A modified algorithm for peer-to-peer security

Akleylek, Sedat
Emmungil, Levent
In this paper we present the steganographic approach to peer-to-peer systems with a modified algorithm. This gives the user a very high level of protection against being compelled to disclose its contents. Even the realization of the quantum computer cannot solve NP-hard problem in a polynomial time, a modified algorithm with steganographic use depending on Knapsack problem may make peer-to-peer systems secure.


A neuro-fuzzy MAR algorithm for temporal rule-based systems
Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04)
This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...
A new likelihood approach to autonomous multiple model estimation
Söken, Halil Ersin (Elsevier BV, 2020-04-01)
This paper presents an autonomous multiple model (AMM) estimation algorithm for hybrid systems with sudden changes in their parameters. Estimates of Kalman filters (KFs) that are tuned and employed for different system modes are merged based on a newly defined likelihood function without any necessity for filter interaction. The proposed likelihood function is composed of two measures, the filter agility measure and the steady-state error measure. These measures are derived based on filter adaptation rules....
A temporal neural network model for constructing connectionist expert system knowledge bases
Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01)
This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
A simulated annealing approach to bicriteria scheduling problems on a single machine
Karasakal, Esra (2000-08-01)
In this paper, we apply a simulated annealing approach to two bicriteria scheduling problems on a single machine. The first problem is the strongly NP-hard problem of minimizing total flowtime and maximum earliness. The second one is the NP-hard problem of minimizing total flowtime and number of tardy jobs. We experiment on different neighbourhood structures as well as other parameters of the simulated annealing approach to improve its performance. Our computational experiments show that the developed appro...
A linear approximation for training Recurrent Random Neural Networks
Halıcı, Uğur (1998-01-01)
In this paper, a linear approximation for Gelenbe's Learning Algorithm developed for training Recurrent Random Neural Networks (RRNN) is proposed. Gelenbe's learning algorithm uses gradient descent of a quadratic error function in which the main computational effort is for obtaining the inverse of an n-by-n matrix. In this paper, the inverse of this matrix is approximated with a linear term and the efficiency of the approximated algorithm is examined when RRNN is trained as autoassociative memory.
Citation Formats
S. Akleylek, L. Emmungil, and U. NURİYEV, “A modified algorithm for peer-to-peer security,” APPLIED AND COMPUTATIONAL MATHEMATICS, pp. 258–264, 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67317.