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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| Vydáno v: | Applied soft computing Ročník 167; s. 112460 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.12.2024
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| ISSN: | 1568-4946 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 112460 |
| Author | Nathan, Uma Devi Gurumuni T, Daniya Maram, Balajee Das, Smritilekha Vadivu, P. Balashanmuga Gopisetty, Guru Kesava Dasu |
| Author_xml | – sequence: 1 givenname: Uma Devi Gurumuni surname: Nathan fullname: Nathan, Uma Devi Gurumuni organization: Computer Science and Engineering, University of Engineering and Management, Jaipur, Rajasthan, India – sequence: 2 givenname: P. Balashanmuga surname: Vadivu fullname: Vadivu, P. Balashanmuga organization: Department of ECE, Mahendra Engineering College, Namakkal, Tamil Nadu, India – sequence: 3 givenname: Balajee surname: Maram fullname: Maram, Balajee organization: School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, 506371 – sequence: 4 givenname: Guru Kesava Dasu surname: Gopisetty fullname: Gopisetty, Guru Kesava Dasu organization: Department of Information Technology, KKR & KSR Institute of Technology and Sciences, Guntur, Andhra Pradesh, India – sequence: 5 givenname: Smritilekha surname: Das fullname: Das, Smritilekha organization: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India – sequence: 6 givenname: Daniya surname: T fullname: T, Daniya email: daniya.t@gmrit.edu.in organization: Department of CSE (AI&ML), GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India |
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| Title | M-Net based stacked autoencoder for ransomware detection using blockchain data |
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