6154835
Microarray Probe Design Using epsilon-Multi-Objective Evolutionary Algorithms with Thermodynamic Criteria
As DNA microarrays have been widely used for gene expression profiling and other fields, the importance of reliable probe design for microarray has been highlighted. First, the probe design for DNA microarray was formulated as a constrained multi-objective optimization task by investigating the characteristics of probe design. Then the probe set for human paillomavrius (HPV) was found using e-multi-objective evolutionary algorithm with thermodynamic fitness calculation. The evolutionary optimization of probe set showed better results than the commercial microarray probe set made by Biomedlab Co. Korea.
18729972
Temperature Gradient-Based DNA Computing for Graph Problems with Weighted Edges
We propose an encoding method of numerical data in DNA using temperature gradient. We introduce melting temperature (Tm) for this purpose. Melting temperature is a unique characteristic to manipulate the hybridization and denaturation processes that used in the key steps in DNA computing such as the solution generation step and the amplification step. DNA strands of lower melting temperature tend to denature with ease and also be easily amplified by slightly modified polymerase chain reaction, called denaturation temperature gradient polymerase chain reaction. Using these properties, we implement a local search molecular algorithm using temperature gradient, which is contrasted to conventional exhaustive search molecular algorithms. The proposed methods are verified by solving an instance of the travelling salesman problem. We could effectively amplify the correct solution and the use of temperature gradient made the detection of solutions easier.
8056566
Bayesian Evolutionary Optimization Using Helmholtz Machines
Recently, several evolutionary algorithms have been proposed that build and use an explicit distribution model of the population to perform optimization. One of the main issues in this class of algorithms is how to estimate the distribution of selected samples. In this paper, we present a Bayesian evolutionary algorithm (BEA) that learns the sample distribution by a probabilistic graphical model known as Helmholtz machines. Due to the generative nature and availability of the wake-sleep learning algorithm, the Helmholtz machines provide an effective tool for modeling and sampling from the distribution of selected individuals. The proposed method has been applied to a suite of GA-deceptive functions. Experimental results show that the BEA with the Helmholtz machine outperforms the simple genetic algorithm.
14659614
NACST/Seq: A Sequence Design System with Multiobjective Optimization
The importance of DNA sequence design for reliable DNA computing is well recognized. In this paper, we describe a DNA sequence optimization system NACST/Seq that is based on a multiobjective genetic algorithm. It uses the concept of Pareto optimization to reflect many realistic characteristics of DNA sequences in real bio-chemical experiments flexibly. This feature allows to recommend multiple candidate sets as well as to generate the DNA sequences, which fit better to a specific DNA computing algorithm. We also describe DNA sequence analyzer that can examine and visualize the properties of given DNA sequences.
41720714
LSAD: Lightweight SYN Flooding Attack Detector
Currently, there are lots of approaches to detect SYN flooding, but they require too many resources to manage most of ongoing traffic We propose a simple and robust approach to detect SYN flooding attacks by observing essential network information Instead of managing all ongoing traffic on the network, our approach only monitors SYN count and ratio between SYN and other TCP packets To make the detection mechanism robustly and easily, we use EWMA (exponentially weight moving average) approach in SPC (statistical process control) [3] [10] [11] It makes the detection mechanism much more generally applicable and easier to implement The trace-driven simulation results demonstrate that our proposal is efficient and simple to implement and prove that it detects SYN flooding accurately and finds attack in a very short detection time.
34432262
Web Services
13187779
D-SAT: detecting SYN flooding attack by two-stage statistical approach
We propose D-SAT (detecting SYN flooding attack by two-stage statistical approach) system that is simple and robust approach to detect SYN flooding attacks by observing network traffic. Instead of managing all ongoing traffic on the network, D-SAT only monitors SYN count and ratio between SYN and other TCP packets at first time. And it detects SYN flooding and finds victims more accurately in its second stage. To make the detection mechanism robustly and easily, D-SAT uses CUSUM (cumulative sum) approach in SPC (statistical process control) (H. Wang et al., 2002) (D.C. Montgomery, 2001) (D.M. Hawkins et al., 1998). It makes the detection mechanism much more generally applicable and easier to implement. D-SAT also employed AFM (aggregation flow management) for finding victims quickly and accurately. The trace-driven simulation results demonstrate that D-SAT system is efficient and simple to implement and prove that it detects SYN flooding accurately and finds attack in a very short detection time.
15631215
Global software piracy revisited
Determining why the roots of the disparity in national piracy levels lie beyond economics.