Intrusion Detection System (IDS) is an application software which detects the presence of hostile or intrusive elements inside the system. As the nature and type of the intrusions are continuously changing, a simple IDS cannot completely tackle the security threat. In this paper, we have proposed an IDS model, which classifies different types of intrusion attacks based on Stacking classifier. Stacking is an ensemble based classifier. We have achieved good accuracy while classifying the KDD-Cup 99 dataset and that has been achieved with 10 fold cross validation. © 2014 IEEE.

Sanjiban Sekhar Roy

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Krishna P.V

Yenduri S.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Analyzing Intrusion Detection System: An ensemble based stacking approach

2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Customer satisfaction is an important term in business as well as marketing as it surely indicates how well the customer expectations have been met with by the product or the service. Thus a good prediction model for customer satisfaction can help any organization make better decisions with respect to its services and work in a more informed matter to improvise on the same. The problem considered in this study is optimization of customer satisfaction for the customers of San Francisco International Airport. This paper adopts three classification models Naive Bayes, MultiClass Classifier, K-Star and IBK as potential classifiers for prediction of customer satisfaction. The customer satisfaction depends on various factors. The factors which we consider are the user ratings for artwork and exhibitions, restaurants, variety stores, concessions, signage, directions inside SFO, information booths near baggage claim and departure, Wi-Fi, parking facilities, walkways, air train and an overall rating for the airport services. The ratings are obtained from a detailed customer survey conducted by the mentioned airport in 2015. The original survey focused on questions including airlines, destination airport, delays of flights, conveyance to and from the airport, security/immigration etc. but our study focuses on the previously mentioned questions. Graphs are plotted for actual and predicted values and compared to find the least amount of deviation from the actual values. The model which shows least deviation from actual values is considered optimal for the above mentioned problem. © Springer International Publishing AG 2018.

Soft Computing Applications Advances in Intelligent Systems and Computing

Prediction of Customer Satisfaction Using Naive Bayes, MultiClass Classifier, K-Star and IBK

Emails are a very important part of our life today for information sharing. It is used for both personal communication as well as business purposes. But the internet also opens up the prospect of an enormous amount of junk and useless information which overwhelms and irritates us. These unnecessary and unsolicited emails are what comprise of spam. This study presents the application of a classification model to classify spam emails from using a model- Deep Support Vector Machine (Deep SVM). Moreover, other classifier models like Support Vector Machine (SVM), Artificial Neural Network models have also been implemented to compare the performance of proposed Deep SVM model. Furthermore analysis has been done to compare all the performances using available numerical statistics obtained from these models to find the best model for the purpose. Spam filtering is a very essential feature in most email services and thus effective spam classification models are pertinent to the current digital communication scenario and various work has been done in this area. © Springer International Publishing AG 2018.

Spam Email Detection Using Deep Support Vector Machine, Support Vector Machine and Artificial Neural Network

Air pollution is one of the major environmental worries in recent time. Abrupt increase in the concentration of any gas leads to air pollution. The cities are mostly affected due to the abundance of population there. One of the worst gaseous pollutants is OZONE (O3). In this paper, we propose three predictive models for estimation of concentration of ozone gases in the air which are Random Forest, Multivariate Adaptive Regression Splines and Classification and Regression Tree. Evaluation of the prediction models indicates that the Multivariate Adaptive Regression Splines model describes the dataset better and has achieved significantly better prediction accuracy as compared to the Random Forest and Classification and Regression Tree. A detailed comparative study has been carried out on the performances of Random Forest, Multivariate Adaptive Regression Splines and Classification and Regression Tree. MARS gives the result by considering less variables as compared to other two. Moreover, Random Forest takes a little more time for building the tree as the elapsed time was calculated to 45 s in this case. In addition, variable importance for each model has been predicted. Observing all the graphs Multivariate Adaptive Regression Splines gives the closest curve of both train and test set when compared. It can be concluded that multivariate adaptive regression splines can be a valuable tool in predicting ozone for future. © Springer International Publishing AG 2018.

Predicting Ozone Layer Concentration Using Multivariate Adaptive Regression Splines, Random Forest and Classification and Regression Tree

Uncertain data can be dealt with rough set theory and dominance based rough set approach which is an extension to the classical rough set theory is a new mathematical technique to deal with multicriteria indecisive data. Here in this paper we have shown how dominance based rough set approach can be useful for analysis and evaluation of intrusion detection data set. © 2013 Springer.

Advances in Intelligent Systems and Computing Proceedings of International Conference on Advances in Computing

Multicriteria Decision Analysis for Intrusion Detection Data

Network Security

Mitigating denial of service attacks in hierarchical wireless sensor networks

Auto Tech Review

For Magna, India is a Strategic Manufacturing, Engineering Hub

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Quality, Reliability, Security and Robustness in Heterogeneous Networks

Journal	Data powered by Typeset2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Publisher	Data powered by TypesetIEEE
Open Access	0