2010/12/01 by Ivan Firdausi, Charles Lim, Alva Erwin +1 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Digital and Cyber Forensics #Network Security and Intrusion Detection #Malware #C4.5 algorithm #Computer science #Decision tree #Machine learning #Artificial intelligence #Support vector machine #Naive Bayes classifier #Sandbox (software development) #Artificial neural network #Perceptron #Malware analysis #Multilayer perceptron #Data mining #Computer security #Operating system
paper · doi:10.1109/act.2010.33
openalex publication_date 2010/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated (sandbox) environment will be automatically analyzed and will generate behavior reports. These reports will be preprocessed into sparse vector models for further machine learning (classification). The classifiers used in this research are k-Nearest Neighbors (kNN), Naïve Bayes, J48 Decision Tree, Support Vector Machine (SVM), and Multilayer Perceptron Neural Network (MlP). Based on the analysis of the tests and experimental results of all the 5 classifiers, the overall best performance was achieved by J48 decision tree with a recall of 95.9%, a false positive rate of 2.4%, a precision of 97.3%, and an accuracy of 96.8%. In summary, it can be concluded that a proof-of-concept based on automatic behavior-based malware analysis and the use of machine learning techniques could detect malware quite effectively and efficiently.