Artificial Neural Network-based Intrusion Detection in Cloud Computing using CSE-CIC-IDS2018 Datasets

Securing digital information is vital for several applications. However, attackers are attacking digital information in numerous ways. Traditional methods still uncover several types of intrusions due to the rapid growth in internet technology. In addition, conventional methods cannot detect network attacks in real time. Therefore, the present study proposed the Artificial Neural Network (ANN) based system to detect real-time network intrusion. The proposed model uses 19 features selected using the decision tree method implemented in Python (Anaconda 3) environment. The performance of the ANN model was evaluated with accuracy, precision, recall, and f1-score, achieving results of 0.999, 0.999, 0.998, and 0.999, respectively. The results are promising and can be used for real-time datasets.