Network attacks: Taxonomy, tools and systems

Hoque, Nazrul; Bhuyan, Monowar H.; Baishya, Ram Charan; Bhattacharyya, Dhruba K.; Kalita, Jugal

doi:10.1016/j.jnca.2013.08.001

Cited by 185 publications

(114 citation statements)

References 40 publications

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“…The new network is a parallel network, which each arc (i; j) on the original network is replaced with three arcs (i; j) 1 , (i; j) 2 , and (i; j) 3 on the parallel network (see Figure 1). Consider the minimum cost ow problem on the parallel network; we call it the parallel adjusting problem or the PA-problem.…”

Section: Goldberg and Tarjan's Algorithmmentioning

confidence: 99%

“…Let x be an optimal ow for the PA-problem and x ij (1), x ij (2), and x ij (3) be the ows on arcs (i; j) 1 , (i; j) 2 , and (i; j) 3 , respectively. For each arc (i; j), de ne:…”

Section: Goldberg and Tarjan's Algorithmmentioning

confidence: 99%

“…So, the adjustment cost is 1. 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 .…”

Section: Goldberg and Tarjan's Algorithmmentioning

confidence: 99%

“…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%

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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%

“…Let x be an optimal ow for the PA-problem and x ij (1), x ij (2), and x ij (3) be the ows on arcs (i; j) 1 , (i; j) 2 , and (i; j) 3 , respectively. For each arc (i; j), de ne:…”

Section: Goldberg and Tarjan's Algorithmmentioning

confidence: 99%

Section: Goldberg and Tarjan's Algorithmmentioning

confidence: 99%

“…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%

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

Ghiyasvand

2017

Scientia Iranica

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“…SOMs assume that there is some topology or ordering among the input objects and that the units will eventually take on this structure in space. Design and implement systems based on SOM clustering method to detect and classify DDoS attack has been broadly used in the recent topics (Kumar and Selvakumar, 2011;Dusan et al, 2012), (Hoque et al, 2013).…”

Section: Other Detection Schemesmentioning

confidence: 99%

Review Clustering Mechanisms of Distributed Denial of Service Attacks

Bhaya¹,

Manaa²

2014

Journal of Computer Science

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Distributed Denial of Service attacks (DDoS) overwhelm network resources with useless or harmful packets and prevent normal users from accessing these network resources. These attacks jeopardize the confidentiality, privacy and integrity of information on the internet. Since it is very difficult to set any predefined rules to correctly identify genuine network traffic, an anomaly-based Intrusion Detection System (IDS) for network security is commonly used to detect and prevent new DDoS attacks. Data mining methods can be used in intrusion detection systems, such as clustering k-means, artificial neural network. Since the clustering methods can be used to aggregate similar objects, they can detect DDoS attacks to reduce false-positive rates. In this study, a review of DDoS attacks using clustering data mining techniques is presented. A review illustrates the most recent, state-of-the art science for clustering techniques to detect DDoS attacks.

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