A few-shot malware classification approach for unknown family recognition using malware feature visualization

Conti, Mauro; Khandhar, Shubham; Vinod, P.

doi:10.1016/j.cose.2022.102887

Cited by 15 publications

(1 citation statement)

References 33 publications

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“…This section covers visualization-related studies, including malware identification using statistical similarity measures, machine learning, and deep learning. Traditional MD techniques primarily analyse harmful code properties ( Conti, Khandhar & Vinod, 2022 ). These capabilities also utilize advanced machine learning-based MD techniques to identify new forms of destructive code.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial intelligence-driven malware detection framework for internet of things environment

Alsubai

Dutta²,

Alnajim

et al. 2023

PeerJ Computer Science

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The Internet of Things (IoT) environment demands a malware detection (MD) framework for protecting sensitive data from unauthorized access. The study intends to develop an image-based MD framework. The authors apply image conversion and enhancement techniques to convert malware binaries into RGB images. You only look once (Yolo V7) is employed for extracting the key features from the malware images. Harris Hawks optimization is used to optimize the DenseNet161 model to classify images into malware and benign. IoT malware and Virusshare datasets are utilized to evaluate the proposed framework’s performance. The outcome reveals that the proposed framework outperforms the current MD framework. The framework generates the outcome at an accuracy and F1-score of 98.65 and 98.5 and 97.3 and 96.63 for IoT malware and Virusshare datasets, respectively. In addition, it achieves an area under the receiver operating characteristics and the precision-recall curve of 0.98 and 0.85 and 0.97 and 0.84 for IoT malware and Virusshare datasets, accordingly. The study’s outcome reveals that the proposed framework can be deployed in the IoT environment to protect the resources.

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Section: Literature Reviewmentioning

confidence: 99%