A Classification System for Visualized Malware Based on Multiple Autoencoder Models

Lee, Jongkwan; Lee, Jongdeog

doi:10.1109/access.2021.3122083

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Only fine-tuned layers, including FC2, FC1, and Block5, are used for less computational cost and faster classification, which fulfill the demand of most practical applications [18]. Lee [19] used multiple autoencoder models for identifying malware images. Some researchers focus on the study of deep learning models themselves.…”

Section: Related Workmentioning

confidence: 99%

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

Lu,

Ren,

Zhang

et al. 2023

Electronics

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Malware is a significant threat to the field of cyber security. There is a wide variety of malware, which can be programmed to threaten computer security by exploiting various networks, operating systems, software and physical security vulnerabilities. So, detecting malware has become a significant part of maintaining network security. In this paper, data enhancement techniques are used in the data preprocessing stage, then a novel detection mode—CPL-Net employing malware texture image—is proposed. The model consists of a feature extraction component, a feature fusion component and a classification component, the core of which is based on the parallel fusion of spatio-temporal features by Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). Through experiments, it has been proven that CPL-Net can achieve an accuracy of 98.7% and an F1 score of 98.6% for malware. The model uses a novel feature fusion approach and achieves a comprehensive and precise malware detection.

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Section: Related Workmentioning

confidence: 99%

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

Lu,

Ren,

Zhang

et al. 2023

Electronics

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“…Lee et al [1] illustrates the effectiveness of autoencoder by applying multiple AEs. Each AE model classifies only one type of malware and is trained using only samples from the corresponding family.…”

Section: Related Workmentioning

confidence: 99%

“…Malware is a powerful tool for an attacker to intrude, sabotage, and control a target indirectly as a remote administration tool through the Internet. The abuse of various malware causes a significant impact on cyber-security and threats to individuals, society, and countries [1], [2]. Authors of malware mix different evading techniques such as user interaction, environment awareness, obfuscation, code compression, and code encryption to change existing malicious code's appearance to bypass the Anti-virus System and Intrusion Detection System (IDS).…”

Section: Introductionmentioning

confidence: 99%

An Attention Mechanism for Combination of CNN and VAE for Image-Based Malware Classification

2022

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Currently, malware is increasing in both number and complexity dramatically. Several techniques and methodologies have been proposed to detect and neutralize malicious software. However, traditional methods based on the signatures or behaviors of malware often require considerable computational time and resources for feature engineering. Recent studies have applied machine learning to the problems of identifying and classifying malware families. Combining many state-of-the-art techniques has become popular but choosing the appropriate combination with high efficiency is still a problem. The classification performance has been significantly improved using complex neural network architectures. However, the more complex the network, the more resources it requires. This paper proposes a novel lightweight architecture by combining small Convolutional Neural Networks and advanced Variational Autoencoder, enhanced by channel and spatial attention mechanisms. We achieve overperformance and sufficient time through various experiments compared to other cutting-edge techniques using unbalanced and balanced Malimg datasets.

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ACMFNN: A Novel Design of an Augmented Convolutional Model for Intelligent Cross-Domain Malware Localization via Forensic Neural Networks

Pateriya

Tomar

2023

IEEE Access

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A Classification System for Visualized Malware Based on Multiple Autoencoder Models

Cited by 5 publications

References 23 publications

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

An Attention Mechanism for Combination of CNN and VAE for Image-Based Malware Classification

ACMFNN: A Novel Design of an Augmented Convolutional Model for Intelligent Cross-Domain Malware Localization via Forensic Neural Networks

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