國立虎尾科技大學 |

Malware Detection and Classification Using Different Neural Networks.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Malware Detection and Classification Using Different Neural Networks./
作者:	Richie, Imafidon Abiola.
面頁冊數:	1 online resource (74 pages)
附註:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
標題:	Computer engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798382607238

Malware Detection and Classification Using Different Neural Networks.
Richie, Imafidon Abiola.

Malware Detection and Classification Using Different Neural Networks. - 1 online resource (74 pages)

Source: Masters Abstracts International, Volume: 85-11.

Thesis (M.S.)--Western Illinois University, 2024.

Includes bibliographical references

This study, portrayed in this thesis, aims to investigate the effectiveness of various neural network (NN) architectures for malware detection and classification. The study's scholar utilized a dataset sourced from Kaggle, comprising diverse malware types, the research employs Convolutional Neural Networks (CNN), Recurrent NN (RNN), and Artificial NN (ANN). Results demonstrate CNN's superior performance in accuracy and F1-score, attributed to its ability to extract hierarchical features from image data. RNNs and ANNs exhibit comparatively lower efficiency due to limitations in spatial awareness and sequential modeling. The study underscores the significance of NN architecture in malware identification, highlighting CNN's robustness in capturing subtle malware patterns. Future research avenues include refining NN structures for image-based malware detection and optimizing training data quality and hyperparameter tuning for enhanced model performance.

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

Mode of access: World Wide Web

ISBN: 9798382607238Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

554714
Electronic books.

Malware Detection and Classification Using Different Neural Networks.
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520 $a This study, portrayed in this thesis, aims to investigate the effectiveness of various neural network (NN) architectures for malware detection and classification. The study's scholar utilized a dataset sourced from Kaggle, comprising diverse malware types, the research employs Convolutional Neural Networks (CNN), Recurrent NN (RNN), and Artificial NN (ANN). Results demonstrate CNN's superior performance in accuracy and F1-score, attributed to its ability to extract hierarchical features from image data. RNNs and ANNs exhibit comparatively lower efficiency due to limitations in spatial awareness and sequential modeling. The study underscores the significance of NN architecture in malware identification, highlighting CNN's robustness in capturing subtle malware patterns. Future research avenues include refining NN structures for image-based malware detection and optimizing training data quality and hyperparameter tuning for enhanced model performance.
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653 $a Malware detection
653 $a Convolutional Neural Networks
653 $a Hyperparameter tuning
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