Source of Publication
International Journal of Interactive Mobile Technologies
© 2019, International Association of Online Engineering. Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanism that used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity and availability of the services. The speed of the IDS is very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The techniques J48, Random Forest, Random Tree, MLP, Naïve Bayes and Bayes Network classifiers have been chosen for this study. It has been proven that the Random forest classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type (DOS, R2L, U2R, and PROBE).
International Association of Online Engineering
DDoS, IDS, MLP, Naïve Bayes, Random Forest
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Obeidat, Ibrahim M.; Hamadneh, Nabhan; Alkasassbeh, Mouhammd; Almseidin, Mohammad; and AlZubi, Mazen Ibrahim, "Intensive pre-processing of KDD Cup 99 for network intrusion classification using machine learning techniques" (2019). All Works. 2061.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series