Protecting Against Distributed Denial of Service (DDoS) Attacks Using Distributed Filtering

Trostle, Jonathan

doi:10.1109/seccomw.2006.359548

Cited by 17 publications

(9 citation statements)

References 9 publications

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“…The main objective of this attack is, to take control over resources (time of CPU, bandwidth of network) such that it can't give services to the authorized users. The another objective is achieved by hiding the identity through mimicking legal traffic of web service and creation of a large number of agents for initiating an DDOS attack [3,4]. As a common way of DDoS attack, the attacker hides their identity generally by spoofing the IP address part of a packet header from the victim.…”

Section: Ddos Attacksmentioning

confidence: 99%

Study on Cloud Security Risk and Remedy

Aich¹,

Sen²

2015

IJGDC

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Section: Ddos Attacksmentioning

confidence: 99%

Study on Cloud Security Risk and Remedy

Aich¹,

Sen²

2015

IJGDC

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“…Availability means that a web server should provide service to its authorized clients in all conditions without any interruption. Distributed Denial of Service (DDoS) attacks threat this criterion [1,2]. By finding and compromising a large number of vulnerable unaware users, so called Zombies, through the Internet, a DDoS attacker makes an army to flood a victim server with a large number of spurious packets [3].…”

Section: Introductionmentioning

confidence: 99%

Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection

Kayatas

Fouladi

Ermis

et al. 2018

International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)

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Data availability should be guaranteed by a web service in order to satisfy customers. One of the main challenges of information security professionals is DDoS attack which affects the availability. By masquerading itself as a legitimate user, a DDoS attacker tries to overwhelm a server by sending a great number of useless packets that influences the quality of service (QoS) of the network. DDoS attack can result in a great damage to network services. Useless packets similar to normal ones are dispatched by the attacker which leaves the intrusion detection system impotent of detection. Transferring from conventional packet-based analysis methods to time series based (flow-based) algorithms would be a promising alternative to spot DDoS attacks. In this work, we extract four measures of periodicity, kurtosis, skewness and self-similarity of a time series and investigate the performance of these parameters in separating DDoS attack from normal traffic.

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“…Availability means that a web server should provide service to its authorized clients in all conditions without any interruption. Distributed Denial of Service (DDoS) attacks threat this criterion [1], [2]. By finding and compromising a large number of vulnerable unaware users, so called Zombies, through the Internet, a DDoS attacker makes an army to flood a victim server with a large amount of spurious packets [3].…”

Section: Introductionmentioning

confidence: 99%

Statistical measures: Promising features for time series based DDoS attack detection

Fouladi¹,

Kayatas²,

Anarım³

2018

2018 26th Signal Processing and Communications Applications Conference (SIU)

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Abstract-Data availability should be guaranteed by a web service in order to satisfy customers. One of the main challenges of information security professionals is DDoS attack which affects the availability. By masquerading itself as a legitimate user, a DDoS attacker tries to overwhelm a server by sending numerous amount of useless packets that influences the quality of service (QoS) of the network. DDoS attack can result in a great damage to network services. Useless packets similar to normal ones are dispatched by the attacker which leaves the intrusion detection system impotent of detection. Transferring from conventional packet-based analysis methods to time series based (flow-based) algorithms would be a promising alternative to spot DDoS attacks. In this work, we extract four measures of periodicity, kurtosis, skewness and self similarity of a time series and investigate the performance of these parameters in separating DDoS attack from normal traffic.

show abstract

Protecting Against Distributed Denial of Service (DDoS) Attacks Using Distributed Filtering

Cited by 17 publications

References 9 publications

Study on Cloud Security Risk and Remedy

Study on Cloud Security Risk and Remedy

Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection

Statistical measures: Promising features for time series based DDoS attack detection

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