Many-to-many relations are often observed between real life objects. When many-tomany relations are between objects in the same class the data mining process becomes more complicated than mining objects when there are no such recursive relations. Mining objects related to other objects in the same class requires construction and execution of recursive queries and hence interpretation of the res...

This paper uses a simple modification of classic Kohonen network (SOM), which allows parallel processing of input data vectors or partitioning the problem in case of insufficient resources (memory, disc space, etc.) to process all input vectors at once. The algorithm has been implemented to meet a specification of modern multicore graphics processors to achieve massive parallelism. The algorith...

An Intrusion detection system (IDS) is a device or a software application that monitors a network or systems for malicious activity or policy violations. Intrusion Detection Systems supports to discover, determine, and identify unauthorized use, duplication, alteration, and destruction of information systems. So, to make intrusion detection system more efficient, the techniques that are more ad...

A machine learning-based approach to the prediction of molecular bioactivity in new drugs is proposed. Two important aspects are considered for the task: feature subset selection and cost-sensitive classification. These are to cope with the huge number of features and unbalanced samples in a dataset of drug candidates. We designed a pattern classifier with such capabilities based on information...

This paper describes the solution method taken by LeBuSiShu team for track1 in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two main challenges: the unique item taxonomy characteristics as well as the large data set size. To handle the item taxonomy, we present a novel method called Matrix Factorization Item Taxonomy Regularization (MFITR). MFITR obtained the 2nd best pr...

In a network system, numbers of systems are interconnected to each other, Inter connected systems examples are cloud computing servers, web servers, database servers etc. Now these networks are under risk of network attackers. In this project we introduce a new implementation for efficient accurate network traffic characterization by separating the geometrical correlation between network traffi...

Detection of malicious connections in computer networks has been a growing problem motivating widespread research in computer science to develop better intrusion detecting systems (IDS). In this paper, we present a machine learning approach known as Genetic Algorithm (GA), to identify such harmful/attack type of connections. The algorithm takes into consideration different features in network c...

An intrusion detection system’s main goal is to classify activities of a system into two major categories: normal and suspicious (intrusive) activities. Intrusion detection systems usually specify the type of attack or classify activities in some specific groups. The objective of this paper is to incorporate several soft computing techniques into the classifying system to detect and classify in...

The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with irrelevant and redundant features. How to choose the effective and key features to IDS is very important topic in information security. Support vector machine (SVM) has been employed to provide potential solutions for the IDS p...

In this paper, we present a dos attack detection system that uses multivariate correlation analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based dos attack detection system employs the principle of anomaly based detection in attack recognition. This makes our solution capable of detecting known and...