Tracking Per-Flow State – Binned Duration Flow Tracking

Whitehead, Brad; Lung, Chung-Horng; Rabinovitch, Peter

doi:10.4304/jnw.7.1.37-51

Cited by 7 publications

(5 citation statements)

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“…Correspondingly, there is not much work dedicated to investigate the stability of impulsive discrete-time cellular neural networks. Recently, some mathematical models of impulsive cellular neural networks described by measure differential equations or general impulsive differential equations have been formulated ( [1], [2], [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]). Some interesting results on the stability of impulsive discretetime cellular neural networks without delay or with constant delays have been obtained.…”

Section: Introductionmentioning

confidence: 99%

Stability of Impulsive Cellular Neural Networks with Time-varying Delays

Chen¹

2013

JNW

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The problems of exponential stability and exponential convergence rate for a class of impulsive cellular neural networks with time-varying delays are studied. By means of the Lyapunov stability theory and discrete-time Halanay-type inequality technique, stability criteria for ensuring global exponential stability of no-impulsive discrete-time cellular neural networks and impulsive discrete-time cellular neural network with time-varying delays are derived respectively, and the estimated exponential convergence rate is provided as well. Finally, the validity of the obtained results is shown by two numerical examples.

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Section: Introductionmentioning

confidence: 99%

Stability of Impulsive Cellular Neural Networks with Time-varying Delays

Chen¹

2013

JNW

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“…Bonomi et al [67] propose to use a flag to distinguish between recently used and expired entries, parsing all the table at regular intervals. Binned Duration Flow Tracking [68] divides the hash tables in a fixed number of ordered bins, across which the flows are swapped. When space is needed, the oldest bin is deleted.…”

Section: B Flow Ageingmentioning

confidence: 99%

High-speed Connection Tracking in Modern Servers

Girondi

Chiesa

Barbette

2021

2021 IEEE 22nd International Conference on High Performance Switching and Routing (HPSR)

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The rise of commodity servers equipped with highspeed network interface cards poses increasing demands on the efficient implementation of connection tracking, i.e., the task of associating the connection identifier of an incoming packet to the state stored for that connection. In this work, we thoroughly investigate and compare the performance obtainable by different implementations of connection tracking using high-speed real traffic traces. Based on a load balancer use case, our results show that connection tracking is an expensive operation, achieving at most 24 Gbps on a single core. Core-sharding and lock-free hash tables emerge as the only suitable multi-thread approaches for enabling 100 Gbps packet processing. In contrast to recent beliefs, we observe that newly proposed techniques to "lazily" delete connection states are not more effective than properly tuned traditional deletion techniques based on timer wheels.Index Terms-Connection tracking, load balancer, high-speed networking, multi-core processing, hash table

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“…Brauckhoff et al (2009) used three weeks of NetFlow data from August 2007 by aggregating the traffic at 15 minute intervals. Alternatively, Whitehead, Lung, and Rabinovitch (2010) show that there exists different ways of time binning data which offer good performance for tracking per-flow data. It presented an alternative to NetFlow.…”

Section: Comparison With Related Workmentioning

confidence: 99%

A Hybrid Technique Using PCA and Wavelets in Network Traffic Anomaly Detection

Novakov

Lung

Lambadaris

et al. 2014

International Journal of Mobile Computing and Multimedia Communications

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Research into network anomaly detection has become crucial as a result of a significant increase in the number of computer attacks. Many approaches in network anomaly detection have been reported in the literature, but data or solutions typically are not freely available. Recently, a labeled network traffic flow dataset, Kyoto2006+, has been created and is publicly available. Most existing approaches using Kyoto2006+ for network anomaly detection apply various clustering techniques. This paper leverages existing well known statistical analysis and spectral analysis techniques for network anomaly detection. The first popular approach is a statistical analysis technique called Principal Component Analysis (PCA). PCA describes data in a new dimension to unlock otherwise hidden characteristics. The other well known spectral analysis technique is Haar Wavelet filtering analysis. It measures the amount and magnitude of abrupt changes in data. Both approaches have strengths and limitations. In response, this paper proposes a Hybrid PCA–Haar Wavelet Analysis. The hybrid approach first applies PCA to describe the data and then Haar Wavelet filtering for analysis. Based on prototyping and measurement, an investigation of the Hybrid PCA–Haar Wavelet Analysis technique is performed using the Kyoto2006+ dataset. The authors consider a number of parameters and present experimental results to demonstrate the effectiveness of the hybrid approach as compared to the two algorithms individually.

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Tracking Per-Flow State – Binned Duration Flow Tracking

Cited by 7 publications

References 0 publications

Stability of Impulsive Cellular Neural Networks with Time-varying Delays

Stability of Impulsive Cellular Neural Networks with Time-varying Delays

High-speed Connection Tracking in Modern Servers

A Hybrid Technique Using PCA and Wavelets in Network Traffic Anomaly Detection

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