Natural Computing Algorithms

Brabazon, Anthony; O’Neill, Michael; McGarraghy, Seán

doi:10.1007/978-3-662-43631-8

Cited by 87 publications

(30 citation statements)

References 359 publications

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“…The security of information transfer on internet and the cryptography need random generators [26]. Randomness and Random generators are used extensively in solving hard problems in the framework of stochastic optimization and naturally inspired algorithm in order to bypass the problem of getting stuck in local extremes [27]- [29].…”

Section: Random Generatormentioning

confidence: 99%

An Information Theoretic Analysis of Random Number Generator based on Cellular Automaton

Nayyeri¹,

Dastghaibyfard²

2018

ijacsa

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Abstract-Realization of Randomness had always been a controversial concept with great importance both from theoretical and practical Perspectives. This realization has been revolutionized in the light of recent studies especially in the realms of Chaos Theory, Algorithmic Information Theory and Emergent behavior in complex systems. We briefly discuss different definitions of Randomness and also different methods for generating it. The connection between all these approaches and the notion of Normality as the necessary condition of being unpredictable would be discussed. Then a complex-system-based Random Number Generator would be introduced. We will analyze its paradoxical features (Conservative Nature and reversibility in spite of having considerable variation) by using information theoretic measures in connection with other measures. The evolution of this Random Generator is equivalent to the evolution of its probabilistic description in terms of probability distribution over blocks of different lengths. By getting the aid of simulations we will show the ability of this system to preserve normality during the process of coarse graining.

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Section: Random Generatormentioning

confidence: 99%

An Information Theoretic Analysis of Random Number Generator based on Cellular Automaton

Nayyeri¹,

Dastghaibyfard²

2018

ijacsa

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“…Eight numbers of hidden units are considered to collect the average of accuracy, the average of loss, the training time and the testing time of the ADDM. The numbers of hidden units used are 3,4,5,6,7,8,9, and 10. Table 3 summarizes the obtained results for each number of hidden units.…”

Section: B Optimum Pcc Threshold Selectionmentioning

confidence: 99%

“…In this paper we present a DoS detection method based on Artificial Neural Networks (ANN) [4], [5]. The proposed method is a victim-end solution in which a Feed-forward Neural Network (FNN) [6], [7] is used to classify the incoming network traffic into DoS or normal. The simplicity of the proposed method design which consists of three layers (input, hidden and output layers) allows to detect DoS attack with minimum resources usage.…”

Section: Introductionmentioning

confidence: 99%

DoS Detection Method based on Artificial Neural Networks

Idhammad¹,

Afdel²,

Belouch³

2017

ijacsa

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Abstract-DoS attack tools have become increasingly sophisticated challenging the existing detection systems to continually improve their performances. In this paper we present a victimend DoS detection method based on Artificial Neural Networks (ANN). In the proposed method a Feed-forward Neural Network (FNN) is optimized to accurately detect DoS attack with minimum resources usage. The proposed method consists of the following three major steps: (1) Collection of the incoming network traffic, (2) selection of relevant features for DoS detection using an unsupervised Correlation-based Feature Selection (CFS) method, (3) classification of the incoming network traffic into DoS traffic or normal traffic. Various experiments were conducted to evaluate the performance of the proposed method using two public datasets namely UNSW-NB15 and NSL-KDD. The obtained results are satisfactory when compared to the state-of-the-art DoS detection methods.

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“…Ants are a diverse taxon of more than 14,000 species that have also evolved distributed algorithms to establish trail networks [4]. Investigating the algorithms used by biological systems can reveal novel solutions to engineering problems [5,3].…”

Section: Introductionmentioning

confidence: 99%

A distributed algorithm to maintain and repair the trail networks of arboreal ants

Chandrasekhar

Gordon

Navlakha

2017

Preprint

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We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests together. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, to describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why the algorithm succeeds under certain conditions. The turtle ant algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

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Natural Computing Algorithms

Cited by 87 publications

References 359 publications

An Information Theoretic Analysis of Random Number Generator based on Cellular Automaton

An Information Theoretic Analysis of Random Number Generator based on Cellular Automaton

DoS Detection Method based on Artificial Neural Networks

A distributed algorithm to maintain and repair the trail networks of arboreal ants

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