Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures. This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems. The objectives include Investigating the performance of various classifiers, such as Gaussian Naïve Bayes, K Nearest Neighbors (KNN), Stochastic Gradient Descent Classifier (SGDC), and Decision Tree, in detecting Windows malware. Evaluating the accuracy, efficiency, and suitability of each classifier for real-world malware detection scenarios. Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers. Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence. The study employs a structured methodology consisting of several phases: exploratory data analysis, data preprocessing, model training, and evaluation. Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements. Data preprocessing includes cleaning, feature encoding, dimensionality reduction, and optimization to prepare the data for training. Model training utilizes various supervised classifiers, and their performance is evaluated using metrics such as accuracy, precision, recall, and F1 score. The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection. Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware. Additionally, insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses. Overall, this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.