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Analyzing and Explaining Machine Learning Based Online Malware Detection in Cloud.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Analyzing and Explaining Machine Learning Based Online Malware Detection in Cloud./
Author:	Kimmell, Jeffrey Calen.
Description:	1 online resource (82 pages)
Notes:	Source: Masters Abstracts International, Volume: 83-11.
Contained By:	Masters Abstracts International83-11.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798438791386

Analyzing and Explaining Machine Learning Based Online Malware Detection in Cloud.
Kimmell, Jeffrey Calen.

Analyzing and Explaining Machine Learning Based Online Malware Detection in Cloud. - 1 online resource (82 pages)

Source: Masters Abstracts International, Volume: 83-11.

Thesis (M.S.)--Tennessee Technological University, 2022.

Includes bibliographical references

The introduction of cloud environments for work flows and other uses has led to a dramatic shift for private firms and government entities alike. These cloud environments are constructed and offered by cloud service providers (CSP). Key CSPs such as Google, Microsoft, and Amazon offer products and services that provide firms with scalable infrastructure options without firms having to invest in hardware; this is known as infrastructure as a service (IaaS). This infrastructure allows for a central point for collaborators to work and access resources. This nature of IaaS makes these cloud environments very attractive to attackers. Securing these systems has become a top priority for these CSPs and researchers as compromised cloud environments can have much more drastic impacts as opposed to a single compromised machine. Malware is a common way these attackers gain access to these systems. That is why it is of the utmost importance to create sophisticated malware detection methods that can sufficiently protect these systems. This work provides a cloud specific online malware detection method and compares the effectiveness of Convolutional Neural Networks (CNN), Support Vector Classifier (SCV), Random Forest Classifier (RFC), K-Nearest Neighbor (KNN), Gradient Boosted Classifier (GBC), Gaussian Naive Bayes (GNB), and two types of Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Bidirectional (BIDI).We also deploy a feed-forward neural network and then use Shapley Additive Explanations (SHAP) to explain the decisions that the model produces. Our models are trained validated and tested on a dataset that contains 40,680 data points that are comprised of process sytstem features that represents the behavior of the virtual machine (VM). This data was collected by running malware openly in a cloud environment to ensure the behavior of the malware and VMs is as realistic as possible.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798438791386Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

Analyzing and Explaining Machine Learning Based Online Malware Detection in Cloud.
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