Modified stacking ensemble machine learning method for network intrusion detection

Machine learning (ML) methods became highly popular since the amount of the data produced on the Internet started increasing exponentially, although it was a known topic in academic studies before that era. It started becoming highly hard to extract rules from this huge amount of data or find patterns. ML started playing an important role in the area of finding patterns and extracting rules. With increasing number of people accessing the Internet and having smart mobile phones, the possible threats in the Internet became important topic in network security studies. The conventional way of detection network intrusion is to use signature based rules (pre-defined rules), where this type of rules can't detect unknown signatures even though the type of the attack is the same. In last decade, ML started to be used in network security studies more often. This study is based on using stacking ensemble machine learning method for the purpose of detecting network intrusion. In this study, we propose two different methods to improve the performance of ML methods to detect network intrusion. Firstly, we used different combination algorithms and different base model selection methods to improve the performance of stacking ensemble method which provided significant results when it is compared to conventional machine learning methods. Secondly, we used genetic algorithm to for the base model selection phase of the first study.