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

Chen et al., 2017 - Google Patents

An effective conversation‐based botnet detection method

Chen et al., 2017

View PDF @Full View
Document ID
13418233668801487511
Author
Chen R
Niu W
Zhang X
Zhuo Z
Lv F
Publication year
Publication venue
Mathematical Problems in Engineering

External Links

Snippet

A botnet is one of the most grievous threats to network security since it can evolve into many attacks, such as Denial‐of‐Service (DoS), spam, and phishing. However, current detection methods are inefficient to identify unknown botnet. The high‐speed network environment …
Continue reading at onlinelibrary.wiley.com (PDF) (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
    • H04L63/1458Denial of Service
    • 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
    • H04L63/145Countermeasures against malicious traffic the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms
    • 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
    • H04L63/1483Countermeasures against malicious traffic service impersonation, e.g. phishing, pharming or web spoofing
    • 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
    • H04L63/1491Countermeasures against malicious traffic using deception as countermeasure, e.g. honeypots, honeynets, decoys or entrapment
    • 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
    • H04L63/1466Active attacks involving interception, injection, modification, spoofing of data unit addresses, e.g. hijacking, packet injection or TCP sequence number attacks
    • 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
    • H04L63/0245Filtering by information in the payload

Similar Documents

Publication Publication Date Title
Chen et al. An effective conversation‐based botnet detection method
Lima Filho et al. Smart detection: an online approach for DoS/DDoS attack detection using machine learning
Ye et al. A DDoS attack detection method based on SVM in software defined network
Idhammad et al. Detection system of HTTP DDoS attacks in a cloud environment based on information theoretic entropy and random forest
Bapat et al. Identifying malicious botnet traffic using logistic regression
Aiello et al. DNS tunneling detection through statistical fingerprints of protocol messages and machine learning
Xu et al. Profiling internet backbone traffic: behavior models and applications
Lin et al. Application classification using packet size distribution and port association
CN101741862B (en) System and method for detecting IRC bot network based on data packet sequence characteristics
Alaidaros et al. An overview of flow-based and packet-based intrusion detection performance in high speed networks
Haddadi et al. Botnet behaviour analysis using ip flows: with http filters using classifiers
Yin Towards Accurate Node‐Based Detection of P2P Botnets
Hung et al. A botnet detection system based on machine-learning using flow-based features
Shanthi et al. Detection of botnet by analyzing network traffic flow characteristics using open source tools
Liu et al. A survey of botnet architecture and batnet detection techniques
Jenefa et al. The ascent of network traffic classification in the dark net: A survey
Nair et al. A study on botnet detection techniques
Rimmer et al. Open-world network intrusion detection
Hsu et al. Detecting Web‐Based Botnets Using Bot Communication Traffic Features
Kostas et al. IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
Stergiopoulos et al. Using side channel TCP features for real-time detection of malware connections
Alizadeh et al. Traffic classification for managing applications’ networking profiles
Mahardhika et al. An implementation of Botnet dataset to predict accuracy based on network flow model
Kheir et al. Behavioral fine-grained detection and classification of P2P bots
Shalini et al. Early detection and mitigation of TCP SYN flood attacks in SDN using chi-square test