Windows PE Malware Detection Using Ensemble Learning
Autor: | Sanjay Misra, Robertas Damaševičius, Oluwanifise Ebunoluwa Odufuwa, Jonathan Oluranti, Nureni Ayofe Azeez |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Computer Networks and Communications
Computer science 02 engineering and technology Machine learning computer.software_genre Naive Bayes classifier malware detection 0202 electrical engineering electronic engineering information engineering Ransomware lcsh:T58.5-58.64 Artificial neural network lcsh:Information technology business.industry Communication Deep learning deep learning 020206 networking & telecommunications computer.file_format Ensemble learning Human-Computer Interaction ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS ComputingMethodologies_PATTERNRECOGNITION stacking Malware ensemble learning 020201 artificial intelligence & image processing Gradient boosting Artificial intelligence business computer Portable Executable |
Zdroj: | Informatics Volume 8 Issue 1 Informatics, Vol 8, Iss 10, p 10 (2021) |
ISSN: | 2227-9709 |
DOI: | 10.3390/informatics8010010 |
Popis: | In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naïve Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier. |
Databáze: | OpenAIRE |
Externí odkaz: |