Data warehousing

The security of wireless networks has gained considerable importance due to the rapid proliferation of wireless communications. While computer network heuristics and rules are being used to control and monitor the security of Wireless Local Area Networks (WLANs), mining and learning behaviors of network users can provide a deeper level of security analysis. The objective and contribution of this thesis is three fold: exploring the security vulnerabilities of the IEEE 802.11 standard for wireless networks; extracting features or metrics, from a security point of view, for modeling network traffic in a WLAN; and proposing a data mining-based approach to intrusion detection in WLANs. A clustering- and expert-based approach to intrusion detection in a wireless network is presented in this thesis. The case study data is obtained from a real-word WLAN and contains over one million records. Given the clusters of network traffic records, a distance-based heuristic measure is proposed for labeling clusters as either normal or intrusive. The empirical results demonstrate the promise of the proposed approach, laying the groundwork for a clustering-based framework for intrusion detection in computer networks.

This study looks at the database technique of data warehousing and data mining to analyze the business problems related to customer churn in the wireless industry. The customer churn due to new industry regulations has hit the wireless industry hard. The study uses data warehousing and data mining to model the customer database to predict churn rates and suggest timely recommendations to increase customer retention and thereby increase overall profitability. The Naive Bayes algorithm for supervised learning was the prediction algorithm used for data modeling in the study. The data set used in the study consists of one hundred thousand real wireless customers. The study uses database tools such as Oracle database with data mining options and JDeveloper for implementing the models. The data model developed with the calibration data was used to predict the churn for the wireless customers along with the predictive accuracy and probabilities of the results.