Distributed denial of service detection using TCP/IP header and traffic measurement analysis

Limwiwatkul, L.; Rungsawang, Arnon

doi:10.1109/iscit.2004.1412917

Cited by 49 publications

(20 citation statements)

References 7 publications

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“…Limwiwatkul's study discovered the DDoS attacking signature by analyzing TCP/IP packet headers against the well-defined rules and conditions, and distinguished the difference between normal and abnormal traffic [5]. The authors could obtain some sample signatures of the basic DDoS attack.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent DDoS Attack Detection System Using Packet Analysis and Support Vector Machine

Kato¹,

Klyuev²

2014

IJICR

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Section: Related Workmentioning

confidence: 99%

An Intelligent DDoS Attack Detection System Using Packet Analysis and Support Vector Machine

Kato¹,

Klyuev²

2014

IJICR

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“…Moreover, it was proven that data does not need to be stored in some cases, as it can be or even has to be processed in an on-line manner. Examples include the statistical detection of DDoS attacks [14] and solutions developed on field-programmable gate array (FPGA) cards [12].…”

Section: Flow Collectionmentioning

confidence: 99%

On the testing of network cyber threat detection methods on spam example

et al. 2014

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As a response to the increasing number of cyber threats, novel detection and prevention methods are constantly being developed. One of the main obstacles hindering the development and evaluation of such methods is the shortage of reference data sets. What is proposed in this work is a way of testing methods detecting network threats. It includes a procedure for creating realistic reference data sets describing network threats and the processing and use of these data sets in testing environments. The proposed approach is illustrated and validated on the basis of the problem of spam detection. Reference data sets for spam detection are developed, analysed and used to both generate the requested volume of simulated traffic and analyse it using machine learning algorithms. The tests take into

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“…SOS network is able to change overlay topology dynamically to avoid DDoS and can survive in case that some key nodes are attacked. In [11], DDoS attacks were discovered by analyzing the TCP packet header against the well-defined rules and conditions and distinguished the difference between normal and abnormal traffic.…”

Section: Related Workmentioning

confidence: 99%

Detection of Application Layer DDOS Attacks Using Information Theory Based Metrics

Renuka Devi

2012

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Distributed denial of service detection using TCP/IP header and traffic measurement analysis

Cited by 49 publications

References 7 publications

An Intelligent DDoS Attack Detection System Using Packet Analysis and Support Vector Machine

An Intelligent DDoS Attack Detection System Using Packet Analysis and Support Vector Machine

On the testing of network cyber threat detection methods on spam example

Detection of Application Layer DDOS Attacks Using Information Theory Based Metrics

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