Effective Obfuscated Malware Detection Leveraging Cutting-edge Machine and Deep Learning Approaches

Detecting obfuscated malware concentrates on specifying any malicious software (malware) that has been intentionally disguised or concealed to avoid conventional detection systems.Typically, obfuscation methods like runtime obfuscation, packing, and code encryption have been utilized by malware authors to change the appearance of malware while preserving its malicious functionality.As these methods have evolved, conventional detection systems have become less effective, requiring further sophisticated solutions like machine and deep learning approaches.Machine and deep learning approaches work on obfuscated malware detection via auto-learning features and patterns from massive datasets.Additionally, these approaches can specify formerly unseen malware by analyzing various features.In this paper, the Independent Component Analysis (ICA) is first utilized for isolating relevant features from obfuscated data to be prepared for binary classification.For further data analysis, essential feature selection, and a deeper comprehension of the relationships inside the dataset, the Pearson correlation coefficient is applied to the dataset to be prepared for multiclass classification.This dual scheme improves the feature extraction process depending on the classification type, enhancing the system's versatility and performance.Then, a proposed One-Dimensional Convolutional Neural Network (1D-CNN) is leveraged for extracting efficient features from memory traces, and providing an accurate system for classifying obfuscated memory malware.The combination of ICA or Pearson correlation with 1D-CNN in a unified system offers a scalable and inclusive settlement for binary or multiclass classification and contributes to the progress of malware classification systems.Besides the proposed 1D-CNN approach, two machine learning approaches are trained and assessed on the CIC-MalMem-2022 dataset, and the attained results depicted that the performance of the proposed 1D-CNN approach was superior, with accuracies of 99% and 88% for binary and multiclass classification, respectively.