Review on Malware Classification with a Hybrid Deep Learning

Abstract

This study introduces advanced methodology for classifying malware by leveraging hybrid deep learning algorithms. The research presents a pioneering framework that seamlessly integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models to deliver a robust malware classification approach. The primary objective is to effectively differentiate between normal behavioral patterns and malicious network data. The efficacy of this innovative approach is evaluated by comparing it with conventional machine learning techniques like Support Vector Machines (SVM). Through this comparative analysis, the investigation aims to uncover the unique strengths and potential limitations of the proposed method, with the intention of establishing it as a superior alternative to current malware classification methods. By harnessing the individual capabilities of CNN and LSTM models, the proposed framework achieves a higher level of accuracy in identifying and categorizing malware compared to existing approaches. While CNN models excel in feature extraction from raw data, LSTM models exhibit proficiency in understanding sequential patterns. By synergizing these models, the resulting framework demonstrates significantly improved performance in classifying malware. The empirical assessment strongly suggests that the newly proposed framework is poised to outperform established techniques. These research findings hold great promise in advancing the development of more efficient systems for detecting and preventing malware.

Proactive ransomware prevention in pervasive IoMT via hybrid machine learning

“…Feature selection is vital for machine learning in IoMT, streamlining ransomware detection by isolating key data features. Techniques include statistical-based filter methods [18] for independent feature evaluation, performance-driven wrapper methods [19] for intensive computation, embedded methods [20] that integrate selection within training, enhancing relevance learning, and hybrid methods [21] [23] that merge techniques for optimized accuracy and model efficiency. These methods range from statistical evaluation to leveraging model performance and integrating selection into the learning process, each offering distinct advantages in identifying significant features within vast datasets.…”