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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4144

Title: CONSTRUCTING PREDICTIVE MODEL FOR NETWORK INTRUSION DETECTION
Authors: TIGABU, DAGNE
Advisors: Dr. Gashaw Kebede.
Keywords: Information science
Copyright: Jun-2012
Date Added: 29-Nov-2012
Publisher: AAU
Abstract: While advances in computer and communications technology have made the network ubiquitous, they have also rendered networked systems vulnerable to malicious attacks devised from a distance. These attacks or intrusions start with attackers infiltrating a network through a vulnerable host and then launching further attacks on the local network or Intranet. Nowadays, system administrators and network professionals can attempt to prevent such attacks by developing intrusion detection tools and systems using data mining technology. In this study, the experiments were conducted following the Knowledge Discovery in Database process model. The Knowledge Discovery in Database process model starts from selection of the datasets. The dataset used in this study has been taken from Massachusetts Institute of Technology Lincoln laboratory. After taking the data, it has been preprocessed. The major preprocessing activities include fill in missed values, remove outliers; resolve inconsistencies, integration of data that contains both labeled and unlabeled datasets, dimensionality reduction, size reduction and data transformation activity like discretization tasks were done for this study. A total of 21,533 intrusion records are used for training the models. For validating the performance of the selected model a separate 3,397 records are used as a testing set. For building a predictive model for intrusion detection J48 decision tree and the Naïve Bayes algorithms have been tasted as a classification approach for both with and without feature selection approaches. The model that was created using 10-fold cross validation using the J48 decision tree algorithm with the default parameter values showed the best classification accuracy. The model has a prediction accuracy of 96.11% on the training datasets and 93.2% on the test dataset to classify the new instances as normal, DOS, U2R, R2L and probe classes. The findings of this study have shown that the data mining methods generates interesting rules that are crucial for intrusion detection and prevention in the networking industry. Future research directions are forwarded to come up an applicable system in the area of the study.
Description: A Thesis Submitted to the School of Graduate Studies of Addis Ababa University in Partial Fulfillment of the Requirements for the Degree of Master of Science in Information Science
URI: http://hdl.handle.net/123456789/4144
Appears in:Thesis - Information Science

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