Intrusion Detection System (IDS) is a computer-based data system which purports to observe attacks against computer systems and networks or, against any information system. Its job is to supervise the utilization of such system to detect any insecure states. IDS detect attempts and active misuse of the scheme either by lawful users of the information systems or by outside parties to abuse privi...

Information security is becoming a more important issue in modern computer system. Intrusion Detection System (IDS) as the main security defensive technique that can effectively expand the scope of defense against network intrusion. Data Mining and Machine Learning techniques proved useful and attracted increasing attention in the network intrusion detection research area. Recently, many machin...

In increasing trends of network environment every one gets connected to the system. So there is need of securing information, because there are lots of security threats are present in network environment. A number of techniques are available for intrusion detection. Data mining is the one of the efficient techniques available for intrusion detection. Data mining techniques may be supervised or ...

This paper deals with the problem of machine learning on sets of documents connected into graphs. Our strategy is to represent each document by a diverse set of heterogeneous attributes, including traditional binary and categorical attributes, textual attributes, and attributes derived from the graphs. We present experiments on two datasets, showing the usefulness of graph-based attributes and ...

This paper is devoted to the problem of identification of network attacks via traffic analysis. Neural networks are chosen by us due to their capability to recognize an attack, to differentiate one attack from another, i.e. classify attacks, and, most important, to detect new attacks that were not included into the training set. Performed experiments demonstrate the advantage of our intrusion d...

In this paper we present an unsupervised approach to Query Classification. The approach exploits the Wikipedia encyclopedia as a corpus and the statistical distribution of terms, from both the category labels and the query, in order to select an appropriate category. We have created a classifier that works with 55 categories extracted from the search section of the Bridgeman Art Library website...

Network Security Situational Awareness(NSSA) has been a hot research in the network security domain. Because of the large amount of Intrusion Detection System (IDS), We propose a new method of data preprocessing for NSSA based on conditional random fields(CRFs). It takes advantages of the CRFs models which can stitch to sequence data marking and add random attributes to deal with the amount of ...

We present the ideas and methodologies that we used to address the KDD Cup 2009 challenge on rank-ordering the probability of churn, appetency and up-selling of wireless customers. We choose stochastic gradient boosting tree (TreeNet R ) as our main classifier to handle this large unbalanced dataset. In order to further improve the robustness and accuracy of our results, we bag a series of boos...

This paper provides the solution of the team “commendo” on the Track1 dataset of the KDD Cup 2011 Dror et al.. Yahoo Labs provides a snapshot of their music-rating database as dataset for the competition. We get approximately 260 million ratings from 1 million users on 600k items. Timestamp and taxonomy information are added to the ratings. The goal of the competition was to predict unknown rat...

Precise recommendation of followers helps in improving the user experience and maintaining the prosperity of twitter and microblog platforms. In this paper, we design a hybrid recommender system of microblog as a solution of KDD Cup 2012, track 1 task, which requires predicting users a user might follow in Tencent Microblog. We describe the background of the problem and present the algorithm co...