Performance analysis of Machine learning algorithms for predicting malware
2026-08-03 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors studied ways to detect malicious software, called malware, using machine learning instead of traditional methods that rely on known signatures. They tested several advanced algorithms and found that XGBoost worked best, correctly identifying malware about 98.6% of the time. To make the solution practical, they built a system that can check files in real-time to see if they are harmful or safe. Their work shows that using smart algorithms can help improve malware detection effectively.
malware detectionmachine learningXGBoostArtificial Neural NetworksSupport Vector MachinesExtra Trees ClassifierFlask frameworkcybersecurityclassification algorithmsreal-time detection
Authors
ABM. Adnan Azmee, Pranto Protim Choudhury, Md. Aosaful Alam, Orko Dutta, Muhammad Iqbal Hossain
Abstract
Malware poses a persistent and evolving threat to modern computing systems, making accurate and timely detection a critical cybersecurity challenge. Traditional signature-based antivirus solutions often fail to identify newly emerging malware, leaving systems vulnerable until updated signatures become available. To address this limitation, this study proposes a machine learning-based malware detection framework capable of distinguishing malicious software from benign applications with high accuracy. Several state-of-the-art classification algorithms, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), XGBoost, and Extra Trees Classifier, were evaluated and compared using a benchmark malware dataset. Experimental results demonstrate that XGBoost achieved the best performance, attaining an accuracy of 98.62%, outperforming the other evaluated models. To demonstrate the practical applicability of the proposed approach, a real-time client-server malware detection system was also developed using the Flask framework, enabling efficient classification of executable files as malicious or benign. The findings highlight the effectiveness of advanced machine learning techniques for enhancing malware detection and contribute toward the development of intelligent and scalable cybersecurity solutions.