Towards Improving the Intrusion Detection through ELM (Extreme Learning Machine)

An IDS (intrusion detection system) provides a foremost front line mechanism to guard networks, systems, data, and information. That's why intrusion detection has grown as an active study area and provides significant contribution to cyber-security techniques. Multiple techniques have been in use but major concern in their implementation is variation in their detection performance. The performance of IDS lies in the accurate detection of attacks, and this accuracy can be raised by improving the recognition rate and significant reduction in the false alarms rate. To overcome this problem many researchers have used different machine learning techniques. These techniques have limitations and do not efficiently perform on huge and complex data about systems and networks. This work focused on ELM (Extreme Learning Machine) technique due to its good capabilities in classification problems and dealing with huge data. The ELM has different activation functions, but the problem is to find out which function is more suitable and performs well in IDS. This work investigates this problem. Here, Well-known activation functions like: sine, sigmoid and radial basis are explored, investigated and applied to measure their performance on the GA (Genetic Algorithm) features subset and with full features set. The NSL-KDD dataset is used as a benchmark. The empirical results are analyzed, addressed and compared among different activation functions of the ELM. The results show that the radial basis and sine functions perform better on GA feature set than the full feature set while the performance of the sigmoid function is almost equal on both features sets. So, the proposal of GA based feature selection reduced 21 features out of 41 that brought up to 98% accuracy and enhanced overall efficiency of extreme learning machine in intrusion detection.