Advanced Persistent Threat (APT) has become the concern of many enterprise networks. APT can remain unde- tected for a long time span and lead to undesirable consequences such as stealing sensitive data, broken workflow, so on. APTs often use evasion techniques avoid being detected by security systems like Intrusion Detection System (IDS), Security Event Information Management (SIEMs) or firewa...

in this research, the framework is presented for unsupervised change detection using multitemporal sar images based on integration clustering and level set methods. spatial correlation between pixels were considered by using contextual information. also as proposed method was used integration of gustafson-kessel clustering techniques (gkc) and level set methods for change detection. using clust...

Two major challenging issues arise in unsupervised classification. One is how to generate desired knowledge directly from the data in an unsupervised manner. The other is how to find an appropriate follow-up classifier to use the obtained unsupervised knowledge to perform supervised classification. This paper presents a new approach to unsupervised classification for multispectral imagery. To a...

texture image analysis is one of the most important working realms of image processing in medical sciences and industry. up to present, different approaches have been proposed for segmentation of texture images. in this paper, we offered unsupervised texture image segmentation based on markov random field (mrf) model. first, we used gabor filter with different parameters’ (frequency, orientatio...

The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...

We present a tutorial survey on some recent approaches to unsupervised machine learning in the context of statistical pattern recognition. In statistical PR, there are two classical categories for unsupervised learning methods and models: first, variations of Principal Component Analysis and Factor Analysis, and second, learning vector coding or clustering methods. These are the starting-point ...

The paper presents the design of the parameters for long-term municipal rating. Modelling of the rating is realized by means of unsupervised methods, because the rating classes are not known a priori. The model design based on statistical methods (neural networks) is represented by cluster analysis (self-organizing feature maps). Key-Words: Credit risk, rating, unsupervised learning, cluster an...