Cyberattacks are becoming more sophisticated as attackers continuously use diverse strategies and tactics to attack target systems. To avert this impending threat, various possible solutions have been implemented and a variety of security systems keeps emerging. One of these solutions is intrusion detection systems (IDS). The challenge in developing a model that is efficient to detect intrusion is directly dependent on the features selected during the training process and also to detect known and unknown attacks. In this thesis a machine learning model is proposed based on a Hidden Markov Model. The model was trained and tested with data from the NSL KDD dataset, which was selected based on four categories of attacks. The relevant features for training were selected from the dataset based on the scores evaluated from a Laplacian model. New features were extracted from the selected features based on variance from a Principal Component Analysis to decrease the dimensionality of the dataset. K-Means Algorithm was utilized in mapping the extracted data into a new feature space for training. The proposed model produced an accuracy of 83.85% which outperformed the accuracies of the existing models built with the Support Vector Machine (SVM), Random Forest (RF), Deep Belief Network (DBN), and the Convolutional Neural Networks (CNN) which had 71.30%, 74.18%, 71.91% and 80.13% respectively. Keywords: Cyberattacks, intrusion, security, detection, Hidden Markov Model