Traffic Similarity Observation Using a Genetic Algorithm and Clustering
نویسندگان
چکیده
منابع مشابه
HTTP Traffic Graph Clustering using Markov Clustering Algorithm
Graph-based techniques and analysis have been used for IP network traffic analysis. The objective of this paper is to study the hosts' interaction behavior and use graph clustering algorithm, the Markov clustering algorithm, to group (cluster) hosts which have interaction using the HTTP protocol. Using real network traces, the clustering results show that MCL algorithm successfully group t...
متن کاملA Peer to Peer Traffic Identification Method Using K-means Clustering, Svm & Genetic Algorithm
The utilization of shared (P2P) applications is developing significantly, which brings about a few major issues, for example, the system clog and traffic obstruction. Consequently P2P traffic identification is the most blazing theme of P2P traffic administration. Support vector machine (SVM) has points of interest with settling little examples for P2P characterization issues. However, the execu...
متن کاملA Hybrid Algorithm using Firefly, Genetic, and Local Search Algorithms
In this paper, a hybrid multi-objective algorithm consisting of features of genetic and firefly algorithms is presented. The algorithm starts with a set of fireflies (particles) that are randomly distributed in the solution space; these particles converge to the optimal solution of the problem during the evolutionary stages. Then, a local search plan is presented and implemented for searching s...
متن کاملA Multi-Objective Approach to Fuzzy Clustering using ITLBO Algorithm
Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...
متن کاملA Hybrid Data Clustering Algorithm Using Modified Krill Herd Algorithm and K-MEANS
Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Technologies
سال: 2018
ISSN: 2227-7080
DOI: 10.3390/technologies6040103