M-Net based stacked autoencoder for ransomware detection using blockchain data

Ransomware is a kind of malevolent program software that encrypts the items on the hard disc and prevents the clients from accessing them until they are paid a ransom. Associations like monetary establishments and medical care areas (i.e., smart medical care) are mostly targeted by ransomware attack...

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Bibliographic Details
Published in:Applied soft computing 2024-12, Vol.167, p.112460, Article 112460
Main Authors: Nathan, Uma Devi Gurumuni, Vadivu, P. Balashanmuga, Maram, Balajee, Gopisetty, Guru Kesava Dasu, Das, Smritilekha, T, Daniya
Format: Article
Language:English
Subjects:
Deep Q-network
Deep Stacked Autoencoder
Lorentzian Similarity
MobileNet
Yeo-Johnson Transformation
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Summary:Ransomware is a kind of malevolent program software that encrypts the items on the hard disc and prevents the clients from accessing them until they are paid a ransom. Associations like monetary establishments and medical care areas (i.e., smart medical care) are mostly targeted by ransomware attacks. Ransomware assaults are crucial holes still in blockchain technology and prevent effective data communication in networks. This study aims to introduce an efficient system, named M-Net-based Stacked Autoencoder (M-Net_SA) for ransomware detection using blockchain data. Initially, the input data is taken from a dataset and then sent to the feature extraction process, which utilizes sequence-based statistical features. After that, data transformation is completed using the Yeo-Johnson transformation to transform the data into a usable format. After that, feature fusion is executed using a Deep Q-network (DQN) with Lorentzian similarity to enhance the representativeness of the target features. Finally, ransomware detection is accomplished by the proposed M-Net_SA, which is the integration of MobileNet and Deep Stacked Autoencoder (DSAE). The experimental validation of the proposed M-Net_SA is compared with other conventional techniques and the proposed model attained maximum accuracy, sensitivity, and specificity of 0.959, 0.967, and 0.957 respectively. •Data transformation is processed utilizing the Yeo-Johnson transformation.•Feature fusion is executed using a Deep Q-network.•Ransomware detection is effectuated using the M-Net-based Stacked Autoencoder.
ISSN:1568-4946
DOI:10.1016/j.asoc.2024.112460