Abstract

The major problem computer network users face concerning data – whether in storage, in transit, or being processed - is unauthorized access. This unauthorized access typically leads to the loss of confidentiality, integrity, and availability of data. Consequently, it is essential to implement an accurate Intrusion Detection System (IDS) for every information system. Many researchers have proposed machine learning and deep learning models, such as autoencoders, to enhance existing IDS. However, the accuracy of these models remains a significant research challenge. This paper proposes a Correlation-Based Feature Selection and Autoencoder (CFS-AE) to enhance detection accuracy and reduce the false alarms associated with the current anomaly-based IDS. The first step involves feature selection for the NSL-KDD and CIC-IDS2017 datasets which are used to train and test our model. Subsequently, an autoencoder is employed as a classifier to categorize data traffic into attack and normal categories. The results from our experimental study revealed an accuracy of 94.32% and 97.71% for the NSL-KDD and CIC-IDS2017 datasets, respectively. These results demonstrate improved performance over existing IDS systems.