An improved binary manta ray foraging optimization algorithm based feature selection and random forest classifier for network intrusion detection

The growth within the Internet and communications areas have led to a massive surge in the dimension of network and data. Consequently, several new threats are being created and have posed difficulties for security networks to correctly discover intrusions. Intrusion Detection System (IDs) is one amongst the foremost essential events for security arrangements in network environments, and it is commonly applied to spot, track, and detect malevolent threats. Detecting intruders using metaheuristics and machine learning methodologies in recent trend offers improved discovery rate. Therefore, this paper presented an intrusion detection model using an improved Binary Manta Ray Foraging (BMRF) Optimization Algorithm based on adaptive S-shape function and Random Forest (RF) classifier. The BMFR is envisioned to identify the most relevant features and remove redundant and irrelevant ones from the intrusion detection datasets. Furthermore, the RF is used for feature evaluation and to build the intrusion detection model. The proposed method was validated and compared with other methods using two IDs benchmark datasets, which include NSL-KDD and CIC-IDS2017 datasets. The result indicates that the presented model selected 38 features with 99.6% precision, 94.3% recall, 96.9% f-measure, and 99.3% accuracy for the CIC-IDS2017 dataset. Moreover, for the NSL-KDD dataset, the presented model selected 22 features with 96.8%, 96.2%, 96.5%, and 98.8% for precision, recall, F-measure, and accuracy. In addition, a statistical significance test reveals a significance difference between the presented model and the compared methods in terms of F-measure.