Abstract- With the advancement and development of computer network technologies, the way for intruders has become smoother; therefore, to detect threats and attacks, the importance of intrusion detection systems (IDS) as one of the key elements of security is increasing. One of the challenges of intrusion detection systems is managing of the large amount of network traffic features. Removing un...

Intrusion detection systems (IDS) by exploiting Machine learning techniques are able to diagnose attack traffics behaviors. Because of relatively large numbers of features in IDS standard benchmark dataset, like KDD CUP 99 and NSL_KDD, features selection methods play an important role. Optimization algorithms like Genetic algorithms (GA) are capable of finding near-optimum combination of the fe...

For the purpose of improving real time and profiles accuracy, a parallel anomaly detection algorithm based on hierarchical clustering has been proposed. Training and predicting are two busiest processes and they are parallel designed and implemented. Moreover, an abnormal cluster feature tree is built to dig anomalies from normal profiles. A series of experiment results on wellknown KDD Cup 199...

The SIGMOD conference organized by the ACM Special Interest Group on Management of Data is one of the major conferences in the database area. Since databases play an important role in knowledge discovery, the SIGMOD conference is also an important forum for researchers in the KDD area, especially with respect to aspects of KDD which deal with very large data sets. At this years SIGMOD conferenc...

We present our overall third ranking solution for the KDD Cup 2010 on educational data mining. The goal of the competition was to predict a student’s ability to answer questions correctly, based on historic results. In our approach we use an ensemble of collaborative filtering techniques, as used in the field of recommender systems and adopt them to fit the needs of the competition. The ensembl...

In October, 2006 Netflix released a dataset containing 100 million anonymous movie ratings and challenged the data mining, machine learning and computer science communities to develop systems that could beat the accuracy of its recommendation system, Cinematch. We briefly describe the challenge itself, review related work and efforts, and summarize visible progress to date. Other potential uses...

Wikipedia’s category graph is a network of 400,000 interconnected category labels, and can be a powerful resource for many classification tasks. However, its size and the lack of order can make it difficult to navigate. In this paper, we present a new algorithm to efficiently explore this graph and discover accurate classification labels. We implement our algorithm as the core of a query classi...

Data Mining is a technique to drilling the database for giving meaning to the approachable data. It involves systematic analysis of large data sets. And the classification is used to manage data, sometimes tree modeling of data helps to make predictions about new data. Recently, we have increasing in the number of cyber-attacks, detecting the intrusion in networks become a very tough job. In Ne...