A CNN and Image-Based Approach for Malware Analysis

Migdady, Aya; Smadi, Lara; Yaseen, Qussai

doi:10.1109/etcea57049.2022.10009748

Cited by 2 publications

(1 citation statement)

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“…Drawing inspiration from this, several studies adopt an approach where malware is transformed into data representations with spatial structure relationships, leveraging CNN for malware recognition [57], [58]. Migdady et al [36] and Král et al [46] proposed converting software binary files into hexadecimal sequences and employing 1-dimensional CNNs to construct malware recognition models. Gibert et al [41] segmented the hexadecimal representation of malware into fixed-size blocks, calculated the entropy value of each block, used Haar Wavelet Transform (HWT) to obtain approximation and detail coefficients for the entropy sequence, and used these as inputs for CNN to build the recognition model.…”

Section: B Malware Identification Based On Convolutional Neuralmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Approaches for Detecting Malicious Software

Yuanming,

Latih

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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With the continuous development of technology, the types of malware and their variants continue to increase, which has become an enormous challenge to network security. These malware use a variety of technical means to deceive or evade traditional detection methods, making traditional signature-based rule-based malware identification methods no longer applicable. Many machine algorithms have attracted widespread academic attention as powerful malware detection and classification methods in recent years. After an in-depth study of rich literature and a comprehensive survey of the latest scientific research results, feature extraction is used as the basis for classification. By extracting meaningful features from malware samples, such as behavioral patterns, code structures, and file attributes, researchers can discern unique characteristics that distinguish malicious software from benign ones. This process is the foundation for developing effective detection models and understanding the underlying mechanisms of malware behavior. We divide feature engineering and learning-based methods into two categories for investigation. Feature engineering involves selecting and extracting relevant features from raw data, while learning-based methods leverage machine learning algorithms to analyze and classify malware based on these features. Supervised, unsupervised, and deep learning techniques have shown promise in accurately detecting and classifying malware, even in the face of evolving threats. On this basis, we further look into the current problems and challenges malware identification research faces.

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