One of the biggest cybersecurity challenges in recent years has been the risk that insiders pose. Internet consumers are susceptible to exploitation due to the exponential growth of network usage. Malware attacks are a major concern in the digital world. This occurrence indicates that threats necessitate specialized detection techniques and equipment, including the ability to facilitate accurate and rapid detection of an insider threat. In this research, we propose a machine learning algorithm using a neural network to enhance malware detection accuracy in response to this threat. A feature extraction, anomaly detection, and classification workflow is also proposed. We use the CERT4.2 dataset and preprocess the data by encoding text strings and differentiating threat and non-threat records. Our developed machine learning model incorporates multiple dense layers, ReLU activation functions, and dropout layers for regularization. The model attempts to detect and classify internal threats in the dataset with precision. We employed Random Forest, Naive Bayes, KNN, SVM, Decision Tree, Logical Regression, and the Gradient Boosting algorithm to compare our proposed model with other classification techniques. According to the results of the experiments, the proposed method functions properly and can detect malware more effectively and with 100% accuracy.