Monitoring the Application-Layer DDoS Attacks for Popular Websites

Xie, Yi; Shu, Yu

doi:10.1109/tnet.2008.925628

Cited by 212 publications

(10 citation statements)

References 18 publications

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“…Many security experts and academic studies have highlighted how DDoS attacks can be executed through various vectors [4], such as volumetric attacks [5], protocol attacks, and application layer attacks [6], [7]. Examining the diversity of these vectors is crucial for understanding the full range of tactics used by threat actors to the extent where they cause disruption.…”

Section: Background Literature a Attack Vector Diversitymentioning

confidence: 99%

2019–2023 in Review: Projecting DDoS Threats With ARIMA and ETS Forecasting Techniques

Falowo,

Abdo

2024

IEEE Access

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Section: Background Literature a Attack Vector Diversitymentioning

confidence: 99%

2019–2023 in Review: Projecting DDoS Threats With ARIMA and ETS Forecasting Techniques

Falowo,

Abdo

2024

IEEE Access

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“…In addition to more accurate estimates of the required throughput, the stability and reproducibility of the dynamic rank-size statistics for very short 1 s duration fragments could be employed also in a simple scalable model of normal network traffic to be used as null hypothesis during anomaly detection. Considering recent evidence of significant changes in the rank distributions including their deviation from the expected Zipf's law in the presence of anomalies at application level [29][30][31], similar anomalies in access patterns at different network levels may be of interest for early detection and localization of suspicious and unauthorized network activity such as DDoS attacks. Finally, we believe that similar principles could be used to model other complex systems that exhibit similar superstatistical organization with long-range correlations in the intensity variations, such as biopolymers [24,32], finance [33,34], precipitation [27], seismicity [35], see also [36].…”

Section: Long-term Correlations In User Activity Patternsmentioning

confidence: 99%

Universal rank-size statistics in network traffic: Modeling collective access patterns by Zipf's law with long-term correlations

Nguyen¹,

Markelov²,

Serdyuk³

et al. 2018

EPL

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We analyze network traffic rank-size statistics at different levels and organization. Our results support the emergence of Zipf's law in the rank-size traffic distributions by time, source and destination. The corresponding empirical laws considering typical discreteness and finite-size effects can be well approximated by q-exponential distributions for external IPs as well as by β-and Γ-distributions for internal LAN IPs and time fragments, respectively. Once appropriately normalized, the observed rank-size statistics exhibit rather universal shapes that are well reproduced by nonextensive entropy maximization algorithm for finite system sizes and can be used to model typical network activity patterns for a given community with a given number of active nodes as a sole free parameter.

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“…Be that as it may, the vast majority of them have not met the prerequisites of location in the overwhelming movement condition. For instance, Yi Xie et al received a shrouded semi-Markov procedure to show the conduct of Web clients [1,3]. The concealed semi-Markov approach is an intricate calculation.…”

Section: Motivationsmentioning

confidence: 99%

Network anomaly detection for protecting web services from the application layer bandwidth flooding attack

Raghavender

Premchand²

2018

IJET

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Web servers are generally situated in an efficient server center where these servers associate with the outside Web straightforwardly through spines. In the interim, the application layer Bandwidth flooding attack (ALBFA) assaults are basic dangers to the Web, especially to those business web servers. As of now, there are a few strategies intended to deal with the ALBFA assaults, however the greater part of them can't be utilized as a part of substantial spines. In this paper, we propound another technique namely BFADM to identify ALBFA assaults. Our work separates itself from past techniques by considering ALBFA assault discovery in overwhelming spine movement. Moreover, the recognition of ALBFA assaults is effortlessly deceived by streak swarm activity. Keeping in mind the end goal to beat this issue, our propounded technique develops a Constant Recurrence Vector and genuine opportune describes the movement as an arrangement of models. By looking at the entropy of ALBFA assaults and blaze swarms, these models can be utilized to perceive the genuine ALBFA assaults. We coordinate the above discovery standards into a modularized resistance design, which comprises of a head-end sensor, an identification module and an activity channel. With a quick ALBFA discovery speed, the channel is equipped for letting the true blue demands through however the assault movement is ceased.

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Monitoring the Application-Layer DDoS Attacks for Popular Websites

Cited by 212 publications

References 18 publications

2019–2023 in Review: Projecting DDoS Threats With ARIMA and ETS Forecasting Techniques

2019–2023 in Review: Projecting DDoS Threats With ARIMA and ETS Forecasting Techniques

Universal rank-size statistics in network traffic: Modeling collective access patterns by Zipf's law with long-term correlations

Network anomaly detection for protecting web services from the application layer bandwidth flooding attack

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