Visualize Network Anomaly Detection by Using K-Means Clustering Algorithm
نویسندگان
چکیده
منابع مشابه
Visualize Network Anomaly Detection by Using K-means Clustering Algorithm
With the ever increasing amount of new attacks in today’s world the amount of data will keep increasing, and because of the base-rate fallacy the amount of false alarms will also increase. Another problem with detection of attacks is that they usually isn’t detected until after the attack has taken place, this makes defending against attacks hard and can easily lead to disclosure of sensitive i...
متن کاملTraffic Anomaly Detection Using K-Means Clustering
Data mining techniques make it possible to search large amounts of data for characteristic rules and patterns. If applied to network monitoring data recorded on a host or in a network, they can be used to detect intrusions, attacks and/or anomalies. This paper gives an introduction to Network Data Mining, i.e. the application of data mining methods to packet and flow data captured in a network,...
متن کاملPersistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm
Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...
متن کاملAn Advanced Moving Object Detection Using K-Means Clustering Algorithm
In this paper, we present a comparative study of several state of the art background subtraction methods. Approaches ranging from simple background subtraction with global thresholding to more sophisticated statistical methods have been implemented and tested on different videos with ground truth. The goal of this study is to provide a solid analytic ground to underscore the strengths and weakn...
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: International journal of Computer Networks & Communications
سال: 2013
ISSN: 0975-2293,0974-9322
DOI: 10.5121/ijcnc.2013.5514