Hybridization of OFDM and Physical Layer Techniques for Information Security in Wireless System

Maimuna Yusuf Ma’aji and Muhammad Sirajo Aliyu

Keywords:Data Mining, Intrusion Detection, KDD CUP 99, Network security


Network security is the greatest challenges in our current generation. Network intrusion detection (NID) is one of the
fundamental techniques used to protect computer networks from threads. However, there is contemplation on the
possibility and sustainability of the traditional approaches employed especially with the current modern networks.
Recently, majority of the researchers employ the application of machine learning models such as logistic regression
(LR), random forest (RF), K-nearest neighbor (KNN) and Support Vector Machines (SVM) among others to address
the NID problem. In this research, the Network intrusion classification system has been analyzed based on KDD Cup
’99 data set. In the classifier implementation section, the three most widely used models were employed; K-nearest
neighbor, random forest and logistic regression as our classification algorithms. The attack detection accuracy and
training time was used to evaluate the models. The random forest provides the best detection accuracy of 99.5% and
74 second training time provide by logistic regression.