Intrusion detection is a major research problem in network security. Due to the nonlinear nature of the intrusion attempts, unpredictable behavior of the network traffic and the large number of features in the problem space, intrusion detection systems represent a complicated problem area. Choosing effective and key features for intrusion detection is a very important topic in information secur...

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 numerous features. As a result, in order to get rid of this problem, several types of intrusion detection methods with different levels of accuracy have been proposed which leads the choice of an effective and robust method for...

Our final solution (RMSE=0.8712) consists of blending 107 individual results. Since many of these results are close variants, we first describe the main approaches behind them. Then, we will move to describing each individual result. The core components of the solution are published in our ICDM'2007 paper [1] (or, KDD-Cup'2007 paper [2]), and also in the earlier KDD'2007 paper [3]. We assume th...

We describe our solution for the KDD Cup 2011 track 2 challenge. Our solution relies heavily on ensembling together diverse individual models for the prediction task, and achieved a final leaderboard misclassification rate of 3.8863%. This paper provides details on both the modeling and ensemble

We discuss the challenges of the 2009 KDD Cup along with our ideas and methodologies for modelling the problem. The main stages included aggressive nonparametric feature selection, careful treatment of categorical variables and tuning a gradient boosting machine under Bernoulli loss with trees.

Integration and diversity of IOT terminals their applicable programs make them more vulnerable to many intrusive attacks. Thus, designing an intrusion detection model that ensures the security, integrity, reliability is vital. Traditional technology has disadvantages low rates weak scalability cannot adapt complicated changing environment Internet Things. Hence, one most widely used traditional...

This paper describes a solution to the 2011 KDD Cup competition, Track2: discriminating between highly rated tracks and unrated tracks in a Yahoo! Music dataset. Our approach was to use supervised learning based on 65 features generated using various techniques such as collaborative filtering, SVD, and similarity scoring. During our modeling stage, we created a number of predictors including lo...

The 1998 KDD Data cup provides a large dataset that has a number of features which can be learned to attempt to predict potential respondents to a mailing. It is our goal to show that the naive Bayes classifier may be accurate enough to successfully choose who will reply to the mailing. By using cross validation, we hope to establish a basis for the expected performance. We also analyze the spa...

The theme of the the KDD cup 2011 challenge was to identify user tastes in music by leveraging the actual Yahoo! Music dataset. Two datasets were sampled for the raw data: The larger dataset contained 262,810,175 ratings of 624,961 music items by 1,000,990 users was created for Track1 and and a smaller dataset with 62,551,438 ratings of 296,111 music items by 249,012 was created for Track2. A d...

In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...