Cloud and internet-of-things secure integration along with security concerns

Ullah, Arif; Lansari, Imane; Şahin, Canan Batur; Dinle, Ozlem Batur; Aznaoui, Hanane

doi:10.11591/ijict.v12i1.pp62-71

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…The specific file was then transferred to Google Cloud for verification using the KRDMCDLT to determine whether it was a rootkit or benign [19]. When this verification was conducted on the optimal launch system in windows 2022 against 64 rootkits [20], every test attempting to differentiate the cloud was unsuccessful. The rootkit [21], which targeted a brand-new boot system, was included in the tests.…”

Section: Resultsmentioning

confidence: 99%

Kernel rootkit detection multi class on deep learning techniques

Srinivasan,

Thalavaipillai

2024

Bulletin EEI

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The harmful code application known as a rootkit is designed to be loaded and run directly from the operating system's (OSs') Kernel. Rootkits deployed in the Kernel, called Kernel-mode rootkits, can alter the OS. The intention behind these Kernel changes is to conceal the hack. Detecting a Kernel rootkit in a target machine is found to be quite challenging. Numerous techniques can be employed to modify the Kernel of a system. Kernel rootkits also create hidden access for attacks, enabling unauthorized entry to be gained by attackers on the machine. The ultimate consequence is that essential computer data can be modified, personal information can be gathered, and hackers can observe behavior. Synthetic neural networks support artificial intelligence, a branch of deep learning that models the human brain and operates on large datasets. This study proposed the Kernel rootkit detection multi-class deep learning techniques (KRDMCDLT). Deep learning algorithms are utilized to recognize the Kernel rootkit from a batch of data by selecting essential properties for learning tracking models. Thus, by identifying the OS malware, trojan assaults can be stopped before they can access infected data. This Kernel rootkit detection was tested in a Google Cloud Platform (GCP) computing system.

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Section: Resultsmentioning

confidence: 99%

Kernel rootkit detection multi class on deep learning techniques

Srinivasan,

Thalavaipillai

2024

Bulletin EEI

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“…In this section, we present the different modified sections that took place in SVM by different research in different years. Table 2 (see in Appendix) presents the different categories of the result like the year, function, improvement, and objective of many researchers' modification in SVM for their research purpose [69]- [79]. In this research, we collect all the information related to SVM.…”

Section: Resultsmentioning

confidence: 99%

Generalization of linear and non-linear support vector machine in multiple fields: a review

Khan,

Aznaoui

et al. 2023

Comput Sci Inf Technol

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Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.

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“…Once the program has been downloaded, the indicated protection measure will be implemented once it is present in the other node to which the file is being transferred. This makes the client node rootkit safe by ensuring that the incoming file cannot infect it [19].…”

Section: Methodsmentioning

confidence: 99%

Kernel rootkit prevention model using multiclass

Srinivasan,

Thalavaipillai

2024

IJRES

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Malicious individuals can access a computer network or application thanks to a series of programmes known as rootkit malware. These kernel rootkits use covert methods to conceal the kernel components, various control frameworks, and system activities, making identifying or prohibiting their presence in the target machine challenging. The bulk of rootkit detection and prevention techniques used today are particular to a system and dependent on recognized sources, making them ineffective for growing, evolving, concealed, or unnamed rootkits. This study proposes using the kernel rootkit prevention model using multiclass (KRPMM) system to identify hash values and detect/prevent such rootkits. The file downloaded by the client, who is availing of the service, is not permitted into the node used by the client in the cloud. But, it is redirected to the node wherein the file that has been downloaded and has entered the node anew is examined by a program which is specially coded to test the presence of rootkit in the file by some mechanisms and then comes to a conclusion of either the file being malicious or the file being clean and is free of rootkits. KRPMM tested only 64 rootkits.

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Cloud and internet-of-things secure integration along with security concerns

Cited by 7 publications

References 35 publications

Kernel rootkit detection multi class on deep learning techniques

Kernel rootkit detection multi class on deep learning techniques

Generalization of linear and non-linear support vector machine in multiple fields: a review

Kernel rootkit prevention model using multiclass

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