Malware Detection Using Decision Tree Algorithm Based on Memory Features Engineering

Malware is malicious software that can harm, manipulate, steal from victim's device system. Due to the diverse needs of using internet services, security threats are also increasingly difficult detect. now attackers starting develop malware change their own signature which referred as polymorphism. Therefore, improvements in traditional approach detecting presence needed be improved. One detection approaches, memory-based analysis technique has proven a powerful and effective analytical studying behavior. In this study, implementation Decision Tree-based classification algorithm was carried out analyze data set. Classifier model created for purpose classifying based on memory features engineering. The result shows Tree machine learning been well performed with accuracy 99.982 %, false positive rate equal 0.1% precision 99.977%