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Automated Machine Learning for Malware Detection with Deep Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automated Machine Learning for Malware Detection with Deep Learning./
Author:	Brown, Austin.
Description:	1 online resource (72 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-03.
Contained By:	Masters Abstracts International84-03.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29258983click for full text (PQDT)
ISBN:	9798841746997

Automated Machine Learning for Malware Detection with Deep Learning.
Brown, Austin.

Automated Machine Learning for Malware Detection with Deep Learning. - 1 online resource (72 pages)

Source: Masters Abstracts International, Volume: 84-03.

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

Includes bibliographical references

Deep learning (DL) has proven to be very effective in detecting sophisticated malware that is constantly evolving. Even though deep learning has alleviated the feature engineering problem, finding the most optimal DL model, in terms of neural architecture search (NAS) and the model's optimal set of hyper-parameters, remains a challenge that requires domain expertise. In addition, many of the proposed state-of-the-art models are very complex and may not be the best fit for different datasets. A promising approach, known as Automated Machine Learning (AutoML), can reduce the domain expertise required to implement a custom DL model. AutoML reduces the amount of human trial-and-error involved in designing DL models, and in more recent implementations can find new model architectures with relatively low computational overhead.Research on the feasibility of using AutoML for malware detection is very limited.As such, first, this thesis provides a comprehensive analysis and insights on using AutoML for static malware detection. Our analysis is performed on two widely used malware datasets: SOREL-20M to demonstrate efficacy on large datasets; and EMBER-2018, a smaller dataset specifically curated to hinder the performance of machine learning models. In addition, we show the effects of tuning the NAS process parameters on finding a more optimal malware detection model on these static analysis datasets.We also show that AutoML is performant in online detection scenarios using Convolutional Neural Networks (CNNs) to detect malware execution. We compare an AutoML technique to six existing state-of-the-art CNNs using a newly generated online malware dataset with and without other applications running in the background during malware execution. We show that the AutoML technique is more performant than the state-of-the-art CNNs with little overhead in finding the architecture.Our experimental results show that the performance of AutoML based malware detection models are on par or better than state-of-the-art models or hand-designed models designed presented in other works.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798841746997Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Automated machine learningIndex Terms--Genre/Form:

542853
Electronic books.

Automated Machine Learning for Malware Detection with Deep Learning.
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500 $a Source: Masters Abstracts International, Volume: 84-03.
500 $a Advisor: Gupta, Maanak.
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504 $a Includes bibliographical references
520 $a Deep learning (DL) has proven to be very effective in detecting sophisticated malware that is constantly evolving. Even though deep learning has alleviated the feature engineering problem, finding the most optimal DL model, in terms of neural architecture search (NAS) and the model's optimal set of hyper-parameters, remains a challenge that requires domain expertise. In addition, many of the proposed state-of-the-art models are very complex and may not be the best fit for different datasets. A promising approach, known as Automated Machine Learning (AutoML), can reduce the domain expertise required to implement a custom DL model. AutoML reduces the amount of human trial-and-error involved in designing DL models, and in more recent implementations can find new model architectures with relatively low computational overhead.Research on the feasibility of using AutoML for malware detection is very limited.As such, first, this thesis provides a comprehensive analysis and insights on using AutoML for static malware detection. Our analysis is performed on two widely used malware datasets: SOREL-20M to demonstrate efficacy on large datasets; and EMBER-2018, a smaller dataset specifically curated to hinder the performance of machine learning models. In addition, we show the effects of tuning the NAS process parameters on finding a more optimal malware detection model on these static analysis datasets.We also show that AutoML is performant in online detection scenarios using Convolutional Neural Networks (CNNs) to detect malware execution. We compare an AutoML technique to six existing state-of-the-art CNNs using a newly generated online malware dataset with and without other applications running in the background during malware execution. We show that the AutoML technique is more performant than the state-of-the-art CNNs with little overhead in finding the architecture.Our experimental results show that the performance of AutoML based malware detection models are on par or better than state-of-the-art models or hand-designed models designed presented in other works.
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773 0 $t Masters Abstracts International $g 84-03.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29258983 $z click for full text (PQDT)