Performance Analysis of Machine Learning Classifiers for Detecting PE Malware

In this modern era of technology, securing and protecting one’s data has been a major concern and needs to be focused on. Malware is a program that is designed to cause harm and malware analysis is one of the paramount focused points under the sight of cyber forensic professionals and network administrations. The degree of the harm brought about by malignant programming varies to a great extent. If this happens at home to a random person then that may lead to some loss of irrelevant or unimportant information but for a corporate network, it can lead to loss of valuable business data. The existing research does focus on some few machine learning algorithms to detect malware and very few of them worked with Portable Executables (PE) files. In this paper, we mainly focused on top classification algorithms and compare their accuracy to find out which one is giving the best result according to the dataset and also compare among these algorithms. Top machine learning classification algorithms were used alongside neural networks such as Artificial Neural Network, XGBoost, Support Vector Machine, Extra Tree Classifier, etc. The experimental result shows that XGBoost achieved the highest accuracy of 98.62 percent when compared with other approaches. Thus, to provide a better solution for this kind of anomalies, we have been interested in researching malware detection and want to contribute to building strong and protective cybersecurity.

@article{Azmee2020,
  title     = {Performance Analysis of Machine Learning Classifiers for Detecting PE Malware},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {1},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {ABM.Adnan Azmee and Pranto Protim Choudhury and Md. Aosaful Alam and Orko Dutta and Muhammad Iqbal Hossai},
  doi       = {10.14569/IJACSA.2020.0110163},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110163}
}