A survey of network flow applications

Li, Bingdong; Springer, Jeff; Bebis, George; Güneş, Mehmet Hadi

doi:10.1016/j.jnca.2012.12.020

Cited by 218 publications

(124 citation statements)

References 116 publications

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“…In the corresponding PAproblem, we have arcs (x; y) 1 and (z; x) 1 with (cost, lower, upper) = (-1,0,1), arc (y; z) 1 with (cost, lower, upper) = (-1,0,3), arcs (x; y) 2 , (y; z) 2 , and (z; x) 2 with (cost, lower, upper) = (0,0,1), and arcs (x; y) 3 , (y; z) 3 , and (z; x) 3 with (cost,lower,upper) = (1; 0; 1). Then, we get a feasible ow by assigning ow of 1 unit on arcs (x; y) 1 , (x; y) 2 , (z; x) 1 , and (z; x) 2 , and 2 units on (y; z) 1 . The total cost is -4, which is minimum.…”

Section: Goldberg and Tarjan's Algorithmmentioning

confidence: 99%

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Adjusting an Infeasible Network by Minimizing the Sum of the Violation Costs

Ghiyasvand

2017

Scientia Iranica

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KEYWORDS Infeasible network;The cost scaling algorithm; The minimum cost ow problem; Parallel arcs; Convex cost.Abstract. In this paper, for a given infeasible network, we change the lower and upper bounds, such that the sum of the violations costs from the lower and upper bounds is minimum. We call this problem as the adjusting problem and show that it is transformed to a minimum cost ow problem on a special parallel network. Thus, the adjusting problem is solved using minimum cost ow algorithms. Solving a minimum cost ow problem with parallel arcs, in practice, is complicated and needs more time in comparison with a minimum cost ow problem without parallel arcs. If the parallel arcs are eliminated, then, we achieve substantial saving in the storage requirements, which translates into enhanced speed of algorithms. One of the best algorithms to solve the minimum cost ow problem is the cost scaling algorithm of Goldberg and Tajan (1990). In this paper, we present two modi ed versions of their algorithm to solve the adjusting problem. In the rst modi cation, in order to achieve an enhanced speed of algorithm, the parallel arcs are eliminated using an especial residual network. In the second modi cation, the adjusting problem is transformed to a convex cost ow problem, and the cost scaling algorithm is modi ed in a way which performs fewer operations than our rst implementation.

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Section: Goldberg and Tarjan's Algorithmmentioning

confidence: 99%

“…Such problems often involve few indivisible objects, and this leads to a nite set of feasible solutions. Li et al [1] presented a good survey of network ow applications. Some recent applications of network ows problem have been presented in [2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Adjusting an Infeasible Network by Minimizing the Sum of the Violation Costs

Ghiyasvand

2017

Scientia Iranica

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“…To combat this, some researchers have turned to network flow applications (such as NetFlow, sFlow, OpenFlow and IPFIX) to cut down on data overheads with filtering, sampling, and flow aggregation techniques [55].…”

Section: Network Flow and Feature Selectionmentioning

confidence: 99%

A survey of cloud-based network intrusion detection analysis

Keegan

Chaudhary

et al. 2016

Hum. Cent. Comput. Inf. Sci.

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As network traffic grows and attacks become more prevalent and complex, we must find creative new ways to enhance intrusion detection systems (IDSes). Recently, researchers have begun to harness both machine learning and cloud computing technology to better identify threats and speed up computation times. This paper explores current research at the intersection of these two fields by examining cloud-based network intrusion detection approaches that utilize machine learning algorithms (MLAs). Specifically, we consider clustering and classification MLAs, their applicability to modern intrusion detection, and feature selection algorithms, in order to underline prominent implementations from recent research. We offer a current overview of this growing body of research, highlighting successes, challenges, and future directions for MLA-usage in cloud-based network intrusion detection approaches.

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“…Network management and intrusion detection systems heavily rely on automatically identifying communication behaviors [1], [2]. To keep up with new malware and changing usage patterns, models and detection rules must be updated regularly.…”

Section: Introductionmentioning

confidence: 99%

Behavioral clustering of non-stationary IP flow record data

Christian

Marchal

State

et al. 2016

2016 12th International Conference on Network and Service Management (CNSM)

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Abstract-Automated network traffic analysis using machine learning techniques plays an important role in managing networks and IT infrastructure. A key challenge to the correct and effective application of machine learning is dealing with non-stationary learning data sources and concept drift. Traffic evolves overtime due to new technology, software, services being used, changes in user behavior but also due to changes in network graphs like dynamic IP address assignment. In this paper, we present an automatic online method to detect changepoints in network traffic based on IP flow record analysis. This technique is used to segment an observed behavior into smaller consecutive behaviors differing one from another. The segmented traffic is used to learn small communication profile characterizing accurately the activities present between two observed changepoints. We validate our method using synthetic data and outline a real-world application to botnet hosts behavior modeling.

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A survey of network flow applications

Cited by 218 publications

References 116 publications

Adjusting an Infeasible Network by Minimizing the Sum of the Violation Costs

Adjusting an Infeasible Network by Minimizing the Sum of the Violation Costs

A survey of cloud-based network intrusion detection analysis

Behavioral clustering of non-stationary IP flow record data

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