The internet’s extensive expansion and development over the past few decades has raised concerns about the constantly evolving and growing number of cyberattacks. To safeguard data, an efficient intrusion detection system is therefore necessary. This research uses the ‘CIC-IDS2018’ dataset for implementing three algorithms: Autoencoder, Convolution Neural Network (CNN), and CNN+ Long Short-Term Memory (LSTM) to monitor intrusion during data transmission. Performance measures are highlighted to validate the output of the classifiers. A comparative study is conducted between the model based on their performances. A significant takeaway from this study is that CNN + LSTM outperformed in intrusion detection. In the future, this study will emphasize not only monitoring but also controlling intrusion in networks.