo et al. Discovery of DoS attacks by the ZED-IDS anomaly detector
Jyothsna et al. Flow based anomaly intrusion detection system using ensemble classifier with Feature Impact Scale
Islam et al. Identifying VoIP traffic in VPN tunnel via flow spatio-temporal features
Abdullah et al. TiSEFE: Time series evolving fuzzy engine for network traffic classification
Latah The art of social bots: A review and a refined taxonomy
Difaizi et al. URL Based Malicious Activity Detection Using Machine Learning
Shinan et al. BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning.
Lindstedt Methods for network intrusion detection: Evaluating rule-based methods and machine learning models on the CIC-IDS2017 dataset
Bian et al. Characterizing network traffic behaviour using granule‐based association rule mining