RED-FT: A Scalable Random Early Detection Scheme with Flow Trust against DoS Attacks

Jiang, Xianliang; Yang, Jiangang; Jin, Guang; Wei, Wei

doi:10.1109/lcomm.2013.022713.122652

Cited by 18 publications

(7 citation statements)

References 7 publications

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“…Currently, LDoS attack detection can be divided into time‐frequency domain detection and feature detection. Literature [5] first proposed a method to detect LDoS attacks in the frequency domain. After autocorrelation of the sampling sequence, the power spectrum density was obtained after discrete Fourier transform, and then the normalised power spectrum density was used as the detection feature for detection.…”

Section: Related Workmentioning

confidence: 99%

LDoS attack detection method based on traffic classification prediction

Liu

Yin

et al. 2021

IET Information Security

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Aiming at the low rate and strong concealment of low‐rate Denial of Service (LDoS) attacks, the calculation of traffic Hurst index is combined with traffic classification, and a machine learning LDoS attack detection method based on search sorting is proposed. The method first calculates the segmentation Hurst exponent of each flow, and constructs a traffic similarity matrix as a statistical feature. Then, using the improved model XGBoost of the Gradient Boosting Decision Tree (GBDT), the traffic is classified and predicted. The network angle distinguishes between normal traffic and abnormal Origin‐Destination (OD) flows containing LDoS attacks, thereby achieving the purpose of detecting LDoS attacks. The method in this study was validated using the US public network dataset Abilene. The experimental results show that the global LDoS attack traffic detection method based on the Hurst index and GBDT algorithm achieves better detection results under different attack rates.

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Section: Related Workmentioning

confidence: 99%

LDoS attack detection method based on traffic classification prediction

Liu

Yin

et al. 2021

IET Information Security

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“…This method highly depends on the AQM model. In , RED flow trusts (RED‐FT), a RED‐like network flow trust scheme, is proposed to detect flooding DoS and LDDoS attacks. RED‐FT monitors data rate and packet loss ratio of each flow in routers and calculates trust ratios for each flow.…”

Section: Related Studiesmentioning

confidence: 99%

A new metric for flow‐level filtering of low‐rate DDoS attacks

Şimşek

2015

Security Comm Networks

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Low-rate distributed denial-of-service (LDDoS) attacks dramatically reduce transmission control protocol throughput by exploiting the vulnerability in the transmission control protocol congestion control mechanism. The current study proposes a new metric called mean Internet Protocol (IP) packet delay variation (mipdv) to detect LDDoS flows and a filtering method called ipdv-based LDDoS filtering (ILF) using mipdv. Receiving first seven packets from a flow is sufficient to calculate the mipdv metric. Subsequently, mipdv can be recalculated for each received packet. This makes the detection of LDDoS flows possible in a short time (in a few tens of milliseconds in most cases). Ns2 simulations were conducted to evaluate the performance of ILF. Experimental results show that ILF detects LDDoS flows in a very short time with very high accuracy.

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“…It would result in difficulties in resisting cyber attacks. Based on the consideration, we proposed flow trust [10] recently to defend against low-rate or flooding DoS attacks, and now employ packet marking techniques to improve the packet classification.…”

Section: A Packet Classification Technologymentioning

confidence: 99%

A New Stateless Packet Classification and Filter against DoS Attacks

Jin

Wang

Jiang

2014

JNW

Self Cite

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Capabilities is a typical scheme of stateless filtering. In order to classify and filter packets effectively, a novel scheme of packet classification and filter based on capabilities is proposed in this paper. In our scheme, a new classifier module is added and a new filter structure is designed. We employ capabilities as verification and introduce new authorization in the communications. All these innovations make packet classification owning good effects in attacking scenario. The experimental results based on large-scale topology datasets and NS2 show that our scheme is better than traditional packet classification algorithms, especially under complex cyber environment.

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RED-FT: A Scalable Random Early Detection Scheme with Flow Trust against DoS Attacks

Cited by 18 publications

References 7 publications

LDoS attack detection method based on traffic classification prediction

LDoS attack detection method based on traffic classification prediction

A new metric for flow‐level filtering of low‐rate DDoS attacks

A New Stateless Packet Classification and Filter against DoS Attacks

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