Publication

Abstract : The evolutions in information and communication technology (ICT) devices have led to the rise in cyber-attacks in network systems. Ensuring cybersecurity in these devices is one of the most critical and complex issues. Besides, with technological improvement, hackers have been introducing complex and malicious malware attacks into the network system, making intrusion detection a complicated task. However, various conventional IDS face severe challenges to discern and counteract such risks. Statistical research and an enhanced autoencoder classifier for the network intrusion detection system are introduced in this paper (NIDS). This work uses an optimised autoencoder feature extraction process to extract correlated features to simplify the classification process. The proposed method is estimated using the NSL-KDD, UNSW-NB15, and CIC-DDoS2019 datasets, and its performance is compared to that of current machine learning algorithms. The results indicate that the proposed AE-NIDS outperforms shallow machine learning classifiers regarding detection accuracy and false positive rate.