Developing a Automation Malware Detection Tool using Machine Learning Algorithm

A instructive cybersecurity concern is the increasing sophistication of malware, with Portable Executable (PE) files acting as an ordinary channel for harming activity. This paper presents a machine learning-based method for PE malware detection in response to the everexpanding threat landscape. Using a large benchmark dataset, we carefully evaluate the properties of PE files using the Hyperparameter Tuned KNN (HTKNN) machine learning method to improve malware detection precision. Grid Search Cross-Validation (CV) is used to optimize performance and fine-tune the model parameters. Our work offers a reliable and efficient method for identifying and reducing malware dangers encoded in PE files, which supports the continuous efforts to strengthen cybersecurity defenses. The outcomes of our assessment emphasize. The quality of the feature representation, the quantity and variety of the dataset, and the distance metric selected all affect how successful the K-Nearest Neighbors (KNN) algorithm is at detecting malware. KNN is a straightforward yet effective technique that uses the majority class among its k nearest neighbors in feature space to classify data items.KNN's simplicity and ease of implementation are two of its benefits. Because it is non-parametric, it does not assume anything about the data's underlying distribution. Because of its adaptability, KNN can record intricate decision boundaries, which makes it useful for identifying both known and unknown viruses.