國立虎尾科技大學 |

Automated Machine Learning for Malware Detection with Deep Learning.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Automated Machine Learning for Malware Detection with Deep Learning./
作者:	Brown, Austin.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	72 p.
附註:	Source: Masters Abstracts International, Volume: 84-03.
Contained By:	Masters Abstracts International84-03.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29258983
ISBN:	9798841746997

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

Automated Machine Learning for Malware Detection with Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 72 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798841746997Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Automated machine learning

Automated Machine Learning for Malware Detection with Deep Learning.
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500 $a Source: Masters Abstracts International, Volume: 84-03.
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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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