MDC-Net:Intelligent Malware Detection and Classification using Extreme Learning Machine

Reddy, V. S. K.; Nagaraju, I.; Gayatri, Mantri; Ravindranath, Roopa; Dileep, P; Revathy, P.

doi:10.1109/icais56108.2023.10073874

Cited by 3 publications

(1 citation statement)

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“…Paardekooper used a genetic algorithm (GA) to optimize the CNN topology and hyperparameters for image-based malware classification [24]. Reddy created a highly scalable framework known as MDC-Net to detect classfy and organize malware [25].…”

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%